macroeconomic and financial management … · table 3.1. calibration of credit risk shocks ..... 23...
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MACROECONOMICANDFINANCIALMANAGEMENTINSTITUTEFOREASTERNANDSOUTHERNAFRICA
A MACRO-FINANCIAL STRESS TESTING FRAMEWORK FOR
MOZAMBICAN BANKING SECTOR
Dércio Eunísio Mutimucuio
BANCO DE MOÇAMBIQUE
February, 2015
II
A Technical Paper submitted in partial fulfilment of the Award of MEFMI Fellowship
I
LIST OF ABBREVIATIONS AND ACRONYMS
BCBS Basel Committee on Banking Supervision
BdM Banco de Moçambique
BOU Bank of Uganda
CA Custody abroad
