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“Macro-prudential Analysis of the BASEL III Accord on

the current capital structure of the Kotak Mahindra Bank”

Live Project Submitted to Indian Institute of Finance

towards fulfillment of the requirements of

MANAGEMENT OF BUSINESS FINANCE (MBF 2011-13)

By

SINDUJA SHUKLA

En- No.: 4111045045

Under the Supervision of

Corporate Guide: Name: Ms. Talat Nasreen

Designation: Manager

Company Name: Kotak Mahindra Bank Ltd.

NOIDA

INDIAN INSTITUTE OF FINANCEDelhi, Noida & G-Noida

April, 2013

Academic Guide

Dr. H.P Mathur,

Faculty of Management Studies, BHU

Varanasi.

2 Macro-prudential Analysis of the BASEL III Accord on the capital structure of KMBL

PREFACE

As a part of MBF Programme, a student has to pursue a project on a live issue faced by a company. By

applying the acquired knowledge which is gained from the two-year MBF program along with the

guidance of academic guide a student needs to study, analyze and support the company’s research on

that live issue. I had the privilege of undertaking a project on “Macro-prudential Analysis of the

BASEL III Accord on the current capital structure of the Kotak Mahindra Bank”

Project is categorized into five chapters which are given as under:

1. CHAPTER ONE consists of An Overview of BASEL Framework in context of both the global and

India Scenario, Problem Statement, Rationale of the study, Research methodology Assumptions

and Hypotheses along with a brief about the bank.

2. CHAPTER TWO consists of the review of the research papers and books that are referenced to

make the project and are mentioned in bibliography.

3. CHAPTER THREE deal with the methodology used for making the analysis.

4. CHAPTER FOUR contains the application of the research methodology along with presentation of

data and analyzing and interpreting the study by use of various charts, diagrams etc.

5. CHAPTER FIVE deals with the summary of major findings out of the study, conclusions drawn

from it and suggestions with regard to the study.

I would like to thank Dr. H.P Mathur for his support and guidance and also the faculty members of the

Indian Institute of Finance as well as the staff of the Kotak Mahindra Bank for their helpfulness.

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DECLARARTION

I “SINDUJA SHUKLA” bearing Enrollment .No. 4111045045 declare that study on

Macro-prudential Analysis of the BASEL III Accord on the current capital structure of

the Kotak Mahindra Bank has been pursued by me as a part of the requirements of the

Management of Business Finance (MBF) program at Kotak Mahindra Bank Ltd. This

study is being submitted for approval to the Indian Institute of Finance.

I declare that the work and contents of the mentioned project are original and have not

been submitted in a part or full, for any other degree or diploma of this or any other

organization/institute university.

Signature:

Name: Sinduja Shukla

Enrolment No: 4111045045

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CERTIFICATE (FROM THE ACADEMIC GUIDE)

TO WHOMSOEVER IT MAY CONCERN

This is to certify that “Macro-prudential Analysis of the BASEL III Accord on the current capital structure of the Kotak Mahindra Bank” was carried out by “Sinduja Shukla” bearing Enroll.No.4111045045, as a part of requirements of Management of Business Finance. This study has been pursued under my guidance and supervision.

-------------------------------------------- Dr. H.P Mathur

Allahabad Bank Chair Professor in Management Studies,

Faculty of Management Studies,

Banaras Hindu University,

Varanasi.

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CERTIFICATE (FROM THE CORPORATE GUIDE)

TO WHOMSOEVER IT MAY CONCERN

This is to certify that Ms. “Sinduja Shukla” a student of Indian Institute of Finance,

(Delhi & G.Noida), has successfully completed her Live project at Kotak Mahindra

Bank on the topic “Macro-prudential Analysis of the BASEL III Accord on the

current capital structure of the Kotak Mahindra Bank” under the guidance of Ms.

Talat Nasreen, Manager Privilege Banking from Feb18, 2013 to April 26, 2013.

Signature

Name of the Corporate Guide

Address

Page | 5

Company Seal


Acknowledgement

Working on a real life project is the most significant part of the student life where one gets the

glimpse of what one is going to face and gets acquainted with the professional methodology and the

point one is at in the scale of what is to be achieved. I happened to work with Kotak Mahindra Bank

Ltd. under the mentorship of Ms Talat Nasreen, Manager Privilege Banking who has been really

supportive and discerning as a mentor. I would like to sincerely thank her.

I would also extend my gratitude to Dr. HP Mathur for his kind gesture of being my academic

guide despite his busy schedule and being there to help me.

Also, sincere thanks to Prof. J.D Agarwal and the other entire honorable faculty members for

helping me throughout the project.

This project is a sincere effort of understanding the importance of Macro prudential Analysis of

Basel III in respect to the capital structure of the bank. I hope to build upon this experience and

knowledge that I have gained here and to be able to make a valuable contribution towards this

industry.

Sinduja Shukla

4111045045

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Table of Contents

Chapter Specification 9

Chapter 1 10

The project 11

Company Profile 17

Company Financials 19

Chapter -2 24

Literature Review 25

Work done so far in this field 36

Chapter 3 41

Research Methodology 44

Chapter 4 47

Analysis & Interpretation 48

Regression Analysis 49

CAMEL Analysis 56

Altman Z-Score 65

Stress Testing 68

GAP Analysis 79

VaR Analysis 86

Chapter 5 141

Results 142

Appendix 144

Bibliography 160

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Specifics of Chapters in the Project

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Chapter Specifics

Chapter 1 Introduction

Chapter 2 Literature Review

Chapter 3 Research Methodology

Chapter 4 Analysis & Interpretation

Chapter 5 Results & Observations

Chapter 1

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Introduction

The Project

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As the macro environment continues to have an upper hand in the stability of financial systems

worldwide, the financial institutions cannot function in isolation. The effect of this integration is

mirrored into the stability and growth of the institution.

Functional capabilities can be showcased only if these institutions are able to embrace an integrated

set of norms which can place them on the same side of the coin. A stigma of independent

operability was broken when the Basel Accord was introduced as a common global platform for the

mutual growth and development of banks across the globe.

Since then the norms have been globe trotters and have entered many national territories in order to

identify and resolve banking issues. The first and the foremost concern of the Basel Accord have

been to provide the banks with armour of capital adequacy to combat their problems and thus

affecting the capital structure decisions.

This project intends to evaluate the impact of the Basel III Accord on the Kotak Mahindra Bank in

context of various macro factors.

The project is an insight into the most important decision in the entire finance domain i.e. ‘Capital

Structure Decision’, with respect to a globally accepted set of norms ‘Basel III Accord’.

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The focus of the project is limited to only a single bank i.e. Kotak Mahindra Bank.

As the uncertainties in the global scheme of operations are burgeoning the Bank for International

Settlement is trying time and again to nip the newer problems in the bud by playing a proactive role

in the Global Banking Sector.

It was for the first time in 1988 that the Basel I Capital Accord was created after the world saw

eight great bank failures in the USA during the 1980’s. The purpose of the same was:

Strengthen the stability of international banking system.

Set up a fair and a consistent international banking system in order to decrease competitive

inequality among international banks.

The basic achievement of Basel I have been to define bank capital and capital adequacy ratio. In

order to set up a minimum risk-based capital adequacy applying to all banks and governments in

the world, a general definition of capital was required. Indeed, before this international agreement,

there was no single definition of bank capital.

Basel II was introduced in 2004, laid down guidelines for capital adequacy, risk management and

disclosure requirements. The Basel I Accord lacked sensitivity to variations in risks (both between

and within risk categories). The new accord was introduced to keep pace with the increased

sophistication of lenders' operations and risk management and overcome some of the distortions

caused by the lack of granularity in Basel I. Lenders had been able under Basel I to reduce required

capital in ways that did not reflect lower real risk (in what has become known as regulatory capital

arbitrage). The intention was that Basel II will align required minimum capital more closely with

lenders' real risk profile.

It was based on a three pillar structure:

The 3 Pillars of Basel II enshrine the key principles of the new regime.

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Pillar 1 covers the calculation of risk weights to determine a basic minimum capital figure. The

Accord provides for a choice of ways to calculate required capital. The simplest is the standardised

approach, which provides set risk weights for some asset classes and requires the weight on others

to be determined by the public credit rating assigned to the particular asset by the rating agencies.

Lenders are able to choose the more sophisticated 'internal ratings based' (IRB) approach, either

foundation, advanced or retail. These allow lenders to use their own risk models to determine

appropriate minimum capital. Pillar 1 also requires lenders to assess their market and operational

risk and provide capital to cover such risk.

Under Pillar 2, lenders are required to assess risks to their business not captured in Pillar 1, for

which additional capital may be required (for example the risk caused by interest rate mismatches

between assets and liabilities).

Finally, Pillar 3 requires lenders to publish information on their approach to risk management and is

designed to raise standards through greater transparency.

However, certain shortcomings paved way for Basel III Accord. Basel 3 is a continuation of efforts

initiated by the Basel Committee on Banking Supervision to enhance the banking regulatory

framework under Basel I and Basel II. This latest Accord now seeks to improve

the banking sector's ability to deal with financial and economic stress, improve risk management

and strengthen the banks' transparency.

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Requirements under Basel III Accord as against Basel II Accord:

Regulatory Element Proposed Requirement

Higher Minimum Tier 1

Capital Requirement

Tier 1 Capital Ratio: increases from 4% to

6%

The ratio will be set at 4.5% from 1 January

2013, 5.5% from 1 January 2014 and 6%

from 1 January 2015

Predominance of common equity will now

reach 82.3% of Tier 1 capital, inclusive of

capital conservation buffer

New Capital Conservation Buffer »» Used to absorb losses during periods of

financial and economic stress

»» Banks will be required to hold a capital

conservation buffer of 2.5% to withstand

future periods of stress bringing the total

common equity requirement to 7%

(4.5% common equity requirement and the

2.5% capital conservation buffer)

»» The capital conservation buffer must be

met exclusively with common equity

»» Banks that do not maintain the capital

conservation buffer will face restrictions on

payouts of dividends, share buybacks and

bonuses

Countercyclical Capital Buffer »» A countercyclical buffer within a range of

0% - 2.5% of common equity or other fully

loss absorbing capital will be implemented

according to national circumstances

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»» When in effect, this is an extension to the

conservation buffer

Higher Minimum Tier 1

Common Equity Requirement

»» Tier 1 Common Equity Requirement:

increase from 2% to 4.5%

»» The ratio will be set at 3.5% from 1

January 2013, 4% from 1 January 2014 and

4.5% from 1 January 2015

Liquidity Standard »» Liquidity Coverage Ratio (LCR): to

ensure that sufficient high quality liquid

resources are available for one month

survival in case of a stress scenario.

Introduced 1 January 2015

»» Net Stable Funding Ratio (NSFR): to

promote resiliency over longer-term time

horizons by creating additional incentives for

banks to fund their activities with

more stable sources of funding on an ongoing

structural basis

»» Additional liquidity monitoring metrics

focused on maturity mismatch,

concentration of funding and available

unencumbered assets

Leverage Ratio »» A supplemental 3% non-risk based

leverage ratio which serves as a backstop to

the measures outlined above

»» Parallel run between 2013-2017;

migration to Pillar 1 from 2018

Minimum Total

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Minimum Total Capital Ratio »» Remains at 8%

»» The addition of the capital conservation

buffer increases the total amount of

capital a bank must hold to 10.5% of risk-

weighted assets, of which 8.5% must

be tier 1 capital

»» Tier 2 capital instruments will be

harmonized; tier 3 capital will be phased out

The aforementioned requirements will decide the future course of the banks because a lot is to be

kept in buffer. Harmonising actions and reactions in a continuum is obviously a problem. This

project seeks to examine the financial vulnerability of the Kotak Mahindra Bank in the same

context.

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Company Profile

Established in 1985, the Kotak Mahindra group has been one of India's most reputed financial

conglomerates. In February 2003, Kotak Mahindra Finance Ltd, the group's flagship company was

given the license to carry on banking business by the Reserve Bank of India (RBI). This approval

created banking history since Kotak Mahindra Finance Ltd. is the first non-banking finance

company in India to convert itself in to a bank as Kotak Mahindra Bank Ltd. Today, it is one of the

fastest growing banks and among the most admired financial institutions in India. Enumerated

below are the major offerings and role of the bank.

It has over 357 branches and 866 ATMs, which are spread all over India, not just in the

metros but in Tier II cities and rural India as well, redefining the reach and power of

banking.

They cater to the myriad needs of Resident Individuals, NRIs and Businesses.

They offer complete financial solutions for infinite needs of all individual & non-individual

customers depending on the customer's need - delivered through a state of the art technology

platform. Investment products like Mutual Funds, Life Insurance, retailing of gold coins and

bars etc are also offered. The Bank follows a mix of both open and closed architecture for

distribution of the investment products. All this is backed by strong, in-house research on

Mutual Funds.

The Savings Account of the bank goes beyond the traditional role of savings, and allows

customers to put aside a lot more than just money. The worry-free features of the Account

provides a range of services from funds transfer, bill payments, 2-way sweep through

ActivMoney feature & much more. One can place standing instructions for investment for

attractive returns earned through a comprehensive suite products and services that offer

investment options, all delivered seamlessly to the customer by well integrated technology

platforms.

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Apart from Phone banking and Internet banking, the Bank offers convenient banking

facility through Mobile banking, SMS services, Netc@rd, Home banking and Bill Pay

facility among others options that can be booked through Internet or through Phone banking

services. The Savings Account thus provides

The Depository services offered by the Bank allows the customers to hold equity shares,

government securities, bonds and other securities in electronic or Demat forms.

The Our Salary 2 Wealth offering provides comprehensive administrative solutions for

Corporates with features such as easy and automated web based salary upload process

thereby eliminating the paper work involved in the process, a dedicated relationship

manager to service the corporate account, customized promotions and tie - ups and many

such unique features. The whole gamut of investment products and investment advisory

services is available to the salary account holders as well.

For the business community, they offer comprehensive business solutions that include the

Current Account, Trade Services, Cash Management Service and Credit Facilities, keeping

in mind the myriad needs of businesses.

The Wholesale banking products offer business banking solutions for long-term investments

and working capital needs, advice on mergers and acquisitions and equipment financing.

To meet special needs of the rural market, they have dedicated business offerings for

agricultural financing and infrastructure. The Agriculture Finance division delivers

customised products for capital financing and equipment financing needs of our rural

customers.

For financial liquidity they offer loans that meet the personal requirements with quick

approval and flexible payment options.

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To complete the personal financial offerings space, they now offer Kotak Credit Card which

is a hassle-free, transparent product that also happens to be the first vertical credit card in

the industry.

Kotak Mahindra Bank addresses the entire spectrum of financial needs of Non-Resident

Indians. Their tie-up with the Overseas Indian Facilitation Centre (OIFC) as a strategic

partner them a platform to share a comprehensive range of banking & investment products

and services for Non Resident Indians (NRIs) and Persons of Indian Origin (PIOs).

Their Online Account Opening facility and Live Chat service helps customers to get in

touch with the bank at the comfort of their (customers’) homes and at convenience. These

offerings are specifically designed to suit the overseas Indian's personal financial needs and

give the global Indians a near to home feel.

Kotak Mahindra Bank (KMB) offers a very wide range of products and services targeted at retail

customers, delivered through a state of the art technology platform. In addition to branch banking,

convenience banking facilities offered by the Bank include telephone banking, internet banking,

mobile banking, direct pay services, payment gateway for online shopping, a global debit card, a

prepaid spending card and facility to transfer of funds to all Visa debit and credit cards in India.

It has five broad venture segments:

Venture Fund Management

Corporate Banking

Retail Liabilities

Lending

Treasury & Investments

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The symbol of the infinite Ka reflects the global Indian personality. The Ka is uniquely Indian

while its curve forms the infinity sign, which is universal. One of the basic tenets of economists is

that man's needs are unlimited. The infinite Ka symbolises that the bank has infinite number of

ways to meet those needs.

Disclosures

Company’s Financials:

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Balance sheet(Rs crore)

Mar ' 12 Mar ' 11 Mar ' 10 Mar ' 09 Mar ' 08Sources of fundsOwner's fundEquity share capital 370.34 368.44 348.14 345.67 344.67Share application money - - - - -Preference share capital - - - - -Reserves & surplus 7,610.41 6,464.95 4,191.78 3,559.86 3,249.04Loan fundsSecured loans - - - - -Unsecured loans 38,536.52 29,260.97 23,886.47 15,644.93 16,423.65Total 46,517.28 36,094.36 28,426.38 19,550.46 20,017.35Uses of fundsFixed assetsGross block 955.41 831.8 745.34 460.61 391.42Less : revaluation reserve - - - - -Less : accumulated depreciation 505.45 406.2 317.69 247.25 181.17Net block 449.97 425.61 427.65 213.36 210.25Capital work-in-progress - - - - -Investments 21,566.81 17,121.44 12,512.66 9,110.18 9,141.99Net current assetsCurrent assets, loans & advances 1,935.91 1,503.33 1,420.69 1,622.33 1,258.43Less : current liabilities & provisions 2,553.67 3,032.36 2,869.42 3,257.34 3,175.75Total net current assets -617.76 -1,529.03 -1,448.73 -1,635.01 -1,917.32Miscellaneous expenses not written - - - - -Total 21,399.01 16,018.01 11,491.58 7,688.52 7,434.92Notes:Book value of unquoted investments - - - - -Market value of quoted investments - - - - -Contingent liabilities 23,485.52 16,761.36 7,219.79 5,674.45 7,999.34Number of equity sharesoutstanding (Lacs) 7406.9 7368.72 3481.41 3456.69 3446.73

Quarterly results in brief

(INR crore)

Dec' 12 Sep' 12 Jun' 12 Mar' 12 Dec' 11

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Sales 2,094.58 1,923.70 1,815.83 1,744.83 1,640.98

Operating profit 1,497.13 1,326.36 1,267.62 1,243.56 1,120.46

Interest 1,271.73 1,165.57 1,094.53 1,057.10 989.53

Gross profit 572.62 482.21 448.38 445.32 443.61

EPS (INR) 4.86 3.77 3.8 4.01 3.73

Quarterly results in details


Other income 304.86 250.8 241.15 254.23 281.96

Stock adjustment - - - - -

Raw material - - - - -

Power and fuel - - - - -

Employee expenses 263.59 242.77 256.55 224.83 226.01

Excise - - - - -

Admin and selling expenses - - - - -

Research and development expenses - - - - -

Expenses capitalised - - - - -

Other expenses 291.5 283.95 257.53 271.82 263.79

Provisions made 42.36 70.62 34.14 4.63 30.72

Depreciation - - - - -

Taxation 168.58 131.21 131.79 143.77 136.81

Net profit / loss 361.68 280.38 282.45 296.93 276.08

Extra-ordinary item - - - - -

Prior year adjustments - - - - -

Equity capital 372.1 371.65 371.24 370.35 369.78

Equity dividend rate - - - - -

Agg.of non-prom. shares (Lacs) 4085.11 4076.25 4067.93 4050.06 4037.77

Agg.of non promotoholding (%) 54.89 54.84 54.79 54.68 54.6

OPM (%) 71.48 68.95 69.81 71.27 68.28

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GPM (%) 23.86 22.18 21.8 22.28 23.07

NPM (%) 15.07 12.89 13.73 14.85 14.36

Capital adequacy as at December 31, 2012

Capital

Tier-1 capital 8,574.6

Tier-2 capital 843.81

Total Capital funds of the bank 9,418.38

Total Capital required at 10% 6,024.90

Capital Adequacy ratio 15.63%

Tier- 1 capital adequacy ratio 14.23%

Tier – 2 Capital Adequacy ratio 1.40%

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.

In this project I have tried to use simple tools to analyse a basic structure of the macro-prudential

analysis from the bank’s point of view for a short time horizon. The techniques used or in better

words the research methodology is a simplistic approach to linking various elements of bank’s

functioning with the capital structure and the way these things disturb the entire equilibrium of the

bank’s capital or support the same. The disclosures and the data used have been provided by the

bank as per the discretion of the bank and the extent to which the bank needed my work.

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Chapter2

Literature Review

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Literature Review

Macro-prudential analysis has gained momentum in recent years. As per a report by the

International Monetary Fund,” Keeping individual financial institutions sound is not enough. A

broader approach is needed to safeguard the financial system.”

Financial vulnerabilities have augmented in today’s world and the reasons do not need an

introduction. What needs to be followed is a principal that if benefits are integrated with global

integration, so are the troubles and menaces. Hence, a need of a framework for avoiding these

menaces opens the doors for ‘Macro-prudential Analysis’.

Macro-prudential analysis has many outstanding contributions to its recognition. Enumerated

below are the works which have gained worldwide acceptability and popularity in connotation to

the macro prudential analysis.

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Literature available on the importance of Macro prudential Analysis in context of identifying the

impact of Basel Accord on Banks

Cooke Committee (1974), the precursor to the Basel Committee on Banking Standards first used

this terminology. But only in late 2000s the implementation started.

Blunden (1987) highlighted in a speech how a systemic view could imply curbing banking practices

that would appear to be prudent from an individual bank’s perspective.

Kiyotaki and Moore (1997) in their model of credit cycles have shown how small shocks to the

economy might be amplified by credit restrictions, giving rise to large output fluctuations. The

model assumes that borrowers cannot be forced to repay their debts. Therefore, in equilibrium,

lending occurs only if it is collateralized. That is, borrowers must own a sufficient quantity of

capital that can be confiscated in case they fail to repay. This collateral requirement amplifies

business cycle fluctuations because in a recession, the income from capital falls, causing the price

of capital to fall, which makes capital less valuable as collateral, which limits firms' investment by

forcing them to reduce their borrowing, and thereby worsens the recession. The Kiyotaki-Moore

model showed how relatively small shocks might suffice to explain business cycle fluctuations, if

credit markets are imperfect.

Dermine (1999) in his paper “The Economics of Bank Mergers in the European Union, a Review of

the Public Policy Issues” has talked about the nature of merger and acquisitions and the way public

policy issues need to be addressed in the light of such mergers and acquisitions, how they should

affect the financial stability and competition. He says that the banking world is becoming

increasingly international; there is a need to reassess the structure of bank regulation and

supervision which has been historically assumed by each nation State. So, there is a need for a

macro prudential view point of testing the banks longevity.

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Sanctos (2000) presents that the theoretical literature on bank capital regulation and analyses some

of the approaches to redesigning the 1988 Basel Accord on capital standards. The paper starts with

a review of the literature on the design of the financial system and the existence of banks. It

proceeds with a presentation of the market failures that justify banking regulation and an analysis of

the mechanisms that have been suggested to deal with these failures. The paper then reviews the

theoretical literature on bank capital regulation. This is followed by a brief history of capital

regulation since the 1988 Basel Capital Accord and a presentation of both the alternative

approaches that have been put forward on setting capital standards and the Basel Committee's

proposal for a new capital adequacy framework. This paper has actually justified the BASEL

Accords inevitability.

Kunt and Huizinga (2000) in their paper Financial Structure and Bank Profitability have shown that

for countries with underdeveloped financial systems, a move toward a more developed financial

system reduces bank margins and profitability. Controlling for both the bank and market

development, financial structure per se - the development of banks relative to that of markets-

appears to have no independent effect on bank performance.

Countries differ in the extent to which their financial systems are bank-based or market-based. The

financial systems of Germany and Japan, for example, are considered bank-based because banks

play a leading role in mobilizing savings, allocating capital, overseeing investment decisions of

corporate managers, and providing risk management vehicles. The systems of the United States and

the United Kingdom are considered more market-based. Thus this calls for a need of a macro

prudential analysis of the financial system.

Bordo et al. (2001) developed and examined a discrete financial stress index including time series

on business failures, banking conditions, the real interest rate and a quality spread describing the

condition of the US financial sector.

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http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=15966




Kashyap and Stein (2004) show how time-varying capital requirements are optimal in a model

where the objective of the social planner comprises both protecting the deposit insurance fund and

maintaining credit creation during recessions.

Dzeawuni Sr. and Taoko II (2004) have given a breakthrough to the ratio calculations by

introducing that the CAMEL approach is a way forward for fundamental analysis of the banks.

Despite the continuous use of financial ratios analysis on banks performance evaluation by banks’

regulators opposition to it skill thrive with opponents coming up with new tools capable of flagging

the over-all performance (efficiency) of a bank. This research paper was carried out; to find the

adequacy of CAMEL in capturing the overall performance of a bank; to find the relative weights of

importance in all the factors in CAMEL; and lastly to inform on the best ratios to always adopt for

evaluating banks performance. The paper gives a clear picture of the best trusted ratios for banks’

evaluation even in the CAMEL framework. Thus, provide an efficient way of analysis.

Hanschel and Monnin (2005) both developed and examined a continuous stress index for the Swiss

banking sector by equal-weighting market price, balance sheet, non-public and other structural data.

Illing and Liu (2006) developed a financial stress index for the Canadian sector by variance-equal

weighting several financial market indicators into one single index.

Jimenez and Saurina (2006) find empirical evidence of more lenient credit standards during boom

periods, both in terms of screening of borrowers and collateral requirements. Motivated by this

evidence, they suggest forward-looking loan loss provision that take into account the credit risk

profile of banks’ loan portfolios along the business cycle as a regulatory tool.

Zhu and Phyktin (2006) in their paper “Measuring Counterparty Credit Risk for Trading Products

under Basel II” have showed the treatment of counterparty credit risk of OTC derivatives under

Basel II. According to this framework, minimum capital requirements for counterparty credit risk

Page | 29





are to be calculated according to the corporate loan rules applied to the appropriate exposure at

default (EAD) calculated at the netting set level. They have presented both Non-Internal and

Internal Model Methods (IMM) of calculating this EAD. To obtain supervisory approval for the

IMM, banks must be able to calculate expected exposure at the netting set level for a set of future

dates. They also discussed a modeling framework that can be used for calculating exposure

distribution at a set of future dates and, in particular, for calculating expected exposure profiles.

This framework can be used for both regulatory and internal purposes. Additionally, we explained

the treatment of margin agreements under the IMM that allows one to calculate the collateralized

EPE measures: modeling collateralized exposure and the Shortcut Method. They discussed a

general approach to modeling collateralized exposure that enables one to compute the collateral at a

future date as a function of uncollateralized exposure at another date that precedes the primary date

by the margin period of risk. Finally, they suggest a simple and fast method under this approach for

modeling collateral that avoids the simulation of exposure at the secondary dates. This framework

was an interesting initiative by the researchers in the direction of Basel 2 norms

that could provide banks an opportunity to work out their risk profiles before being able to adopt

the Basel norms. This model finds its applicability even for the Basel 3 norms as monitoring

exposures by banks is more important due to increase in the Capital Conservation Buffers.

Agresti, Baudino & Poloni (2007) have compared the works of ECB & IMF. In January 2007 the

International Monetary Fund (IMF) published, on an ad hoc basis, a series of financial soundness

indicators (FSIs) based on a common methodology (the IMF compilation Guide) for 62 countries,

including all 27 European Union countries. The European Central Bank (ECB), jointly with the

Banking Supervision Committee (BSC), has an interest in monitoring the development of this IMF

initiative in the context of its own work on compiling macro-prudential indicators (MPIs). The aim

of this paper is to identify the main similarities and differences between the FSIs and the MPIs for

national banking sectors, as the overlap between MPIs and FSIs in this sub-set is greatest. As a

result of the recently issued amendments to the IMF compilation Guide for FSIs, some key

Page | 30


methodological differences between the two approaches have been eliminated and it is therefore

expected that the figures published by the two institutions will soon converge. The paper concludes

with an investigation of the few other areas where the remaining differences could potentially be

narrowed.

Borio & Shim (2007) advocated that in the economic environment that has been emerging over the

last couple of decades, it is more likely that the occasional build-up of financial imbalances,

typically in the form of unsustainable credit and asset price booms, will occur against the

background of low and stable inflation, posing a potential threat to financial and macroeconomic

stability. This means that the scope for monetary policy to lean against the build-up may be more

constrained than in the past, when those imbalances would normally develop alongside rising

inflation. This puts a premium on a strengthening of the macro prudential orientation of prudential

frameworks, designed to restrain the build up of the imbalances and to make the financial system

better able to withstand their unwinding. In this paper, we review the progress made in this

direction in recent years. They conclude that there is now a much keener awareness of the

importance of a macro prudential orientation but that progress in making it operational, while

considerable, has been slower. The main obstacles are of an analytical and, above all,

institutional/political economy nature. They have suggested ways in which these obstacles could be

addressed and underline the potential complementary role that adjustments in monetary policy

frameworks could play.

Puddu (2008) constructed a real continuous indicator for the US banking system by aggregating

balance sheet variables of the commercial banking sector and examines the impact of different

weighting schemes on the replication ability of financial crisis events.

Misina and Tessier (2008) have focused on measuring financial (in) stability and capturing financial

distress using VAR. These empirical models are flexible tools for forecasting and allow tracing the

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transmission of shocks through the economy. At the same time, they offer only very stylized

descriptions of the dynamics of the financial sector, and of the feedback to the macro economy.

(Borio and Drehmann, 2009) advocated that macro stress tests, which can be used to trace the

response of the financial system to unusually large exogenous shocks. Macro stress tests are by

nature forward-looking and highlight the transmission of shocks within the system. They rely

explicitly on an underlying view of the forces that can drive financial distress. Similarly to other

methodological approaches, however, these models generally fail to capture feedback effects

between the financial system and the macro economy. They also fail to capture the key aspect of

financial distress that small shocks can have very large effects.

Gorton (2009) in his paper ‘Slapped in the Face by the Invisible Hand: Banking and the Panic of

2007 show that the 'shadow banking system' at the heart of the current credit crisis is, in fact, a real

banking system – and is vulnerable to a banking panic. Indeed, the events starting in August 2007

are a banking panic. A banking panic is a systemic event because the banking system cannot honour

its obligations and is insolvent. Unlike the historical banking panics of the 19th and early 20th

centuries, the current banking panic is a wholesale panic, not a retail panic. In the earlier episodes,

depositors ran to their banks and demanded cash in exchange for their checking accounts. Unable to

meet those demands, the banking system became insolvent. The current panic involved financial

firms 'running' on other financial firms by not renewing sale and repurchase agreements (repo) or

increasing the repo margin ('haircut'), forcing massive deleveraging, and resulting in the banking

system being insolvent. The earlier episodes have many features in common with the current crisis,

and examination of history can help understand the current situation and guide thoughts about

reform of bank regulation. New regulation can facilitate the functioning of the shadow banking

system, making it less vulnerable to panic.

Hanson, Kashyap &Stein (2009) in their paper they have offered detailed vision for how a macro

prudential regime might be designed. Their prescriptions follow from a specific theory of how

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modern financial crises unfold, and why both an unregulated financial system, as well as one based

on capital rules that only apply to traditional banks, is likely to be fragile. They begin by identifying

the key market failures at work: why individual financial firms, acting in their own interests,

deviate from what a social planner would have them do. The paper draws a conclusion that an

obvious alternative: during the phase-in period, regulators should push those banks that are shy of

the new capital standards to raise new dollars of equity, rather than giving them the option to adjust

via asset shrinkage.

Saporta (2009) has mentioned that the macro-prudential objective is ensuring the resilience of the

financial system as a whole in order to maintain a stable supply of financial intermediation services

across the credit cycles.

Caruana (2010) has described the objective of macro-prudential policy as “to reduce systemic risk

by explicitly addressing the inter-linkages between, and common exposures of, all financial

institutions, and the pro-cyclicality of the financial system.

Jahn & Kick (2010) introduced a continuous and forward-looking stability indicator for the German

banking system which is used to identify macro prudential early warning indicators and

international and regional spill over effects. The indicator comprises not only major systemically

relevant institutions, but also small private, savings, and cooperative banks, which are in particular

relevant for regional credit supply. Therefore, the stability indicator is meant to provide a macro

prudential analysis tool for banking supervisors and policy makers.

Cihak & Schaeck (2010) addressed the issue of how reliable are the aggregate prudential ratios for

financial stability analysis by analyzing the performance of aggregate prudential ratios in systemic

banking crises, drawing upon a large cross-country dataset. They have cautioned against sole

reliance on these indicators, and advocate supplementing them with other tools and techniques.

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Nonetheless, the findings offer evidences that some of the ratios can help identify systemic banking

problems. Their work is a great contribution to the area of macro prudential analysis.

Galati & Moessner (2010) published a literature review which advocates the need to go beyond a

purely micro approach to financial regulation and supervision. In recent years, the number of policy

speeches, research papers and conferences that discuss a macro perspective on financial regulation

has grown considerably. The policy debate is focusing in particular on macro prudential tools and

their usage, their relationship with monetary policy, their implementation and their effectiveness.

Blanchard et al. (2010) discuss how coordination is achieved between monetary and regulatory

authorities, and whether the central bank should be in charge of both. They argue that for three

main reasons, the past trend toward separating decision making for these two policies may well

have to be reversed. First, their advantage in monitoring macroeconomic developments makes

central banks an obvious candidate as macro prudential regulators. Second, centralizing macro

prudential responsibilities within the central bank would avoid problems of coordinating the actions

of separate agencies during a crisis such as those highlighted during the bailout of Northern Rock.

Third, monetary policy decisions have potential implications for leverage and risk taking.

Shin (2010) discusses how countercyclical capital requirements, together with forward-looking

statistical provisioning schemes, can mitigate the harmful effects of securitization on risk

concentration in the financial system

Hanson et al (2010) argue that one problem with this approach is that at times of distress; the

regulatory constraint on bank capital might be insufficient to convince markets to continue funding

troubled banks. They therefore argue in favour of minimum capital ratios in good times that

substantially exceed the standards that markets might impose in bad times.

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Stein (2010) developed a theoretical model that shows that in the absence of regulation, money

creation by banks can lead to an externality in which they issue too much short-term debt and make

the financial system excessively vulnerable to costly crises.

Perotti & Suarez (2010) in their paper have discussed that liquidity regulation when short-term

funding enables credit growth but generates negative systemic risk externalities. It focuses on the

relative merit of price versus quantity rules, showing how they target different incentives for risk

creation.

When banks differ in credit opportunities, a Pigovian tax on short-term funding is efficient in

containing risk and preserving credit quality, while quantity-based funding ratios are distortionary.

Liquidity buffers are either fully ineffective or similar to a

Pigovian tax with deadweight costs. Critically, they may be least binding when excess credit

incentives are strongest.

When banks differ instead mostly in gambling incentives (due to low charter value or

overconfidence), excess credit and liquidity risk are best controlled with net funding ratios. Taxes

on short-term funding emerge again as efficient when capital or liquidity ratios keep risk shifting

incentives under control. In general, an optimal policy should involve both types of tools.

Borio (2010) argued that meeting these challenges calls for a finely balanced blend of boldness and

realism. Boldness to design a framework that could in principle act as an effective speed limit on

the build-up of financial imbalances; realism to avoid setting an overly ambitious criterion of

success, beyond strengthening the resilience of the financial system. What is needed is the boldness

to develop better quantitative indicators of systemic risk; realism to recognise the inevitable role of

judgement ;boldness to develop aggregate approaches; realism to avoid the risk of drifting

unintentionally into credit allocation policies; boldness to rely as far as possible on simple and

transparent rules; realism to acknowledge the need for constrained discretion. Boldness to design

new governance arrangements, ensuring a key role for central banks, the necessary degree of

operational independence and control over instruments; realism to avoid a false sense of precision

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in the definition of mandates and to manage expectations about what can be achieved. Boldness to

avoid limiting how daring research should be; realism to gear it also to providing responses to the

more pressing questions policymakers are facing.

Schoenmaker& Wierts (2011) in their research paper have emphasised on the need for a coherent

framework for macro prudential policy. The paper puts forward and implements a method for

arriving at a coherent policy framework. It starts by defining the role of macro prudential policy in

the overall policy framework for the monetary and financial system. It then specifies the objective,

intermediate targets (pillars), instruments, decision-making, accountability, and the legal base. They

introduced a two pillar strategy. The basic presumption is that each instrument should be related to

its intermediate target (pillar). This allows us to select a limited set of core instruments aimed at

stabilising financial imbalances (pillar 1) and addressing externalities that arise from

interconnections in the financial system (pillar 2). The conclusion drawn by the paper says to make

the two pillar strategy operational, the basic presumption is that each instrument should be related

to its intermediate target. This allows us to select a limited set of core instruments aimed at

stabilising financial imbalances and addressing externalities in the financial system.

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Borio (2011) in his paper ‘Central banking post-crisis: What compass for uncharted waters?’

has talked that the global financial crisis has shaken the foundations of the deceptively

comfortable pre-crisis central banking world. Central banks face a threefold challenge:

economic, intellectual and institutional. This essay puts forward a compass to help central

banks sail in the largely uncharted waters ahead. The compass is based on tighter integration

of the monetary and financial stability functions, keener awareness of the global dimensions

of those tasks, and stronger safeguards for an increasingly vulnerable central bank

operational independence.

Vinals (2011) Macro-prudential policy seeks to address two specific dimensions of systemic risk

namely, time dimension and cross-sectional dimension. Those dimensions entail different policy

implications. Time dimension reflects a cumulative, amplifying mechanism that operates within the

financial system, as well as between the financial system and the real economy. In time dimension,

risks are associated with swings in credit and liquidity cycles. Here risk evolved overtime, referring

to the financial cycle and known as the pro-cyclicality. Macro prudential policy is performed as

stabilizer by inducing a build-up of cushions in good times so that they can be drawn down in bad

times. Cross-sectional dimension reflects the distribution of risk in the financial system at a given

point of time. Cross-sectional dimension focuses on the concentration of risk in certain financial

institutions, those institutions having similar exposures within the financial system and who have

interconnected. Macro-prudential tool is focused on the risk with respect to the systemic

significance of individual institutions.

Buncic & Milekey (2012) authored a paper drawing on the lessons from the global financial crisis

and especially from its impact on the banking systems of Eastern Europe; their paper proposes a

new practical approach to macro prudential stress testing. The proposed approach incorporates:

(i) Macroeconomic stress scenarios generated from both a country specific statistical model

and historical cross-country crises experience;

(ii) Indirect credit risk due to foreign currency exposures of unhedged borrowers;

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(iii) Varying underwriting practices across banks and their asset classes based on their

relative aggressiveness of lending;

(iv) Higher correlations between the probability of default and the loss given default during

stress periods;

(v) A negative effect of lending concentration and residual loan maturity on unexpected

losses; and

(vi) The use of an economic risk weighted capital adequacy ratio as the relevant outcome

indicator to measure the resilience of banks to materializing credit risk.

The authors apply the proposed approach to a set of Eastern European banks and discuss

the results. The paper gives an insight into how a proper macro prudential analysis can

be armour for combating credit risk.

Kawata, Kurachi, Nakamura, Teranishi(2012) published a paper that uses a financial macro-

econometric model to compare and analyze the impact of macro prudential policy measures -- a

credit growth restriction, loan-to-value and debt-to-income regulations, and a time-varying capital

requirement -- on the economic dynamics through the financial cycle with the asset price bubble.

Their analysis shows that although these macro prudential policy measures dampen economic

volatility, it is possible that they reduce average economic growth, and the effects on the economic

dynamics differ widely among macro prudential policy measures. In addition, the policy effects are

changed dramatically by lags in recognizing the state of the economy. Their results also suggest

that macro prudential policy measures can help contribute to more stable financial intermediation

by raising the resilience of the financial system against risks.

Meissonier & Renne (2012) said that Macro-prudential policy aims at maintaining system-wide

financial stability that is equal to preventing systemic risk to limit real economic costs of financial

instability by enhancing resilience of actors/structures ex ante and addressing imbalances in real-

time.

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Arnold, Borio, Ellis & Moshiriand (2012) in their paper have analysed various issues that need to

be tackled when promoting financial stability, reviewing the progress made in certain key areas and

the remaining challenges. It explores the measurement of systemic risk and of individual

institutions’ contribution to it. It discusses aspects of macro prudential frameworks, including how

the countercyclical capital buffer envisaged in Basel III takes into account the properties of the

financial cycle and the strengths and weaknesses of macro-stress tests. It analyses some of the

challenges of how best to monitor financial systems and the broader economy in order to detect

signs of vulnerability that might lead to future bouts of financial instability and of how to set

prudential policy accordingly. And it discusses the evolution of capital adequacy standards and the

new emphasis on liquidity standards in international regulation.

Utari & Aritramurti have proposed that any shock faced by individual institutions can spread out

quickly due to the interconnectedness and lead to systemic risk. This condition is worsened by pro-

cyclical behaviour of those institutions in the economy. Thus, the financial system has an inherent

bias toward pro-cyclicality. When there are changes in the financial market, both financial and non

financial institutions with similar risks can emit similar common reactions, creating collective

behaviour that amplifies the economic cycle fluctuations. Macro-prudential policy is needed to

anticipate and mitigate financial risk. It is not always the case that the implementation of macro-

prudential policy can eliminate the vulnerability of the financial system to shock. Nonetheless,

having a proper macro-prudential policy in place will support the stability of the financial system,

enhance market resilience toward shock and can serve as an early warning system to anticipate

potential crisis in the future.

Borgioli, Gouveia, Labanca (2013) showed that non-consolidated, host-country Monetary Financial

Institutions (MFI) balance sheet data, which constitutes a key source of input into monetary

analysis, are a rather weak proxy for consolidated, home-country data and therefore cannot easily

substitute CBD for the purposes of macro-prudential assessment. In addition, it is argued that,

notwithstanding the relevance of large banks, medium-sized and small banks must also be taken

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into account in financial stability analysis, given their relevance in several EU countries and their

different business models. A discussion follows on how aggregate data, broken down by bank size,

can be used to complement micro data, in particular by signaling where and what to look for, again

highlighting the differences between large banks on the one hand and small and medium sized

banks on the other.

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Summary of the work done so far in the area of macro prudential analysis:

Ever since its introduction in the Banking arena the term macro prudential has become an inevitable

part of it. The Cooke Committee used the word for the first time.

“The Chairman [W P Cooke, Bank of England] said that microeconomic problems (which were of

concern to the Committee) began to merge into macro-economic problems (which were not) at the

point where micro-prudential problems became what could be called macro-prudential ones. The

Committee had a justifiable concern with macro-prudential problems and it was the link between

those and macro-economic ones which formed the boundary of the Committee's interest.”

The recent financial crises have made the word even popular. Economies worldwide have realised

the importance of ‘it’ making it a policy speak. The size of an economy or its financial system is

irrelevant for conducting a macro prudential analysis. It has to be done at every level, big or small.

Global Financial Systems being in turmoil need to tackle issues like strengthening the shock

absorption capacity of the banks and financial institutions at individual levels as initial steps of a

defence mechanism. Another problem to be addressed is ensuring the entire banking system does

not fall prey to the contagion effect of one bank’s failure.

In a nutshell, one can say that the macro-prudential elements of the BASEL III have been

introduced in order to prevent systemic risks or at least contain it. The basic elements of the same

are:

Leverage ratio

Capital Conservation Buffer

Counter Cyclical Capital Buffer

Addressing procyclicality of provisioning

Addressing too-big-to-fail problems

Addressing reliance on external credit rating

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Following are the most popular attempts, steps in the direction of Macro-prudential Analysis of the

world which has become a trend setter for the entire world’s financial systems:

Introduction of the Financial Soundness Indicators by the IMF to support the macro-

prudential analysis and assessing strengths and vulnerabilities of financial systems.

The Bank for International Settlements has time and again emphasised on the need for a

comprehensive approach to address various challenges in the path of implementation of the

macro-prudential policies including:

Design and collection of better information and data to support systemic risk

identification and modeling;

Design of techniques to identify and measure systemic risk that utilise this

information and help inform the design of policies;

Design of an effective macro-prudential toolkit of powers and instruments,

including the criteria for the choice and calibration of the instruments and methods

to assess their effectiveness, as well as the respective merits of rules versus

discretion; and

Design of appropriate governance arrangements for the exercise of the macro-

prudential policy powers.

The bank has also given various tools and models for the same purpose.

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In the United States of America, the forerunner of the Global Financial Crisis, the Macro-

prudential Policy Analysis section in the Federal Reserve System is primarily responsible

for research on the ability of macro-prudential policies to foster financial stability and to

contribute to the implementation of policies and tools. This includes developing responses

to emerging threats to financial stability, including contributing a macro-prudential

perspective to supervisory exercises, such as stress tests and resolution planning, and

contributing to regulatory reform to increase the resilience of the financial sector. Section

members work with economists and professionals from other Divisions at the Board and

Reserve Banks, and are expected to contribute to multi-division and multi-agency efforts to

implement macro-prudential policy.

British Bankers Association series of occasional papers titled “Next Step in Banking”

contains a paper called ‘A Possible Macro-prudential Approach’ which was designed to

stimulate debate in the context of the global turmoil and propagates that the need for macro-

prudential regulation to provide a link between the stewardship of the economy and the

oversight of individual firms is of utmost importance.

Australia’s financial stability policy framework involves clear mandates for financial

stability distributed across several agencies, with the Council of Financial Regulators (CFR)

playing a central coordinating role. The prudential elements of that framework rest with

APRA (Australian Prudential Regulation Authority), with analytical support from the

Reserve Bank of Australia. A document called Macro-prudential Analysis and Policy in the

Australian Financial Stability Framework– originally prepared as background for the IMF

FSAP team in early 2012 – sets out the tools and practices of these two agencies that are

designed to support financial stability from a system-wide perspective. The Australian

authorities view macro-prudential policy as subsumed within the broader and more

comprehensive financial stability policy framework. So, APRA has been given the

responsibility of setting prudential standards and instruments for supervision of institutions.

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In context of European Union, various supervisory arrangements have led to the formation

of European Systemic Risk Board (ESRB) as the body responsible for EU macro-prudential

oversight. The European Central Bank supports the macro-prudential oversight process in

the following ways:

(i) risk surveillance by the collection of market data;

(ii) risk identification and evaluation by analytical reviews of the information collected; and

(iii) Risk mitigation by actions such as issuing risk warnings and recommendations.

Also, the individual economies of the European Union have resorted to Macro-prudential analysis

in the wake of worries arising due to the membership and the conditions of the EU.

For example: Croatia which suffered even after having a better position as against other EU

members.

In Japan, that had undergone a major banking crisis in the lost decade, the Bank of Japan

has started conducting macro-prudential analysis ever since 2002; it employs technique such

as VaR, Scenario Analysis & Stress Testing and Structure Modeling for the same.

In Chinese economy, there exists a systemic risk in both the time (procyclicality) and cross-

sectional (contagion) dimensions. The former is reflected as credit and asset price risks,

while the latter is reflected as the links between the banking sector and informal financing

and local government financing platforms. IMF conducted a study based on 171 banks that

has shown some macro-prudential policy tools (e.g., the reserve requirement ratio and

house-related policies) are useful, but they cannot guarantee protection against systemic risk

in the current economic and financial environment. Nevertheless, better-targeted macro-

prudential policies have greater potential to contain systemic risk pertaining to the different

sizes of the banks and their location in regions with different levels of economic

development. Complementing macro-prudential policies with further reforms, including

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further commercialization of large banks, would help improve the effectiveness of those

policies in containing systemic risk in China.

In New Zealand, the Reserve Bank has extended its efforts to monitor and analyse financial

stability issues by the establishment of the Macro-Financial Stability (MFS) section in the

Reserve Bank to identify and address some of the potential financial instability issues New

Zealand faces.

Apart from all the facts above, one fact that is an essence of all the facts presented above is

that the conditions in each country, state, individual institutions, etc, for appropriate results

of the macro-prudential policies is the use of correct tools that have been devised over a

long period of time. The Committee on the Global Financial Systems has prepared a report

in the same context called “Operationalizing the selection and application of macro-

prudential instruments”.

BIS has also provided the key issues for the success of macro-prudential policies which says

While the objectives of monetary and fiscal policies are clearly defined, and often precisely

quantified, the situation is less clear in the case of financial stability. A broad consensus has

emerged on the idea that “macro-prudential” policies directed to preserving financial

stability should limit systemic risk by addressing both the cross-sectional dimension of the

financial system, with the aim of strengthening its resilience to adverse real or financial

shocks, and its temporal dimension, to contain the accumulation of risk over the business or

financial cycle. A key problem is that, whether a broad or a specific mandate is chosen, we

are still far from an operational definition of these objectives. We do have an ample array of

indicators and early warning signals, but we still lack a coherent framework to interpret

them, to assess the need for macro-prudential intervention, and to measure the success of the

policies adopted. While the notion implicit in all definitions of macro-prudential objectives

is that what warrants a macro-prudential regulatory intervention is systemic risk (a negative

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externality), systemic risk presents a number of challenges to the policymaker. First, it is

hard to measure, because of its various dimensions: procyclicality; network or contagion

risk – the spill over effects of a single institution’s distress on the rest of the financial

system; correlation risk, which reflects the common exposures of all financial institutions to

the same risk factors; and concentration risk, due to the presence of a few dominant

institutions in key financial markets and activities. Second, systemic risk may be extremely

difficult to spot ex ante. These difficulties in measuring systemic risk have important

implications for the practical implementation of macro-prudential policy and for the

accountability of the macro-prudential authority. As the new authorities start working, they

will have to base their decision-making on operational arrangements.

Thus, Macro prudential analysis has actually illuminated ways for more conscious and prudent

capital planning.

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Chapter 3

Research Methodology

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Using the data (Secondary) provided by the company and self-findings, the methodology used for

making the analysis in the project includes the following:

Regression analysis

CAMEL Analysis

Stress Testing

Scenario Analysis

VaR

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1: Regression Analysis

A statistical technique used to find relationships between variables for the purpose of

predicting future values. In case of banks usually, the sustainability depends upon predicting the

profitability by regressing it against various parameters such as amount of loan, the levels of NPAs,

etc. All these values tend to determine the financial strength of the bank and give a clarity of

whether the bank is ready to accept the BASEL III norms or not and if so how far.

2: CAMEL Analysis

This approach developed in the United States of America is the wholesome approach of testing

banks vulnerability as against highest standards of norms that are required to ensure a bank’s

functioning. It is based on evaluation of various ratios and presents a better picture of ratio analysis

than mere fundamental analysis.

CAMELS stand for:

C- Capital Adequacy

A- Asset Quality

M- Management Quality

E- Earnings Ratios

L- Liquidity Ratios

S- Sensitivity to Market Risk

This approach helps to evaluate banks with complete coverage of the factors affecting the banks

creditworthiness.

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3: Stress Testing

It is a tool to gauge the impact of stressors on the bank. A simulation technique used on asset and

liability portfolios to determine their reactions to different financial situations. These situations are

various economic scenarios which are termed by the IMF as “unlikely but plausible”. They mainly

assess credit risk, market risk and liquidity risk.

4: Scenario Analysis

This analysis is mainly concerned with the measurement of the operational risk. A bank may use

scenario analysis in order to forecast what may happen if the economy follows various paths and in

each case how it must determine to distribute the assets between asset types and from this it can

calculate a scenario-weighted expected return to help demonstrate the attractiveness of the financial

environment.

5: GAP Analysis

It is a way of capturing bank’s exposure to the changing rates of interests. Gap allows you to get a

quick and intuitive sense of how a bank is positioned by comparing the values of the assets and

liabilities that roll over—or re-price—at various time periods in the future.

5: VaR Analysis

In its most general form, the Value at Risk measures the potential loss in value of a risky asset or

portfolio over a defined period for a given confidence interval. Thus, if the VaR on an asset is $ 100

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million at a one-week, 95% confidence level, there is a only a 5% chance that the value of the asset

will drop more than $ 100 million over any given week. In its adapted form, the measure is

sometimes defined more narrowly as the possible loss in value from “normal market risk” as

opposed to all risk, requiring that we draw distinctions between normal and abnormal risk as well

as between market and nonmarket risk.

Chapter 4

Analysis & Interpretation

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My effort in this project is to figure out how the bank will adopt the BASEL III Accord. Whether it

would be gradual or instant? Whether it would be easy or difficult? If difficult why? If easy, how?

If easy for how long the bank can sustain its position and function efficiently?

The ultimate value point however remains the only thing of value for banks that is the source of

funds, which ultimately affects the capital structure of the bank that is the target of the BASEL III

Accord. The move of the BASEL committee for strengthening the capital adequacy will in future

decide the sustainability of the banks. As discusses in Chapter 3, various methodologies have been

put to use for generating results for the answers above. Nevertheless the project tries to address the

macro elements of the bank in light of the various tests conducted. The elements are:

Leverage Ratio

Capital conservation buffer

Procyclicality of the provisioning norms

Interconnectedness

Too-big-to-fail problem

Reliance on external credit rating

.

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REGRESSION ANALYSIS

The regression needs to be on the kind of data that have affected the capital of the bank in last five

years. The capital must have changed owing to the differences in BASEL requirements over the

period of time. Hence, different metrics have been considered for regression analysis and the best

suited metrics has been regressed against the capital. Before hopping on to the regression part,

scatter plots have been figured out in order check the interdependence between the share capital and

affecting elements.

SCATTER PLOTS

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Share Capital 370.34 368.44 348.14 345.67 344.67

Loans And Advances 41,015.14 30,832.63 22,195.74 18,247.67 16,810.66


Share Capital 370.34 368.44 348.14 345.67 344.67

Investments. 21,566.81 17,121.44 12,512.66 9,110.18 9,141.99

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Share Capital 370.34 368.44 348.14 345.67 344.67

Cash And Bank 2,634.55 2,470.98 2,300.26 1,140.67 2,149.47

Share Capital 370.34 368.44 348.14 345.67 344.67

Capital Adequacy

Ratio 17.52 19.92 18.35 20.01 18.65

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Page | 56


REGRESSION ANALYSIS

Share Capital and Loans & Advances

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.94586145R Square 0.89465388Adjusted R Square -1.66666667Standard Error 4.79861792Observations 1

ANOVAdf SS MS F Significance F

Regression 5 586.6648782 117.333 25.47755492 #NUM!Residual 3 69.08020177 23.02673Total 8 655.74508

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 1.9216E-261 1.9216E-261X Variable 1 1.9216E-261 1.9216E-261X Variable 2 -1.1662E-291 1.1662E-291X Variable 3 -1.1662E-291 1.1662E-291X Variable 4 324.477297 6.501021946 49.91174 1.77107E-05 303.7881438 345.1665 303.7881438 345.1664503X Variable 5 0.00119962 0.000237665 5.04753 0.014998065 0.000443266 0.001956 0.000443266 0.00195598

RESIDUAL OUTPUT

Observation Predicted Y Residuals1 5920974.81 -5920604.465

Interpretation:

We see that there is Multiple R is 0.94 that shows there is a high correlation between the

share capital and the loans and advances.

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Also the R square stands to be 0.89 that implies that 89% of the variability in the loans and

advance is explained by the share capital, hence we can say that the loans and advances are

actually to a large extent dependent on the share capital and any downslide in the amount of

share capital will badly effect the loans and advances. As we see as per the BASEL III

Accord a high capital adequacy has to be maintained and the KMBL is supposed to do so

not only from the point of view of the regulatory compliance but also to support its viability.

Share Capital & Investments

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 5.937696424 5.937696424X Variable 1 0.000403746 0 65535 #NUM! 0.000403746 0.000403746 0.000403746 0.000403746X Variable 2 407 407X Variable 3 -1.0591E-292 1.0591E-292X Variable 4 323.9848679 5.937696424 54.5640674 1.35589E-05 305.0884679 342.8812679 305.0884679 342.8812679X Variable 5 0.002265352 0.000403746 5.610837103 0.011189927 0.000980453 0.003550251 0.000980453 0.003550251

RESIDUAL OUTPUT


Interpretation:

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Share Capital and Investments naturally have a lot of interdependence, expanding capital base leads

to more investments and thus more income and more expansion of capital and earnings. So,

basically it is a continuous cycle. As explained by the results of the regression analysis we see the

following relationship:

Multiple R is 0.95, signifying the presence of a high correlation between the elements.

The R square is again a large number i.e. 0.912, thus 91% of the variability in the

investments is explained by the share capital. In simple terms, the investments remain high

and continuous depending on the availability of the capital that can be invested.

Share Capital and Cash & Bank Balance

SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 0 0X Variable 1 5.7256E-283 5.9657E-283 0.959753413 0.407981113 -1.326E-282 2.4711E-282 -1.326E-282 2.4711E-282X Variable 2 -0 0X Variable 3 5.70455E+88 5.70455E+88X Variable 4 324.1636986 20.57467943 15.75546777 0.000555794 258.6858861 389.6415112 258.6858861 389.6415112X Variable 5 0.014626265 0.00934072 1.565860507 0.215356466 -0.015100076 0.044352606 -0.015100076 0.044352606

RESIDUAL OUTPUT


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Interpretation:

Multiple R does not imply a high correlation but it still is fair enough. At a bank it is really

important to maintain a high amount of cash because of its basic function. The relationship

between cash and share capital definitely shows that share capital is a significantly

important element for the cash that makes a bank fit for servicing its short term needs.

The R square explains clearly that although share capital is important for the cash but cash

is also dependent on other sources. From the point of view of BASEL III norms a high

capital adequacy will be required to be maintained at the bank’s end this will obviously need

more cash so cash needs to be generated rigorously. So, on one hand share capital is the

determinant of cash and on the other hand other sources of cash determine the adequacy of

capital.

Share Capital & CAR

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SUMMARY OUTPUT




Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 9.24023E+88 9.24023E+88X Variable 1 9.24125E+88 9.24125E+88X Variable 2 -5.133E-298 5.133E-298X Variable 3 5.70455E+88 5.70455E+88X Variable 4 403.0309743 128.3037855 3.141224344 0.051614563 -5.288933612 811.3508823 -5.288933612 811.3508823X Variable 5 -2.518738715 6.783525907 -0.371302292 0.735084577 -24.10694567 19.06946824 -24.10694567 19.06946824

RESIDUAL OUTPUT


Interpretation:

It is interesting to note that although the Tier 1 capital consists mainly of the share capital,

the degree of correlation as given by the Multiple R is really low.

Also, the R square is not really contributing to the explanation of the entire capital adequacy

variability.

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CAMEL ANALYSIS

CAMEL analysis is a more refined way of ratio analysis that is put to use for determining various

aspects of bank’s vulnerabilities and strengths. It has gained popularity in the recent times in the

wake of a need for better systems. The results of fundamental ratio analysis does not actually

fructify in terms of explaining the health of the financial systems. This is because of the fact that

CAMEL is a comprehensive framework of the types of ratios the bank needs to concentrate on for

the purpose of defining a better way of functioning as it segregates the stress areas as per the bank’

requirements.

I have tried to find out the CAMEL ratios for the Kotak Mahindra Bank and to determine what

aspects the bank lacks in that are actually needed for becoming a wholesome operationally

successful bank.

Due to radical changes in the banking sector in the recent years, the central banks of all the nations

around the world have improved their supervision quality and techniques. In evaluating the function

of the banks, many of the developed countries are now following uniform financial rating system

(CAMEL RATING) along with other existing procedures and techniques.

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Analysis:

CAPITAL ADEQUACY RATIOS

CAR 17.52 19.92 18.35 20.01 18.65

Interpretation:

Year

Bank's

CAR Loan Book Values Growth of the loan book Correlation

2008 17.52 20,017.35

2009 19.92 19,550.46 -0.023324266 -0.7365447

2010 18.35 28,426.38 0.454000571

2011 20.01 36,094.36 0.269748733

2012 18.65 46,517.28 0.288768661

So, we can see that the bank has been able to maintain a very high capital adequacy ratio way

higher than the BASEL norms. The implementation of the BASEL III Accord in a phased manner

was important because holding onto previous norms can actually dampen the chances of growing

for the banks in general mainly because the economies worldwide are recovering from the recession

and the gradual increase in CAR as imposed by the BASEL will restrain the banks from lending

(more lending on the same capital base means lower ratios). That could be threatening situation. In

case of the KMBL, CAR continues to be high that signifies that they will be actually able to comply

with the additional buffers requirements with an ease. However, the bank has been lending on the

high capital base for quite long now and the lending has shown wavy trends still has been growing.

The problem arises as soon as we move to the correlation between the loan book growth and the

CAR maintained by the bank. There is a negative correlation between both of these values. So, it is

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clear that the capital base which is actually very high is no where driving the loan book growth so

maintenance of high CAR even in future will not affect the loans and thus the earning.

Debt-Equity Ratio

Debt-Equity Ratio 6.908119026 5.997743433 6.613988793 5.517560997 5.99461281

Debt 55,132.04 40,984.92 30,026.98 21,549.00 21,542.90

Net Worth 7,980.76 6,833.39 4,539.92 3,905.53 3,593.71

Interpretation:

The debt to equity ratio is the financial metric used to assess a company's capital structure, or

"capital stack." Specifically, the ratio measures the relative proportions of the firm's assets that are

funded by debt or equity. The debt to equity ratio (also called the risk ratio or leverage ratio)

provides a quick tool for determining the amount of financial leverage a company is using, and thus

its exposure to interest rate increases or insolvency. It’s good to analyze the debt to equity ratio can

help you assess a company's financial health before investing. In case of the KMBL, the debt to

equity ratio is showing the following trend.

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The positive trend shows that the Debt-Equity ratio is likely to grow. This is a plus point for the

bank because this indicates the soundness of long-term financial policies of the company. It shows

the relation between the portion of assets provided by the stockholders and the portion of assets

provided by creditors.

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Advances to Assets Ratio

Advances to Assets Ratio 0.649870391 0.644787112 0.642109648 0.716873185 0.66877196


TOTAL ASSETS 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

Interpretation:

The bank has maintained a high advances to assets ratio for a long time but the following trend also

depicts an unfavourable position for long term.

The bank needs to increase its advances

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Total Investments to Total Assets Ratio

TI 21,566.81 17,121.44 12,512.66 9,110.18 9,141.99

TA 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

TI/TA 0.3417185 0.358051968 0.3619839 0.3579001 0.3636922

Interpretation:

The TI to TA ratio indicates the wellness of the investments in the terms of the assets backing the

same. The investments are backed by the assets in order to ensure the investments reap benefits

even in dire circumstances. When assets back the investments, it is not only good for the bank’s

profitability but also for the credit rating because the assets make it a creditworthy company.

Management Ratios

Total Advances to Total Deposits

Total Advances 41015.14 30832.63 22195.74 18247.67 16810.66Total Deposits 38537 29261 21819.2 13821.8 16423.6TA/TD 1.064305 1.053711 1.017257 1.320209 1.023567

Interpretation:

In case of the total advances to total deposits ratio, what is interesting to note is that how easily is

the bank able to churn the deposits into the operations. A high ratio indicates that the advances are

more than the deposits hence the bank is actually on a better side of net interest income. This is a

positive sign for the bank because the bank does not fall short of cash and thus sustains Tier 1

capital. The ratio also indicates the efficiency of the management decision.

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Earnings Ratio

Return on Net Worth

Profit 10850000000 8181800000 5361100000 276100000 293930000NW 79807600000 68333900000 4539920000 39055300000 35937100000RONW 0.135951964 0.119732666 1.18087984 0.007069463 0.008179013

Interpretation:

The profit as a proportion of the net worth determines the edge the bank has over other banks in

terms of profitability. It shows that the company is earning good profits with the money that the

shareholders have invested in. The relationship of this ratio with the capital structure is quite clear

and so is the need to study this ratio in context of macro prudential analysis. The reason attributed is

that source of funding and a strong capital base is ensured only if the shareholders reap benefits out

of the bank.

Liquidity ratios

G-Sec to Total Assets

Mar'12 Mar'11 Mar'10 Mar'09 Mar'08

G-Sec/TA 0.026 0.027 0.028 0.032 0.032

G-Sec 16,58.72 13,19.83 9,68.99 8,14.99 8,10.7

TA 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

Interpretation:

Investments in the government securities by the bank show how the banks try to cope up with the

horns of dilemma, i.e. maintaining liquidity as well as profitability. Government securities serve

dual purpose thus making the bank strong and resilient to capital changes.

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Liquid Assets to Total Deposits

LA/TD .1132669902 .1138157274 0.11226809 0.14027363 0.11544436

LA 43,649.70 33,303.62 24,496.00 19,388.34 18,960.12

TD 38537 29261 2,18,192 1,38,218 1,64,236

Interpretation:

The ratio of the liquid assets to the total deposits shows that there are enough liquid assets in order

to furnish the need of deposits by the bank. Also known as the total deposit adequacy ratio, it finds

its significance in measuring deposits match to investments and whether they could be converted

quickly to cover redemption. The higher the ratio the better for the bank. In case of KMBL, the

ratio is very high depicting presence of high liquidity in the assets and thus easy cash conversion.

Liquid Assets to Total Assets

LA/TA 0.691614062 0.696461669 0.70865481 0.76168525 0.7542831

LA 43,649.70 33,303.62 24,496.00 19,388.34 18,960.12

TA 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

Interpretation:

The presence of highly liquid assets in the total asset portfolio although is good for short run but

may pose a problem in the long run because the need for capital to be maintained under the BASEL

norms and for the bank in other assets will clash. This could be threatening and the bank needs to

cope up with it.

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ALTMAN Z-SCORE

Altman Z-Score is a gauge of a bank’s profitability and credibility. This was basically designed to

monitor any company’s work based on five key business ratios. The ratios are calculated for all the

elements of the financial statements that directly affect the functioning of the bank. Then they are

added up according to their weights. The weights assigned to all the elements by Ed Altman, as he

is popularly known, are given in the algorithm proposed by him.

This is an assessment of chances whether the company is going to be bankrupt and if yes how near

is the bankruptcy. Albeit at first these scores were only for manufacturing firms. But, in 2012, the

person who conceived this concept, Edward Altman proposed a model for banks as well. Banking

industry is the most vital component of the global financial markets hence this score finds a place in

the macro-prudential analysis. If the bank’s bankruptcy can be forecasted so the revival steps can be

taken to protect it before havoc breaks and then finding solutions. Statistically speaking it is a

linear, multi-factor model that finds out the scores for banks to decide whether the bank is nearing

bankruptcy or not. The interpretation of the scores is as:

Companies with Z-Scores above 3.1 are generally considered to be stable and healthy with

low probability of bankruptcy.

Scores that fall between 1.8 and 3.1 lie in a so-called 'grey area'. They need to cautious

while functioning normally.

Scores of less than 1 indicate the high probability of distress.

The following factors serve as the data for the calculation of the score.

First Factor

=

1.

2

*

(

Work

ing

Capit

al

/ T

o

t

a

l

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A

s

s

e

t

s

)

Second Factor =1.4 *

(Retained Earnings /Total Assets )

Third Factor =3.3 *

(EBITAD /Total Assets )

Fourth Factor =0.6 *

(Market Value of Equity / Total Liabilities )

Fifth Factor =0.99 *

(Revenue / Total Assets )

Z-Score = Sum of the five factors.

CALCULATION:

First Factor

WC 46,203.37

TA 63,112.80

WC/TA 0.732076061

1.2 * (WC/TA) 0.878491273

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Second Factor

RE 7,610.41

TA 63,112.80

RE/TA 0.120584255

1.4* (RE/TA) 0.168817958

Third Factor

EBITAD 1,740.50

TA 63,112.80

(EBITAD/TA) 0.027577607

3.3* (EBITAD/TA) 0.091006103

Fourth Factor

M.V of Equity 581.25

TL 2,553.67

M.V of Equity/TL 0.227613591

0.6* (Market Value of

Equity/TL) 0.136568155

Fifth Factor

Revenues 7,157.58

TA 2,573.67

Revenue/TA 2.71

0.99* (Revenue/TA) 2.6829

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Z-Score 4.028152

Hence, the Z-Score presents a rosy picture for the bank. The score stands high mainly due to the

fifth factor i.e. the Revenues to Total Assets which is also very high. This could be an implication

that the bank is earning enough revenues backed by assets. This indeed is a healthy number because

ultimately the bank knows that it has rich advances backing the revenues generated and that will

eventually create a strong balance sheet position.

STRESS TESTING

The next technique used for this project is ‘Stress testing’. As explained earlier, stress testing is a

tool to gauge the impact of stressors on the bank. A simulation technique used on asset and liability

portfolios to determine their reactions to different financial situations. These situations are various

economic scenarios which are termed by the IMF as “unlikely but plausible”. They mainly assess

credit risk, market risk and liquidity risk.

The concept of the same was bought by stork in frequent use after the banking systems worldwide

faced the victimization of the financial turmoil due to the contagion. The micro-level factors and

the internal strengths of even the most efficient banks could not restrain the effect.

A time had come when the banks realized the need for a macro-prudential framework of

sustainability rather a micro-level.

Stress Testing Analysis

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It is a powerful tool to equip the banks with the desirable capital and liquidity. There are basically

three types of capital and liquidity:

The capital the banks actually have.

The capital the banks need to have.

The capital the regulatory bodies think the banks must have.

Being studied for multiple factors, it is a sophisticated approach for figuring out liquidity

requirements. The following figure clearly explains the comprehensive framework of the stress

testing analysis.

Key challenges for the stress testing.

Reducing down the complexity of the financial systems worldwide.

It’s a difficult method as it seeks to develop a firm wide stressor assessment

It can hamper the banks planning process.

Although an intellectual and technical resource, the contingency plans furnished by it cannot

be adhered to for long.

Recipients

Despite shortcomings, stress testing gives its recipients a reliable level of capital adequacy. The

robustness of the results although depend upon the applicability of the same.

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Stress testing needs to form an integral part of the governance and risk management process.

Board and senior management are to be the ultimate owners of the stress testing program in

the organization. Onus is on the senior management to use stress test results in operational

and strategic decision making.

Stress testing needs to complement other risk management tools and models in identifying

and managing risk across the institution. It should be rigorous and be able to identify

scenarios that could have an adverse impact.

In addition to other requirements, stress testing has been made the central tool for

identifying and controlling liquidity risk of the organization. Liquidity risk scenarios need to

cover both bank-specific and market-wide.

Use of multiple techniques and processes for stress testing. These techniques range from use

of deterministic parameter based stress tests to much more complex and evolved scenario

models that use advanced statistical methods in estimating the impact of stress tests. Stress

testing processes also need to factor the inter-related impact of a shock across risk

categories.

Institutions need to have a robust stress testing framework which is flexible and scalable to

address current and future requirements.

Stress testing program should deliver a comprehensive assessment of enterprise-wide risk.

This involves stress testing multiple measures across assets, liabilities, income and capital of

the organization. This also includes use of multiple scenarios, of varying levels of severity,

in assessing the vulnerability of the institution.

And finally, BIS requires banks to adopt reverse stress testing to identify scenarios that will

cause maximum impact.

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Application

A fundamental way of applying stress testing is assessing various scenarios through ‘Scenario

Analysis’. I have already calculated Altman Z-Score for the bank and next what I am going to do is

to reframe the ratio and check its effects on the Z-Score. The ratios will be put to extremities for

evaluating the score and then studying the effects of the same. Although, the Z-Score does not

directly affect the capital structure of the bank it certainly assures if the bank is self-sustaining or

not. A bank that is self-sustaining can actually cope-up with the BASEL requirements of capital

because it holds strength and confidence of the investors and in the long run if it falls short of

capital, the government is present there to aid knowing that the bank is trustable and can recover.

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Following is the current scenario, the true position of the bank’s balance sheet is used and then Z-Score has

been found out.

Particulars Value(cr) Ratios

WC 46,203.37 WC/TA

TA 63,112.80 0.732076061

RE 74,610.41 RE/TA

TA 63,112.80 1.182175565

EBITAD 1,740.50 EBITAD/TA

TA 63,112.80 0.027577607

M.V of

Equity 581.25 MV/TL

TL 2,553.67 0.227613591

Revenues 7,157.58 Revenue/Tad

TA 2,573.67 2.781079159

Z-Score 5.51438

The ratio shows that the bank is nowhere heading to bankruptcy.

Next I have assumed that the bank grows positively next year and all the factors grow too. But, the

real fundamental of stress testing would be to derive a scenario where the Z-Score decreases at the

gray area. This will give the actual valuation of the bank at which it needs to be cautious. Following

is the summary of scenarios where a nominal change has been used in the ratios and still the change

is noticeable.

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Scenario Summary

Current Values:

Change in Z-Score due to changing values

in factors

Changing Z

score

Changing Z

score with

increase in

values of

factors

Changing

Cells:

$F$3 46,203.37 46,003.37 46,003.37 48,203.37

$F$4 63,112.80 60,112.80 60,112.80 65,112.80

$F$5 74,610.41 7,410.41 74,410.41 74,810.41

$F$6 63,112.80 60,112.80 60,112.80 65,112.80

$F$7 1,740.50 1,750.50 1,750.50 1,730.50

$F$8 63,112.80 60,112.80 60,112.80 65,112.80

$F$9 581.25 571.25 571.25 591.25

$F$10 2,553.67 2,533.67 2,533.67 2,573.67

$F$11 7,157.58 7,057.58 7,150.58 7,197.58

$F$12 2,573.67 2,553.67 2,553.67 2,593.67

Result Cells:

$F$14 5.51438 4.0583648 5.654818574 5.469724237

Next I am going to calculate the values of the factor element when the Z-Score lies in the gray area

because obviously with the implementation of the BASEL norms bank is going to be under

pressure at some point of time if not the near future so keeping current values in mind we derive a

scenario of constrained elements.

Following is the table that shows a great amount of change in all the factor ratios.

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Particulars Value(cr) Ratios

WC 46203.37

TA 126225.6 0.366038

RE 74,610.41

TA 126225.6 0.591088

EBITAD 1,700.00

TA 126225.6 0.013468

M.V of

Equity 560

TL 10214.68 0.054823

Revenues 7,157.58

TA 10294.68 0.69527

Z-Score 2.032424 12

Dodd-Frank Stress Testing

1

? The change in the Z-Score has been calculated as per the bank’s presumption.2 The changes in the values of the factors used for calculation is provided by the bank

2

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With the advent of the global crisis, US firms have shifted their emphasis a lot from internal shock

preventions to external shock absorbents. The Federal Reserve Board launched a scheme called

Comprehensive Capital Analysis and Review that applies to 18 banks and it requires them to

submit a capital plan that includes projections related to the following :

Pre-provision net income

Losses

Net income before taxes

Pro-forma Capital ratios over a nine-quarter horizon

Banks with significant trading activities are also required to apply hypothetical Global Market

Shock to trading, counter party and fair value loan exposures.

The stress testing done is under mandatory supervisory set of adverse scenarios as given by the

FRB. The banks are required to formulate stable capital actions excluding any planned capital

actions.

In this project with the help of the figures given by bank I have tried to incorporate the same for

risk management at the macro level.

This perhaps is the best stress testing technique used by any bank so far. The best part about the

testing is that it links the income statement items with the balance sheet items and thus project the

capital requirements.

Description of risk types:

Market Risk: Arising from counter party credit exposure from capital market activities.3

Credit Risk: Arising from defaults (NPAs and Available for Sale and Held to Maturity

securities)

Operational Risk: Litigation, reputation and franchise risks.

3 As assumed for Kotak Mahindra Bank

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Analysis:

To project the capital position, a hypothetical stress scenario4 has been taken that precedes the

calculation of PPNR5 and stress losses that eventually determines the capital position.

Calculation of change in Regulatory Capital

The first step is the calculation of the PPNR, the following excel sheet shows the calculation.

4 As adapted from the CCAR-Dodd-Frank Stress Testing of Bank of America.5 Pre-provision net revenues

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Calculation of PPNR

Net Interest Income 2,811.47

Non-Interest Income 1,345.65

Non-Interest Expenses 1683.59

PPNR

2,473.53

Next, we arrive at calculation of Pre-tax Net Income (PNI) and After Tax Net Income (ANI). The

net income has been calculated supposing trading and counter party losses and loan losses for the

projections at 15% change at the end of the FY 20146.

Calculation of After Tax Net Income

PNI -1,058.93

Taxes 248.92

6 As per the baseline scenario projections of the bank

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Calculation of Pre-tax Net Income

PPNR 2,473.53

Other Revenue

Provisions 43

Loan Losses 3489.459

Trading and Counterparty Losses

Other Losses

PNI -1,058.93


Extraordinary items

ANI -1,307.85

Next step is to find the change in capital and regulatory capital.

Calculation of Change in Equity

Capital

ANI -1307.85

Distributions and other significant

deductions 359.05

Change in Capital -1666.9

Calculation of Change in Regulatory

Capital

Change in Capital -1666.9

deductions from regulatory capital 300.04

Other additions

Change in Regulatory Capital -1966.94

So, a loss from the regulatory capital maintained by the bank is likely to take place. In this scenario,

the bank needs additional capital to meet the BASEL requirements.

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At present the bank maintains a CAR of 15.6 % way above the requirements of the BASEL III

norms but for coping up with the implementation phase it needs to stay steady. So, next projections7

related to the Basel III requirements which acknowledge the time frame in which the bank prepares

to fully adapt to the BASEL III Accords. Although Kotak Mahindra Bank has sufficient capital for

the implementation phase8 as depicted in the following table still the stress testing gives a clear picture.

BASEL III

REQUIREMENTS

AS ON Tier 1 Tier 2

Common

equity

Requirements Buffers

Leverage

Ratio

Q4 2014-15 6 1 4.5 2.5 3

Capital Adequacy of KMBL.

Total Capital funds of the bank 9,418.38




7 The projections are based on the change in the regulatory capital as calculated above.8 Implementation is in a phased manner so capital levels will change as per the requirements over a period of time

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The total Capital Adequacy is at 15.63% i.e. 9418.38crores in terms of value. Now if this figure

sustains the additional capital figure comes to be 74519 and the regulatory requirements sum up to

17. So, we can say that the bank needs to address the capital adequacy needs because the stress

scenarios keep a large sum into loss. Even if the bank has excess capital, if losses on account of

loans start taking place, the excess capital cannot sustain the bank for long.

GAP Analysis

9 Deduct the change in regulatory capital from the capital adequacy.

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Market risk—the risk that a sudden change in market prices could affect earnings or capital—can

be difficult to measure, but that doesn’t make it any less important. One way of capturing a bank’s

exposure to changing interest rates is Gap Analysis. Although not as sophisticated as some other

tools for measuring interest rate risk, Gap allows you to get a quick and intuitive sense of how a

bank is positioned by comparing the values of the assets and liabilities that roll over—or re-price—

at various time periods in the future.

Although the simplicity of the Gap methodology makes it an attractive tool for measuring interest

rate risk, users of Gap need to be aware of its weaknesses and limitations. While Gap is a good

measure of re-pricing risk, it is not able to measure interest rate risk stemming from options risk,

basis risk or yield curve risk.

By monitoring the differences in the maturity and re-pricing of the earning assets and liabilities we

are able to derive a model that measures the gap and eventually helps in deciding how is the net

interest income earned is affected.

The GAP between assets and liabilities could be of either way negatively positioned or positively

positioned.

Negatively positioned GAP is when rate sensitive liabilities exceed the rate sensitive assets.

Positively placed GAP is just the reverse of above.

Following is the gap analysis of the earning assets and liabilities of the bank. The earning assets and

liabilities ratio as well as cumulative ratios have been formulated along with the GAP and earning

assets ratios.

Page | 86


GAP Analysis (crores)0-3 months 3-6 months 6-12 months 1-2 years 2-5 years Over 5 years Total

Earning AssetsFOREX Assets 266.05 393.58 14.51 21.33 6.93 51.05 753.45Securities 1,881.03 1,837.00 2,076.73 4,085.48 704.47 992.79 11,577.50Mortgages 1,052.08 4,630.77 5,682.85Commercial Loans 5,126.39 5,126.39Other loans 3,121.28 3,065.17 5,208.69 14,669.15 3,535.55 5,907.26 35,507.10Total Earning Assets 5268.36 5295.75 7299.93 18775.96 5299.03 16708.26 58647.29Interest-Bearing LiabilitiesDeposits 6,029.40 4,379.45 5,397.51 12,826.13 1,285.82 230.73 30,149.04FOREX Liabilities 1,093.19 560.34 1,058.75 676.94 2.24 229.33 3,620.79Other liabilities 1,649.48 1,203.14 2,196.73 2,034.08 898 630.63 8,612.06Total IB Liabilities 8,772.07 6,142.93 8,652.99 15,537.15 2,186.06 1,090.69 42,381.89Gap MeasuresInterval Gap -3,503.71 -847.18 -1,353.06 3,238.81 3,112.97 15,617.57 16,265.40Cumulative Gap -3,503.71 -4,350.89 -5,703.95 -2,465.14 647.83 16,265.40 32,530.80RSA / RSL 0.60058344 0.86208861 0.84363093 1.208456 2.424009 15.31898156 1.383782Gap / Earning assets (%) -0.6650476 -0.8215815 -0.78137051 -0.13129 0.122254 0.973494547 0.554685

Interpretation:

The GAP analysis has following implications.

The rate sensitive liabilities are more in the short term so the bank needs to bring down the

RSL or concentrate on increasing the RSA for short term period otherwise it may fall into

the pit of paying up liabilities even when the assets are not backing it up.

Page | 87


VaR Analysis

Now that the bank has been analysed on various parameters regarding the absorption of extra

burden by BASEL III, one more question that is for sure needs to be answered is the investor

confidence defined in terms of the returns that they have gained and whether or not the trading of

the KMBL stock will continue to yield better returns. This question finds itself suitable on the

capital front because the trading frequency and the riding demands for the stock definitely raises the

stock value and thus makes capitalization a more favorable task.

The VaR analysis is a metrics that will justify investors’ confidence in the stock. Given the results

of previous methods of studying the banks position, VaR will only complement the main tools of

the study of this project.

VaR in its simplest form is defined as the maximum loss that an investor can suffer. In more

statistical terms, VaR measures what is the most that an investor might lose, based on a specific

level of confidence, over a specific period of time.

It is important because most risk measurements focus on volatility whereas VAR focuses

specifically on losses. It is commonly used to evaluate risk across a portfolio, but can also be

applied to single indexes or anything that trades like a stock. VAR is important because it provides

financial executives with a method of quantifying risk that is rigorous but also easily understood by

nonfinancial executives.

Various methods are adopted for calculation of VaR, viz

Historical Simulation

Variance –Covariance Method

Monte-Carlo Simulation

Page | 88


Historical Simulation:

This method employs historical returns data to assemble the cumulative distribution function, and

does not place any assumptions on the shape of the distribution.

A historical simulation simply sorts the returns by size. If the sample includes 100 returns, the

value at risk at a confidence of 95% is the fifth largest loss.

Several criticisms are often made of this approach.

Historical simulation assumes that returns are independent and identically distributed. This

not necessarily the case; real-world data often displays volatility clustering.

Returns in the recent-past and far-past are given equal weighting. However, recent returns

have greater bearing on future behaviour than older returns.

This method requires a large set of historical data for accuracy; this, however, is not always

available.

Because the method is entirely reliant on historical data, the result cannot be influenced by

subjective information (as with Monte-Carlo simulation). This may be significant if a fund

manager predicts large changes in the business environment.

Variance –Covariance Method

This approach for calculating the value at risk is also known as the delta-normal method. It needs

the average returns, variances and correlation coefficients (derived from historical data). The

variance-covariance method assumes that historical returns are normally distributed, and that the

future will mirror the past.

The calculation is straightforward, and for a one-asset portfolio is given by this equation.

Page | 89


xα is the αth percentile of a normal distribution, and P is the portfolio value. xα is calculated with

Excel’s NORM.S.INV() function.

Monte-Carlo Simulation

The Monte-Carlo Method involves running multiple trials to calculate the portfolio returns.

Generally, this method involves the following steps.

Generate simulated returns by sampling a probability distribution.

Order the returns by size

Compute the VaR at the required confidence level. For a simulation of 1000 returns, the

95% percentile would correspond to the 50th lowest value.

Monte-Carlo simulation is an extremely flexible method for calculating Value at Risk. This is

because any probability distribution can be selected for all the significant risk factors. However, for

a large investment universe, Monte-Carlo simulation can be computationally intensive.

ANALYSIS

Page | 90


Historical Simulation

The chart above shows the first step of the Historical Simulation. A chart plot of the daily returns is

generated and then the DPR is sorted into bins and a histogram is generated. The histogram for this

set of data stands to be as below.

Page | 91


The histogram clearly depicts that the returns over a last hundred days have been in the range of

somewhere around -0.1 to around 3.5%, so on the basis of this histogram if one needs to calculate

VaR, one must proceed as:

Say I need to calculate VaR at a 99% confidence level

I look up at the figure above and try to see where my 1% chance of losing remains. That is

the point to value VaR

I then try to find out the date on the basis of the daily price range(DPR)

I then calculate the VaR using the following DPR table

Date Close DPR

01-04-2013 651.2 -0.00031

28-03-2013 651.4 0.01789

26-03-2013 639.85 0.012661

25-03-2013 631.8 -0.01313

22-03-2013 640.15 0.013446

21-03-2013 631.6 -0.00371

20-03-2013 633.95 -0.00692

19-03-2013 638.35 -0.02468

18-03-2013 654.3 -0.01494

15-03-2013 664.15 0.009531

14-03-2013 657.85 0.01262

13-03-2013 649.6 -0.02658

12-03-2013 667.1 -0.00037

11-03-2013 667.35 -0.00865

08-03-2013 673.15 0.02718

07-03-2013 655.1 0.003058

06-03-2013 653.1 -0.0039

05-03-2013 655.65 0.00405

Page | 92


04-03-2013 653 -0.00054

01-03-2013 653.35 -0.00664

28-02-2013 657.7 0.005412

27-02-2013 654.15 -0.00868

26-02-2013 659.85 0.000758

25-02-2013 659.35 -0.00816

22-02-2013 664.75 0.00702

21-02-2013 660.1 -0.01205

20-02-2013 668.1 -0.00835

19-02-2013 673.7 0.013374

18-02-2013 664.75 -0.00794

15-02-2013 670.05 0.004487

14-02-2013 667.05 -0.0003

13-02-2013 667.25 -0.00575

12-02-2013 671.1 -0.00074

11-02-2013 671.6 -0.01096

08-02-2013 679 -0.01193

07-02-2013 687.15 -0.00797

06-02-2013 692.65 0.021156

05-02-2013 678.15 0.006064

04-02-2013 674.05 0.002674

01-02-2013 672.25 -0.00844

31-01-2013 677.95 0.002363

30-01-2013 676.35 0.005709

29-01-2013 672.5 0.006714

28-01-2013 668 0.004727

25-01-2013 664.85 0.018904

24-01-2013 652.4 0.012959

23-01-2013 644 0.005372

Page | 93


22-01-2013 640.55 0.018116

21-01-2013 629.05 0.003025

18-01-2013 627.15 -0.00175

17-01-2013 628.25 0.006628

16-01-2013 624.1 -0.00909

15-01-2013 629.8 0.00151

14-01-2013 628.85 -0.00246

11-01-2013 630.4 -0.03553

10-01-2013 653.2 0.007144

09-01-2013 648.55 -0.00538

08-01-2013 652.05 0.004843

07-01-2013 648.9 -0.00139

04-01-2013 649.8 -0.0082

03-01-2013 655.15 -0.00213

02-01-2013 656.55 0.010796

01-01-2013 649.5 -0.00015

31-12-2012 649.6 -0.00238

28-12-2012 651.15 0.002768

27-12-2012 649.35 7.7E-05

26-12-2012 649.3 0.005482

24-12-2012 645.75 -0.00548

21-12-2012 649.3 -0.01756

20-12-2012 660.8 -0.01024

19-12-2012 667.6 0.007518

18-12-2012 662.6 0.002493

17-12-2012 660.95 -0.0043

14-12-2012 663.8 0.01717

13-12-2012 652.5 -0.0112

12-12-2012 659.85 -0.0095

Page | 94


11-12-2012 666.15 -0.00964

10-12-2012 672.6 0.013321

07-12-2012 663.7 -0.00541

06-12-2012 667.3 0.004581

05-12-2012 664.25 -0.01123

04-12-2012 671.75 0.009573

03-12-2012 665.35 -0.00502

30-11-2012 668.7 0.030442

29-11-2012 648.65 0.021505

27-11-2012 634.85 0.014038

26-11-2012 626 0.00304

23-11-2012 624.1 -0.00663

22-11-2012 628.25 -0.00095

21-11-2012 628.85 0.012159

20-11-2012 621.25 -0.00217

19-11-2012 622.6 0.002412

16-11-2012 621.1 -0.02473

15-11-2012 636.65 0.02957

13-11-2012 618.1 -0.00741

12-11-2012 622.7 0.002492

09-11-2012 621.15 -0.00818

08-11-2012 626.25 -0.00271

07-11-2012 627.95 0.002791

06-11-2012 626.2 0.002638

05-11-2012 624.55 0.019238

02-11-2012 612.65 0.00049

01-11-2012 612.35

Page | 95


Now if I have to find out VaR at 99% confidence level I can simply look up at the above table and

find the corresponding share value which in this case is 8 th of Feb’ 2013 where a loss of INR

8.19% was incurred with return falling from INR 687.19 to 679.

So, one can notice that the Historical Simulation Technique actually return results as per the

confidence level a person puts in. The data simulated here is only for 100 days. However, the actual

simulation can be ‘n’ no. of days depending upon the requirement of the investors. Not deviating

from my topic I would like to make a mention of why this is important for macro-prudential

analysis.

VaR in this case has shown that the variability of returns is almost negligible. The analysis shows

that investors don’t have to lose much if they choose KMBL for parking in their investments. This

is strength for the bank because it will definitely help retain investors and would add up to the

capital of the bank.

Estimation of VaR using Monte Carlo Simulation method

Using Monte Carlo simulation technique a 10 period simulated prices were estimated and the prices

henceforth have been used to generate the VaR for a confidence level of 90%.

Page | 96


The following steps have been undertaken:

Step 1: With the help of the historical prices, log normal returns have been calculated and have

been used for generating simulated random prices.

Step 2: The Monte Carlo Simulation method of VaR begins by using a random number generating

function with a seed of number to create a simulated independently distributed random number

matrix. These simulated random scenarios have been used to simulate the risk factors for each

position. In the case of simple bank equity, the specified closing price from the time series is the

only risk factor for that position.

Thereafter a large no of random numbers have been generated.

1 2 3 4 5 6 7 8 9 10

1 0.51

3214

0.29

845

0.15

0538

0.49

7658

0.82

6538

0.27

8857

0.41

6436

0.90

3185

0.71

8575

0.93

3971

2 0.04

3111

0.35

1167

0.29

124

0.90

6489

0.50

7336

0.20

2222

0.69

4571

0.74

1081

0.41

2291

0.85

1298

3 0.33

0677

0.07

2253

0.36

3545

0.02

9383

0.09

5824

0.90

9255

0.66

5873

0.30

316

0.40

504

0.13

4449

4 0.56

5937

0.45

4353

0.65

8814

0.57

3819

0.77

626

0.61

8271

0.52

0215

0.04

6999

0.01

1475

0.98

5815

5 0.36

7624

0.30

2272

0.67

9317

0.75

5227

0.79

4063

0.59

7722

0.62

1115

0.81

6831

0.30

3343

0.65

8824

6 0.75

1274

0.20

3842

0.88

1007

0.92

4754

0.69

0363

0.00

6437

0.99

4826

0.62

5917

0.62

7312

0.12

6804

7 0.40

1375

0.30

7954

0.00

0674

0.44

9826

0.39

7771

0.79

6033

0.87

089

0.05

4976

0.00

2341

0.25

455

8 0.40 0.29 0.68 0.42 0.77 0.68 0.55 0.33 0.38 0.45

Page | 97


8556 4591 1362 2289 1953 6091 5079 5086 6502 8454

9 0.34

9925

0.18

3367

0.09

0318

0.52

6058

0.11

4005

0.37

446

0.69

0775

0.95

7938

0.97

1718

0.45

3566

10 0.00

4431

0.86

6483

0.76

6041

0.81

7699

0.42

1965

0.61

3281

0.41

3225

0.54

957

0.27

2727

0.39

5027

11 0.06

0036

0.94

8309

0.40

8724

0.99

6747

0.34

1862

0.75

0325

0.48

0537

0.63

8302

0.32

9823

0.28

8434

12 0.24

5354

0.40

0998

0.08

2506

0.83

1334

0.31

2634

0.56

2589

0.07

0184

0.31

0771

0.14

0583

0.71

7945

13 0.10

6376

0.23

2417

0.32

3906

0.22

5762

0.44

5634

0.19

6657

0.98

7313

0.05

7966

0.60

4091

0.30

8449

14 0.23

607

0.43

8724

0.06

0431

0.82

3968

0.67

6158

0.30

0874

0.02

0148

0.23

932

0.89

1458

0.29

6985

15 0.41

4212

0.52

4062

0.64

6767

0.50

7272

0.37

6339

0.53

9809

0.49

4939

0.59

4902

0.92

2768

0.65

3484

16 0.66

6452

0.61

8159

0.39

0733

0.34

1014

0.90

1585

0.73

3654

0.93

0664

0.52

284

0.49

5532

0.34

3922

17 0.84

0835

0.33

8013

0.13

5629

0.80

1344

0.16

551

0.63

8833

0.10

0007

0.44

3909

0.68

85

0.84

4546

18 0.36

3039

0.50

7326

0.79

454

0.88

6021

0.12

8427

0.63

1936

0.48

0945

0.01

7361

0.55

5071

0.62

401

19 0.59

4386

0.34

1718

0.41

8796

0.97

0911

0.93

664

0.59

4422

0.15

2701

0.02

2332

0.61

8633

0.93

9031

20 0.06

4813

0.84

2181

0.46

9031

0.52

5408

0.51

67

0.21

6143

0.99

3786

0.43

2071

0.71

966

0.34

4303

21 0.30

4966

0.47

5724

0.06

6465

0.07

894

0.32

1101

0.67

6063

0.35

5895

0.35

6478

0.62

8569

0.95

4307

22 0.24 0.17 0.63 0.74 0.96 0.91 0.28 0.92 0.17 0.84

Page | 98


6725 406 3952 5744 3628 4192 9049 3013 0852 8569

23 0.02

2197

0.99

4065

0.23

4784

0.28

0811

0.14

0109

0.90

1018

0.57

3414

0.24

334

0.22

9273

0.28

6231

24 0.52

1743

0.27

683

0.39

1034

0.84

4701

0.21

6471

0.38

9539

0.36

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0236

0.84

45

25 0.32

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3154

0.95

3184

0.97

5884

0.89

9402

0.04

2077

0.87

0159

0.61

335

0.09

3192

26 0.62

523

0.20

6959

0.35

2515

0.00

8619

0.73

2629

0.02

5342

0.20

0396

0.10

1769

0.06

9431

0.64

4231

27 0.83

1771

0.13

1786

0.73

7129

0.05

2046

0.49

7083

0.06

713

0.24

5057

0.81

9366

0.54

115

0.16

4495

28 0.06

8293

0.81

4764

0.60

4678

0.85

2387

0.21

398

0.44

1788

0.54

6904

0.92

6967

0.27

6496

0.56

271

29 0.20

9849

0.77

7453

0.87

7565

0.15

2389

0.50

2375

0.88

0322

0.26

1913

0.25

0672

0.13

7952

0.00

6978

30 0.22

4155

0.41

7911

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5776

0.66

1976

0.95

1652

0.22

3777

0.66

344

0.84

1771

0.25

2929

0.67

8345

31 0.15

8981

0.02

0245

0.53

9639

0.24

7749

0.28

7646

0.69

621

0.07

7529

0.18

4537

0.73

6939

0.18

4334

32 0.91

5188

0.68

8074

0.18

238

0.93

7799

0.56

9759

0.42

7478

0.85

4269

0.56

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0.78

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0.85

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33 0.75

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0.36

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0.78

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0.91

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0.04

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0.08

5645

0.37

6665

0.50

4252

34 0.74

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7165

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0.88

7266

0.39

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0.50

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0.52

294

0.97

1263

0.33

9883

0.34

4872

36 0.44 0.59 0.05 0.07 0.73 0.39 0.18 0.31 0.47 0.14

Page | 99


6401 3242 3046 6021 0651 1904 0602 4171 7694 2249

37 0.10

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0.46

6983

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1604

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0.99

3194

0.08

3563

0.36

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0.77

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0026

38 0.12

3112

0.01

9307

0.97

4308

0.18

2338

0.31

1931

0.76

02

0.75

0159

0.15

612

0.59

8535

0.04

676

39 0.10

3583

0.05

0007

0.60

309

0.38

6215

0.73

8505

0.52

0083

0.06

3908

0.99

056

0.75

5697

0.23

9645

40 0.40

4437

0.37

7755

0.93

0195

0.04

2958

0.96

1798

0.44

0585

0.98

4194

0.70

4396

0.11

4567

0.31

049

41 0.56

84

0.66

5242

0.32

1262

0.44

7112

0.37

3162

0.16

3856

0.02

2581

0.13

6031

0.44

5994

0.33

8355

42 0.00

9607

0.47

9562

0.35

0099

0.17

6846

0.41

651

0.75

6433

0.15

5016

0.45

9219

0.35

4165

0.57

3568

43 0.61

1584

0.48

863

0.76

6984

0.35

1556

0.36

3494

0.23

3324

0.49

3006

0.39

0015

0.04

3192

0.18

7709

44 0.58

4725

0.07

5753

0.33

4029

0.90

555

0.15

3887

0.06

5469

0.67

4158

0.33

7533

0.44

8047

0.04

991

45 0.25

5641

0.55

4924

0.64

3467

0.90

1634

0.88

9128

0.68

0257

0.12

8635

0.53

8819

0.28

8244

0.63

0463

46 0.48

049

0.85

9172

0.12

9854

0.28

1106

0.34

9439

0.55

2878

0.52

2262

0.58

8111

0.15

9465

0.52

9622

47 0.35

3045

0.92

9685

0.15

122

0.82

186

0.48

2269

0.26

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0.85

3114

0.33

327

0.13

6047

0.38

5425

48 0.28

6429

0.69

7422

0.89

9232

0.89

6988

0.97

1481

0.08

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0.51

8663

0.97

0581

0.02

4807

0.37

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49 0.57

1936

0.32

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0.24

4237

0.46

853

0.48

3129

0.25

2596

0.99

8434

0.77

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0.10

4762

0.99

4911

50 0.44 0.52 0.13 0.09 0.23 0.77 0.96 0.00 0.79 0.84

Page | 100


6143 8282 5622 0681 0881 3354 5071 5134 3149 0175

51 0.88

808

0.77

9903

0.45

6011

0.20

8868

0.90

6195

0.19

5738

0.16

3669

0.96

5847

0.02

205

0.05

1455

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8 5677 6549 7572 8322 1894 6587 9025 0494 9634 6263

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Page | 119


Step 3: Next I have used the random variables to simulate the returns of risk factors. I have done

this by creating a matrix of random variables and multiplying it by the vector of returns of the risk

factor.

Assuming an analysis date of 28th March, using the matrix of new simulated returns of the risk

factor, I have taken the level of the risk factor (equity value) on the 28 th March in this case, our

analysis date, being equal to INR 651.4 and multiplying this by each simulated return. This

effectively means producing 10 new values for the price.

1 2 3 4 5 6 7 8 9 10

-0.188 -0.1 -0.15 -0.25

-

0.32

-

0.29 -0.28

-

0.33 -0.2

-

0.15

1 0.0002

2E-

04

2E-

04

1E-

04 0 0

2E-

05 0 0 0

2 -0.008 -0.02 -0 -0.01

-

0.01 -0 -0.02

-

0.01 -0

-

0.01

3 -0.004 -0.01 -0.01 -0.01

-

0.01

-

0.01 -0

-

0.01 -0

-

0.01

4 0.0114 0.004 0.01 0.005 0 0.01 0.007 0.01 0 0.01

5 -0.013 -0.01 -0.01 -0

-

0.01

-

0.01 -0

-

0.01 -0

-

0.01

6 0.0029

3E-

04 0.002 0.002 0 0 0.002 0 0 0

7 0.0014

3E-

04 0.006

1E-

04 0 0.01 0.002 0 0.01 0

8 0.0137 0.011 0.004 0.014 0.02 0.02 0.006 0.02 0.02 0.01

9 0.0058 0.006 0.002 0.004 0.01 0 0.014 0 0.01 0.01

Page | 120


10 -0.009 -0 -0.01 -0.01 -0 -0 -0

-

0.01 -0 -0

11 -0.012 -0.01 -0 -0

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0.01

-

0.01 -0.01

-

0.01 -0 -0

12 0.0183 0.018 0.007 0.024 0 0 0.013 0.02 0 0.02

13 0.0001

1E-

04

3E-

04

6E-

06 0 0

1E-

04 0 0 0

14 0.0082 0.005 0.004 0.001 0 0 0.008 0.01 0 0

15 -0.007 -0.02 -0.01 -0.02

-

0.02

-

0.01 -0.01

-

0.02 -0

-

0.01

16 -3E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

17 0.0007 0.003 0.004

6E-

04 0 0 0.004 0 0 0

18 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

19 0.0004

5E-

04

2E-

04

2E-

04 0 0

4E-

05 0 0 0

20 0.0041 0.002 0.003 0.004 0 0 0.004 0 0 0

21 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

22 0.0002 0.002 0.004 0.001 0.01 0.01 0.002 0 0 0.01

23 -2E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

24 0.0059 0.001

9E-

04 0.005 0.01 0 0.006 0 0 0

25 -0.004 -0 -0 -0

-

0.01

-

0.01 -0.01 -0 -0 -0

26 0.0111 0.009

2E-

04 0.004 0.01 0.01 0.01 0 0.01 0.01

27 0.0034 0.006 0.003 0.008 0.01 0 0.003 0.01 0 0.01

28 -0.013 -0.01 -0.01 -0

-

0.01 -0 -0

-

0.01 -0 -0

Page | 121


29 0.0058 0.006 0.004 0.003 0.01 0 0.003 0.01 0 0

30 -3E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

31 0.0001

2E-

04

6E-

05

2E-

05 0 0

1E-

04 0 0 0

32 0.0023 0.004 0.001

3E-

04 0 0

3E-

04 0 0 0

33 0.0006

2E-

04

7E-

04

3E-

04 0 0

5E-

04 0 0 0

34 0.0036 0.005 0.007 0.002 0.01 0.01 0.005 0 0.01 0

35 0.0116 0.006 0.007

9E-

04 0.01 0.01 0.011 0 0.01 0.01

36 0.0079 0.006 0.003 0.006 0 0

8E-

04 0.01 0 0.01

37 -0.02 -0.02 -0.01 -0.02

-

0.01

-

0.01 -0.01

-

0.02 -0

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0.01

38 -0.006 -0 -0 -0 -0 -0 -0 -0 -0 -0

39 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

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42 -0.004 -0 -0 -0 -0 -0 -0.01 -0 -0 -0

43 -0.006 -0.01 -0 -0 -0 -0 -0 -0 -0 -0

44 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

45 -0.016 -0.01 -0.01 -0.01

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0.01 -0.02 -0 -0

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0.01

46 -5E-04 -0 -0.01 -0 -0

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0.01 -0 -0

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0.01

48 -0.014 -0.01 -0 -0.01 -0 -0 -0.01 - -0 -

Page | 122


0.01 0.01

49 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

50 0.0007 0.001

5E-

04

8E-

04 0 0

1E-

03 0 0 0

51 -0.003 -0 -0 -0.01 -0 -0 -0 -0 -0 -0

52 0.0042 0.003 0.001 0.009 0 0

3E-

04 0.01 0 0

53 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

54 0.0014 0.001

8E-

04 0.002 0 0 0.002 0 0 0

55 0.0148 0.015 0.022 0.017 0 0 0.032 0.02 0.03 0

56 -0.004 -0 -0 -0 -0

-

0.01 -0

-

0.01 -0 -0

57 0.0019 0.004

3E-

05 0.003 0 0

5E-

04 0 0 0

58 -4E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

59 0.0012

6E-

04

2E-

04

6E-

04 0 0

8E-

04 0 0 0

60 0.0006 0.007 0.005 0.005 0 0 0.002 0 0 0

61 0.0013 0.002 0.002 0.002 0 0 0.002 0 0 0

62 -0.009 -0.01 -0 -0.01 -0

-

0.01 -0

-

0.01 -0

-

0.01

63 6E-05

1E-

04

1E-

04

5E-

05 0 0

8E-

05 0 0 0

64 0.0019

2E-

04

1E-

03

8E-

04 0 0 0.002 0 0 0

65 -5E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

66 -4E-05 -0 -0 -0 -0 -0 -0 -0 -0 -0

67 -8E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

Page | 123


68 0.0013 0.001 0.003 0.001 0 0 0.004 0 0 0

69 0.0103 0.009 0.014 0.012 0.01 0 0.014 0 0.01 0.01

70 0.0033 0.003 0.005 0.009 0.01 0.01 0.004 0.01 0.01 0.01

71 -4E-04 -0 -0 -0.01 -0

-

0.01 -0.01

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0.01 -0

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72 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

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74 -0.003 -0.01 -0.01 -0.01

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0.01

-

0.01 -0.01

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0.01 -0

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0.02

75 0.003

2E-

04 0.009 0.006 0.01 0 0.006 0.01 0.01 0

76 0.0006 0.004 0.009 0.002 0.01 0.01 0.006 0 0 0.01

77 0.0009

7E-

04 0.005 0.003 0 0.01 0.004 0 0 0

78 -0.005 -0 -0 -0.01 -0 -0 -0

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0.01 -0 -0

79 0.001 0.005 0.005 0.001 0 0 0.001 0 0 0

80 -0.004 -0 -0 -0 -0 -0 -0 -0 -0 -0

81 0.0067 0.005 0.011 0.006 0 0.01 0.004 0.01 0.01 0.01

82 -0.009 -0.01 -0 -0 -0 -0 -0.01 -0 -0 -0

83 0.0042 0.003

4E-

04 0.001 0 0

4E-

04 0 0 0

84 -0.023 -0 -0.01 -0.01 -0

-

0.02 -0.03

-

0.02 -0 -0

85 -4E-04 -0.02 -0 -0.02

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0.02

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0.02 -0

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0.02 -0 -0

86 -0.005 -0.01 -0 -0.01

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87 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

Page | 124


88 0.0054 0.001 0.006 0.005 0 0

5E-

04 0 0 0

89 0.0004

8E-

04

9E-

04

1E-

04 0 0

9E-

05 0 0 0

90 -0.01 -0 -0 -0.01

-

0.01

-

0.01 -0.01 -0 -0

-

0.01

91 0.0006 0.002 0.001 0.001 0 0

2E-

04 0 0 0

92 -7E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

93 0.0026 0.017 0.02 0.02 0.01 0.02 0.013 0.01 0.02 0.01

94 -0.021 -0 -0.02 -0.01

-

0.01

-

0.02 -0.03

-

0.02 -0

-

0.01

95 0.0011

4E-

04 0.006 0.007 0.01 0 0.001 0.01 0 0.01

96 -4E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

97 0.0078 0.005 0.007 0.006 0.01 0 0.008 0.01 0 0

98 0.0024

8E-

04 0.001 0.002 0 0

4E-

04 0 0 0

99 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

100 -6E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

101 -0.018 -0.01 -0.01 -0

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0.01

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0.01 -0.02

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0.02 -0

-

0.01

102 -6E-05 -0 -0 -0 -0 -0 -0 -0 -0 -0

103 -0.009 -0 -0.01 -0.01

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0.01 -0 -0.01 -0 -0

-

0.01

104 -0.008 -0 -0 -0.01 -0 -0 -0.01 -0 -0 -0

105 0.016 0.01

9E-

04 0.016 0.01 0 0.002 0.01 0 0.01

106 0.0054 0.003 0.005 0.004 0 0 3E- 0 0.01 0.01

Page | 125


04

107 0.0059 0.016 0.01 0.011 0.02 0.01 0.003 0 0.01 0.01

108 -0.003 -0.01 -0 -0

-

0.01

-

0.01 -0

-

0.01 -0 -0

109 0.0076 0.018 0.015 0.017 0 0.02 0.004 0.01 0.01 0.01

110 -0.003 -0.01 -0 -0

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0.01 -0 -0 -0 -0 -0

111 0.0131 0.013 0.009 0.003 0.01 0.01 0.011 0.01 0.02 0.01

112 -0.006 -0.01 -0 -0.02 -0

-

0.02 -0.02

-

0.01 -0

-

0.01

113 0.0013 0.002

1E-

03

6E-

04 0 0 0.002 0 0 0

114 0.0022

8E-

04 0.001 0.002 0 0

6E-

04 0 0 0

115 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

116 0.0064 0.005 0.002 0.005 0.01 0.01 0.005 0.01 0 0

117 -0.004 -0 -0 -0

-

0.01

-

0.01 -0.01

-

0.01 -0 -0

118 0.0106 0.008 0.012

9E-

04 0 0.01 0.012 0 0.01 0

119 -0.003 -0.01 -0 -0

-

0.01

-

0.01 -0

-

0.01 -0 -0

120 0.0077 0.013 0.002 0.015 0.01 0.01 0.004 0 0.01 0

121 0.0006 0.003

1E-

03

1E-

04 0 0

3E-

04 0 0 0

122 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

123 0.0014 0.009 0.002

1E-

03 0.01 0.01 0.005 0.01 0.01 0.01

124 0.0032 0.003 0.002 0.002 0 0 0.002 0 0 0

Page | 126


125 -0.003 -0 -0.01 -0

-

0.01 -0 -0 -0 -0 -0

126 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

127 0.0002

1E-

04

9E-

05

1E-

04 0 0

7E-

05 0 0 0

128 -0.01 -0.01 -0.02 -0 -0

-

0.02 -0.03

-

0.02 -0

-

0.02

129 0.0002

5E-

05

3E-

04

5E-

05 0 0

3E-

04 0 0 0

130 -0.008 -0.02 -0.01 -0.03 -0

-

0.02 -0.01

-

0.03 -0

-

0.03

131 -0.005 -0 -0 -0 -0 -0 -0 -0 -0 -0

132 0.0093 0.01 0.011

6E-

04 0 0.01 0.01 0.01 0.01 0.01

133 0.0001

8E-

05

2E-

04

2E-

04 0 0

6E-

04 0 0 0

134 -0.027 -0 -0.01 -0.04

-

0.03

-

0.03 -0.03

-

0.03 -0

-

0.01

135 0.0001

5E-

04

4E-

04

3E-

04 0 0

2E-

05 0 0 0

136 2E-05

2E-

04

5E-

05

3E-

04 0 0

3E-

04 0 0 0

137 -0.017 -0.02 -0.02 -0.02

-

0.02

-

0.01 -0.01

-

0.01 -0

-

0.01

138 0.0019 0.004 0.006 0.003 0 0.01 0.002 0 0 0

139 0.0018 0.002

6E-

04

2E-

04 0 0

6E-

04 0 0 0

140 -8E-04 -0 -0 -0

-

0.01 -0 -0.01 -0 -0

-

0.01

Page | 127


141 -0.001 -0.01 -0 -0 -0 -0 -0.01

-

0.01 -0 -0

142 0.0061 0.005 0.002 0.006 0.01 0 0.005 0 0.01 0.01

143 0.0018

7E-

05 0.003

1E-

03 0 0 0.003 0 0 0

144 0.0078

2E-

04 0.001 0.006 0.01 0 0.004 0 0 0

145 0.0156

4E-

04 0.008 0.003 0 0.01 0.002 0.01 0.02 0.01

146 -0.003 -0 -0.01 -0.01

-

0.01

-

0.01 -0.02

-

0.01 -0 -0

147 -8E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

148 -0.003 -0 -0 -0 -0 -0 -0.01 -0 -0 -0

149 0.019 0.005 0.016 0.019 0.01 0.02 0.004 0.01 0.02 0.01

150 0.0059 0.004 0.005 0.001 0 0 0.002 0 0 0

151 0.0057 0.003

6E-

04 0.006 0.01 0.01 0.001 0 0 0

152 -0.006 -0 -0 -0 -0

-

0.01 -0

-

0.01 -0 -0

153 -6E-06 -0 -0 -0 -0 -0 -0 -0 -0 -0

154 -8E-04 -0 -0 -0 -0

-

0.01 -0 -0 -0

-

0.01

155 0.0025 0.002 0.002 0.002 0 0 0.002 0 0 0

156 -0.007 -0.01 -0 -0 -0 -0 -0

-

0.01 -0 -0

157 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

158 -0.022 -0 -0 -0.02

-

0.02

-

0.03 -0.02 -0 -0 -0

159 0.0007 5E- 5E- 3E- 0 0 6E- 0 0 0

Page | 128


04 04 04 04

160 -0.004 -0 -0.01 -0.01 -0

-

0.01 -0

-

0.01 -0

-

0.01

161 6E-05

6E-

05

2E-

05

3E-

04 0 0

5E-

05 0 0 0

162 -0.002 -0.02 -0 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0 -0

163 0.0007 0.006 0.012 0.002 0 0 0.003 0.01 0.01 0.01

164 -2E-05 -0 -0 -0 -0 -0 -0 -0 -0 -0

165 -0.012 -0.01 -0.03 -0.01

-

0.01

-

0.03 -0.01

-

0.03 -0

-

0.03

166 0.0013 0.002 0.002

5E-

04 0 0

6E-

04 0 0 0

167 -0.003 -0 -0 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

168 0.005 0.003 0.017 0.015 0.01 0.01 0.003 0 0.02 0.01

169 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

170 0.0047 0.009 0.007

9E-

04 0 0.01

2E-

04 0.01 0 0.01

171 0.0029

7E-

04 0.002 0.004 0.01 0.01 0.006 0 0.01 0

172 0.0195 0.005 0.008 0.003 0.01 0.02 0.018 0.01 0.01 0.01

173 0.0084 0.024 0.013 0.002 0 0 0.014 0.01 0.02 0.01

174 0.0189 0.018 0.02 0.011 0.02 0.02 0.008 0.01 0.02 0.02

175 -7E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

176 0.0018

6E-

04 0.002

7E-

04 0 0 0.001 0 0 0

177 0.0127 0.011 0.012 0.006 0.01 0.01 0.006 0.01 0 0.01

178 0.0028 0.002 0.002 0.003 0 0 8E- 0 0 0

Page | 129


05

179 0.0039 0.001 0.002 0.002 0 0 0.004 0 0 0

180 -5E-04 -0.01 -0 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

181 -1E-04 -0 -0 -0

-

0.01

-

0.01 -0.01 -0 -0

-

0.01

182 0.0036 0.005 0.003

7E-

04 0 0.01

9E-

04 0.01 0 0.01

183 0.0004

8E-

04

9E-

04

6E-

04 0 0

9E-

04 0 0 0

184 -0.005 -0 -0.01 -0.01 -0

-

0.01 -0.01

-

0.01 -0

-

0.01

185 0.0076 0.003 0.001 0.004 0.01 0.01

5E-

07 0.01 0.01 0

186 -4E-04 -0.01 -0.01 -0.01 -0 -0 -0 -0 -0 -0

187 -0.004 -0 -0 -0 -0 -0 -0.01

-

0.01 -0

-

0.01

188 -0.008 -0.02 -0.01 -0

-

0.03

-

0.01 -0.01

-

0.02 -0

-

0.01

189 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

190 0.0081 0.012 0.007 0.007 0.01 0.01 0.004 0 0.01 0.01

191 -0.003 -0 -0 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

192 0.0165 0.011 0.003 0.006 0.01 0.02 0.011 0.02 0.01 0.01

193 -0.001 -0.01 -0.01 -0 -0 -0 -0.01 -0 -0

-

0.01

194 -0.013 -0.01 -0.01 -0 -0

-

0.01 -0

-

0.01 -0 -0

195 -9E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

Page | 130


196 0.0005 0.002 0.001

5E-

04 0 0 0.004 0 0 0

197 0.002 0.002 0.002 0.005 0 0.01 0.005 0.01 0 0.01

198 -0.006 -0.01 -0.01 -0.01

-

0.01

-

0.02 -0.01 -0 -0 -0

199 0.0029 0.024 0.009 0.004 0 0.02 0.01 0 0.01 0.02

200 0.0025 0.001 0.003

2E-

05 0 0 0.003 0 0 0

201 -0.002 -0 -0.02 -0.02 -0

-

0.02 -0.02 -0 -0

-

0.01

202 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

203 -0.011 -0.01 -0.01 -0.01

-

0.01

-

0.01 -0 -0 -0 -0

204 0.0012 0.001 0.001

7E-

04 0 0

1E-

03 0 0 0

205 -0.01 -0.02 -0.03 -0.02

-

0.02 -0 -0.01

-

0.02 -0

-

0.01

206 0.0014

3E-

04

8E-

04

9E-

04 0 0 0.001 0 0 0

207 -0.005 -0.01 -0 -0.01 -0

-

0.01 -0.01 -0 -0 -0

208 0.0166 0.021 0.01 0.002 0.02 0.03 0.023 0.02 0.01 0.02

209 -0.002 -0.01 -0.01 -0.02

-

0.01

-

0.02 -0.02

-

0.01 -0 -0

210 0.0107 0.002 0.008 0.012 0.01 0.01 0.004 0 0.01 0

211 -3E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

212 -0.018 -0 -0.01 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

213 0.0002 0.001 0.002 1E- 0 0 5E- 0 0 0

Page | 131


03 04

214 -0.012 -0 -0 -0.02

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.02

215 0.0144 0.016 0.017 0.011 0.01 0.02 0.028 0.02 0.01 0.01

216 0.0086 0.01 0.02 0.005 0.01 0.02 0.021 0 0 0.01

217 -0.013 -0 -0.01 -0.02

-

0.01

-

0.01 -0.01

-

0.02 -0

-

0.01

218 -0.01 -0.01 -0 -0.02 -0 -0 -0.01 -0 -0

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0.01

219 0.009 0.017 0.005 0.018 0.01 0.02 0.005 0.02 0 0.01

220 -4E-04 -0.01 -0.01 -0.01

-

0.01

-

0.01 -0.01

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0.01 -0

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0.01

221 -0.006 -0 -0.01 -0.01 -0 -0 -0.01

-

0.01 -0

-

0.01

222 0.0055 0.004 0.004 0.006 0.01 0 0.003 0 0 0

223 -6E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

224 -2E-05 -0.01 -0 -0

-

0.01

-

0.01 -0 -0 -0

-

0.01

225 0.0209 0.008 0.006 0.031 0.02 0.01 0.025 0.01 0.01 0.01

226 0.021 0.005 0.022 0.04 0.02 0.01 0.01 0.04 0.03 0.01

227 -0.008 -0.01 -0.02 -0.02

-

0.01

-

0.01 -0.03

-

0.01 -0

-

0.01

228 0.0039 0.023 0.023 0.017 0 0.01 0.017 0.02 0.02 0.03

229 0.002 0.005 0.005 0.001 0 0 0.004 0 0 0

230 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

231 -0.001 -0 -0 -0 -0 -0 -0 -0 -0 -0

232 0.0004

5E-

04

5E-

04

2E-

04 0 0

7E-

05 0 0 0

233 -7E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

Page | 132


234 -0.005 -0 -0.02 -0.03

-

0.02

-

0.02 -0

-

0.01 -0

-

0.02

235 0.0093 0.007 0.006 0.005 0.01 0.01

9E-

04 0.01 0 0

236 0.0036 0.005 0.005 0.016 0.02 0.02 0.003 0 0.01 0.02

237 0.0109 0.015 0.005 0.008 0 0 0.013 0.01 0.01 0.02

238 0.0029 0.002 0.005 0.001 0 0 0.005 0 0 0

239 -0.018 -0 -0.02 -0.01

-

0.03

-

0.03 -0.01

-

0.02 -0 -0

240 0.0026 0.003 0.002

4E-

04 0 0

2E-

04 0 0 0

241 0.0031 0.003 0.001 0.002 0 0 0.004 0 0 0

242 -0.004 -0 -0 -0 -0 -0 -0.01 -0 -0 -0

243 -9E-05 -0.02 -0.02 -0

-

0.02

-

0.01 -0.01

-

0.02 -0 -0

244 -0.006 -0 -0 -0 -0

-

0.01 -0 -0 -0 -0

245 -0.011 -0 -0.01 -0.02

-

0.01

-

0.02 -0 -0 -0

-

0.02

246 -0.004 -0 -0.02 -0.02

-

0.02

-

0.02 -0

-

0.01 -0

-

0.02

247 0.0191 0.013 0.029 0.012 0.01 0.02 0.015 0.02 0.03 0.03

248 0.0069 0.01 0.004 0.003 0 0.01 0.011 0 0 0

249 -0.003 -0 -0 -0 -0 -0 -0 -0 -0 -0

250 -0.018 -0.01 -0 -0

-

0.02

-

0.01 -0.01

-

0.02 -0

-

0.01

251 -0.032 -0.03 -0.03 -0.02

-

0.02 -0 -0.01

-

0.02 -0 -0

252 -0.005 -0 -0 -0 -0 -0 -0.01 - -0 -0

Page | 133


0.01

253 0.0187 0.026 0.012 0.011 0.01 0 0.005 0.01 0.01 0.03

254 -6E-04 -0 -0 -0.01

-

0.02

-

0.01 -0.01 -0 -0

-

0.01

255 -0.006 -0.01 -0.01 -0 -0 -0 -0.01

-

0.01 -0

-

0.01

256 -0.009 -0 -0 -0

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

257 0.0013 0.021 0.023 0.021 0.01 0 0.022 0.02 0 0.02

258 -0.013 -0.01 -0.01 -0 -0

-

0.01 -0.01

-

0.01 -0 -0

259 -0.001 -0.01 -0 -0

-

0.01 -0 -0.01 -0 -0 -0

260 0.0153 0.012 0.007 0.016 0 0.01 0.009 0.01 0.01 0

261 0.0243 0.011 0.03 0.003 0 0.03 0.019 0.03 0 0.01

262 0.0056 0.01 0.006

7E-

05 0.02 0 0.011 0.02 0.02 0

263 -0.002 -0 -0 -0 -0 -0 -0 -0 -0 -0

264 -0.008 -0.01 -0.01 -0.01 -0 -0 -0 -0 -0 -0

265 -1E-03 -0.01 -0.01 -0.01 -0 -0 -0 -0 -0

-

0.01

266 -0.011 -0 -0 -0.01

-

0.01

-

0.01 -0.01

-

0.01 -0

-

0.01

267 -9E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

268 0.0062 0.01 0.003

6E-

04 0 0.01 0.006 0 0.01 0

269 0.0019 0.005

2E-

04 0.002 0.01 0.01 0.012 0.01 0.01 0.01

270 -2E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

Page | 134


271 -0.009 -0.01 -0.01 -0.02 -0

-

0.01 -0.02 -0 -0

-

0.01

272 -0.005 -0 -0 -0.01

-

0.01 -0 -0

-

0.01 -0

-

0.01

273 0.0025 0.005

3E-

05

2E-

04 0 0 0.004 0 0 0

274 -0.012 -0 -0.01 -0 -0 -0 -0

-

0.01 -0

-

0.01

275 0.0067 0.011 0.002 0.004 0 0 0.002 0.01 0.01 0.01

276 0.0164 0.023 0.013 0.012 0.02 0 0.025 0.01 0.01 0.02

277 0.0008

7E-

04

1E-

04

6E-

04 0 0

4E-

04 0 0 0

278 0.0067 0.004 0.006 0.005 0 0.01 0.003 0.01 0 0

279 0.001

7E-

04 0.004 0.002 0 0 0.003 0 0 0

280 -0.003 -0 -0 -0 -0 -0 -0.01

-

0.01 -0 -0

281 1E-04

2E-

04

3E-

04

9E-

05 0 0

1E-

04 0 0 0

282 -0.012 -0.01 -0 -0.01

-

0.02

-

0.02 -0

-

0.02 -0

-

0.02

283 0.0072 0.008 0.002 0.005 0.01 0.01 0.003 0.01 0.01 0

284 -0.019 -0 -0.01 -0.02 -0

-

0.04 -0.03

-

0.02 -0

-

0.03

285 0.003 0.004 0.003 0.002 0.01 0 0.004 0.01 0.01 0

286 -0.008 -0.02 -0.03 -0.02

-

0.03

-

0.01 -0.03

-

0.02 -0

-

0.03

287 0.0035 0.007 0.005 0.005 0.01 0 0.004 0 0 0

288 -0.023 -0.01 -0 -0.01 - - -0 - -0 -

Page | 135


0.02 0.01 0.01 0.01

289 -0.004 -0 -0 -0 -0 -0 -0 -0 -0 -0

290 -0.003 -0.01 -0 -0

-

0.01

-

0.01 -0 -0 -0

-

0.01

291 4E-05

1E-

04

3E-

04

4E-

05 0 0

9E-

04 0 0 0

292 -0.014 -0.02 -0.02 -0

-

0.02 -0 -0.01

-

0.02 -0

-

0.02

293 -0.004 -0.02 -0.03 -0 -0

-

0.04 -0.03

-

0.02 -0 -0

294 0.0188 0.011 0.018 0.006 0.01 0 0.024 0.01 0.01 0.02

295 0.0043 0.001

9E-

04 0.004 0 0 0.002 0 0 0

296 0.0003

5E-

04

6E-

04

2E-

04 0 0

4E-

04 0 0 0

297 -0.02 -0.01 -0.01 -0.01

-

0.01

-

0.01 -0.02

-

0.02 -0

-

0.01

298 0.0064 0.009 0.009 0.022 0.01 0 0.008 0.01 0.02 0.01

299 -0.016 -0.01 -0.03 -0.01

-

0.04

-

0.03 -0

-

0.02 -0

-

0.01

300 -0.022 -0.01 -0.01 -0 -0

-

0.01 -0.02 -0 -0

-

0.01

301 0.005

8E-

05 0.026 0.002 0.02 0 0.009 0.01 0.01 0.02

302 -0.018 -0.03 -0 -0.02

-

0.03

-

0.02 -0

-

0.01 -0

-

0.02

303 0.0126 0.001 0.005 0.02 0.02 0.01 0.022 0.01 0.02 0.02

304 -0.001 -0 -0.01 -0.01 -0 -0 -0

-

0.01 -0 -0

Page | 136


305 -2E-04 -0 -0 -0 -0 -0 -0 -0 -0 -0

306 0.0014

3E-

04 0.001 0.001 0 0

9E-

04 0 0 0

307 -0.011 -0.01 -0.01 -0.02

-

0.03

-

0.01 -0.03

-

0.02 -0

-

0.01

308 -0.004 -0 -0 -0 -0 -0 -0 -0 -0 -0

309 0.0022 0.004 0.004 0.003 0 0

2E-

04 0 0 0

310 -0.004 -0.01 -0.01 -0.02

-

0.01 -0 -0

-

0.01 -0 -0

311 -0.006 -0 -0 -0

-

0.01

-

0.01 -0.01 -0 -0

-

0.01

312 0.0179 0.004 0.013 0.015 0.01 0.02 0.015 0 0.02 0.02

313 -0.056 -0.01 -0.04 -0 -0 -0 -0.05

-

0.05 -0.1

-

0.01

Step 4: Thereafter simulated prices have been found and so are the simulated profits and losses.

After the profits have been found, they have been arranged in the descending order of the

confidence intervals and by looking up for the confidence level, VaR has been generated.

Page | 137


Return on stock as on 28th March 651.4

Simulated PricesSimulated P/L

Ranking Simulated P/l

Confidence Level

586.4389259 -64.761074 -1.020669703 10%458.5420779 -192.65792 -36.36364655 20%427.4205911 -223.77941 -64.7610741 30%551.2910354 -99.908965 -99.90896462 40%650.1793303 -1.0206697 -106.0922394 50%614.8363535 -36.363647 -121.5269595 60%529.6730405 -121.52696 -125.359997 70%545.1077606 -106.09224 -139.3928951 80%

525.840003 -125.36 -169.401735 90%511.8071049 -139.3929 -192.6579221 100%

481.798265 -169.40173 -223.7794089 110%

VaR at 90% for Montecarlo Simulation comes to be 169.40

Hence, depending upon what an investor expects he/she can gauge the value at loss. As already

explained the significance lies in retaining investors by giving them a tool of actual measure.

Page | 138


CREDIT MIGRATION MATRIX FOR ASCERTAINING THE PROBABILITY OF

DEFAULT FOR THE BANK

Credit migration or transition matrices, which characterize the past changes in credit quality of

obligors (typically firms), are cardinal inputs to many risk management applications, including

portfolio risk assessment, modeling the term structure of credit risk premia, and pricing of credit

derivatives. For example, in the New Basel Accord (BIS (2001)), capital requirements are driven in

part by ratings migration. Their accurate estimation is therefore critical.

Analysis

Credit

Migration

Page | 139


Matrix

Initial/Final

RATING A1+ FAAA/Stable AA+/Stable LAA B1 BB CC/C D

A1+ 94.00% 3% 1.00% 2.00% 0.00% 0.00% 0.00% 0.00%

FAAA/Stable 0.60% 90% 3.00% 2.00% 1.00% 0.00% 0.00% 3.40%

AA+/Stable 0.20% 3.00% 88% 3.00% 2.00% 1.00% 0.80% 2.00%

LAA 0.00% 0.26% 3.81% 84% 2.70% 0.56% 0.07% 8.61%

B1 0.00% 0.00% 0.00% 7.00% 75% 6.44% 0.09% 11.49%

BB 0.00% 0.00% 0.00% 0.00% 7.83% 75% 2.56% 14.53%

CC/C 0.00% 0.00% 0.00% 0.00% 0.00% 20.00% 45% 35.00%

D 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 100%

The matrix has been formulated using credit ratings and the probabilities of transition within the

bank form one year to another. The matrix can used to determine the probability of default (PD) in

the case of different scenarios.

Say for example that we need the PD for a rating of FAAA/Stable. We see that if we assume that

the credit may fall to lower status with a probability of 4.5, for this the initial probability metrics is

to add up all the non-investment grade ratings after which we get 4.4 as the probability and now we

intend to find the PD when it changes to a lower rating. In this case if we consider the transition

from FAAA we get the PD to be 3.40%. As and when the credit rating changes, the defaults can be

adjudged. This is an important phenomenon for macro-prudential analysis basically because there is

too much reliance of the financial institutions on the credit rating agencies which tend to influence

their decision and if a financial institution is able to calculate the matrix it has an idea of which

credit rating transition in one year is going to affect the bank the most. So, now when the bank has

to adopt the BASEL III norms it can clearly formulate an idea to what an extent it can bear a credit

transition in the entire process.

Page | 140


Chapter 5

Page | 141


Results

The entire project has been prepared with a view to assisting the Kotak Mahindra Bank’s on-going

study on current analysis relating to adoption of the BASEL III Accord in a phased manner. The

study conducted by me has given the following results.

Regression Analysis: In every way the major factors affecting the share capital are highly correlated

to it so fluctuations for each of these factors tend to affect the share capital on a whole. So, the bank

must seek to harmonize the factors.

CAMEL Analysis: Most of the ratios indicate a positive future for the bank but the bank needs to

accumulate more assets in the near future to back the activities. The capital plan needs to

incorporate this fact.

Page | 142


Altman Z-Score: The bank is far away from bankruptcy but the later on stress testing shows that it

is important to protect the elements composing the Z-Score because the bank can easily come into

the grey area.

VaR Analysis: The Value at Risk by investing into the shares of the banks is not really low if time

frames are extended as is done by me, with historical simulation, a hundred days period, the VaR is

too low but as soon as the time frame has been extended, the VaR is nearly 10% of the value. The

bank’s share holder confidence needs to be retained if the bank needs to do well on the capital front.

For this the bank needs to focus on strengthening its capital base and increase the frequency of

advances.

Stress Testing: The most important technique used in this project which is directly related to the

topic being covered unlike others which were just basic tools needed in order to quantify various

aspects on which the bank is leading and lagging. The stress testing was conducted in two ways,

viz, creating scenarios to find out the Altman Z Score changes which led to the clearance of the fact

that persistent minute changes in the years to come can possibly downgrade the bank.

The next test, Dodd-Frank test is rather an experiment of the stress testing guidelines provided by

the Federal Reserve Bank of USA for 18 Bank Holding Companies to create hypothetical scenarios

and forecast capital changes so that they can be ready to take stress.

The results have shown the calculation of the change in regulatory capital that will put a burden on

the bank even if the bank is in a good position as of now.

Credit Migration Matrix: Was formulated using the Markov Process in order to find out the

probabilities of default in case the credit rating of the bank changes. The matrix is useful in

determining whether or not the bank can take risk on the capital front.

Page | 143


So, I can say that the overall study in this project signifies that the Kotak Mahindra Bank is as of

now in a safe situation to carry out phased implementation of the BASEL III Accord but given the

level of volatility in the macro-economic scenarios it has to be careful of the fluctuations.

Appendix & Bibliography

Page | 144


APPENDIX

Following is the list of tables that have been used as a reference for the project.

Balance sheet - Kotak Mahindra Bank Ltd.

Particulars Mar'12 Mar'11 Mar'10 Mar'09 Mar'08

Liabilities

12

Months

12

Months

12

Months

12

Months

12

Months

Share Capital 370.34 368.44 348.14 345.67 344.67

Reserves & Surplus 7,610.41 6,464.95 4,191.78 3,559.86 3,249.04

Net Worth 7,980.76 6,833.39 4,539.92 3,905.53 3,593.71

Secured Loans 16,595.52 11,723.95 6,140.51 5,904.07 5,119.25

Unsecured Loans 38,536.52 29,260.97 23,886.47 15,644.93 16,423.65

Page | 145


TOTAL LIABILITIES 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

Assets

Gross Block 955.41 831.8 745.34 460.61 391.42

(-) Acc. Depreciation 505.45 406.2 317.69 247.25 181.17

Net Block 449.97 425.61 427.65 213.36 210.25

Capital Work in Progress. 0 0 0 0 0

Investments. 21,566.81 17,121.44 12,512.66 9,110.18 9,141.99

Inventories 0 0 0 0 0

Sundry Debtors 0 0 0 0 0

Cash And Bank 2,634.55 2,470.98 2,300.26 1,140.67 2,149.47


Total Current Assets 43,649.70 33,303.62 24,496.00 19,388.34 18,960.12

Current Liabilities 2,502.02 2,991.15 2,839.83 3,227.01 3,145.51

Provisions 51.65 41.21 29.59 30.33 30.24

Total Current Liabilities 2,553.67 3,032.36 2,869.42 3,257.34 3,175.75

NET CURRENT ASSETS 41,096.02 30,271.26 21,626.59 16,130.99 15,784.37

Misc. Expenses 0 0 0 0 0

TOTAL ASSETS (A+B+C+D+E) 63,112.80 47,818.31 34,566.90 25,454.53 25,136.61

Profit and Loss Account

Quarterly results in brief

(INR crore)


Sales 2,094.58 1,923.70 1,815.83 1,744.83 1,640.98

Page | 146


Operating profit 1,497.13 1,326.36 1,267.62 1,243.56 1,120.46

Interest 1,271.73 1,165.57 1,094.53 1,057.10 989.53

Gross profit 572.62 482.21 448.38 445.32 443.61

EPS (INR) 4.86 3.77 3.8 4.01 3.73

Quarterly results in details


Other income 304.86 250.8 241.15 254.23 281.96

Stock adjustment - - - - -

Raw material - - - - -

Power and fuel - - - - -

Employee expenses 263.59 242.77 256.55 224.83 226.01

Excise - - - - -

Admin and selling expenses - - - - -

Research and development expenses - - - - -

Expenses capitalised - - - - -

Other expenses 291.5 283.95 257.53 271.82 263.79

Provisions made 42.36 70.62 34.14 4.63 30.72

Depreciation - - - - -

Taxation 168.58 131.21 131.79 143.77 136.81

Net profit / loss 361.68 280.38 282.45 296.93 276.08

Extra-ordinary item - - - - -

Prior year adjustments - - - - -

Equity capital 372.1 371.65 371.24 370.35 369.78

Equity dividend rate - - - - -

Agg.of non-prom. shares (Lacs) 4085.11 4076.25 4067.93 4050.06 4037.77

Agg.of non promotoholding (%) 54.89 54.84 54.79 54.68 54.6

OPM (%) 71.48 68.95 69.81 71.27 68.28

GPM (%) 23.86 22.18 21.8 22.28 23.07

Page | 147


NPM (%) 15.07 12.89 13.73 14.85 14.36

Unaudited Profit and Loss Account

Page | 148


Promoter Share Holding

Page | 149


Page | 150


Page | 151


Page | 152


BASEL II Disclosures

Capital Rs. Crore

Tier-1 capital 8,574.56

Tier-2 capital 843.81

Total Capital funds of the Bank 9,418.38

Total Capital required at 10% 6,024.90




Page | 153


Leverage Exposure (quarterly averages)B

Leverage Exposure for Tier 1 Leverage Ratio (Applicable to All Banks)

Average Total AssetsAmounts Deducted from Tier 1 Capital (Report as Negative)

Average Total Assets for Leverage Capital Purposes

Leverage Exposure for Supplementary Leverage Ratio (Applicable to Advanced Approaches Banking Organizations)

On-Balance Sheet DerivativesDerivatives, Potential Future ExposureOn-Balance Sheet Repo-Style TransactionsOther On-Balance Sheet Items (Excluding Derivatives and Repo-Style Transactions)Off-Balance Sheet Items (Excluding Derivatives and Repo-Style Transactions)

Of Which: Unconditionally Cancellable Commitments Eligible for 10% Credit Conversion FactorOf Which: All Other

Amounts Deducted from Tier 1 Capital (Report as Negative)

Total Leverage Exposure for Supplementary Leverage Ratio

Loan Losses Calculator

cr

Change in

2014 Default

Auto loans 17,946.60 - 17,946.60 19740.6 987.03

Personal loans 2,413.33 - 2,413.33 2654.663 132.7332

Page | 154


Home loans 8,369.92 - 8,369.92 9206.912 460.3456

Credit cards 256.36 - 256.36 281.996 14.0998

Other retails 7,472.18 - 7,472.18 8219.398 410.9699

Iron and Steel 100.14 414.47 514.61 566.071 28.30355

Chemical 1,189.14 1,030.74 2,219.88 2441.868 122.0934

Engineering 723.19 1,060.95 1,784.14 1962.554 98.1277

Construction 2,773.21 345.23 3,118.44 3430.284 171.5142

Infrastructure 2,724.00 3,005.74 5,729.74 6302.714 315.1357

NBFC 455.57 664.47 1,120.04 1232.044 61.6022

Automobile 1,305.38 1,031.49 2,336.87 2570.557 128.5279

Other 7,832.95 2,330.25 10,163.20 11179.52 558.976

Total 53,561.97 9,883.34 63,445.31 69789.841 3489.459

Page | 155


Page | 156


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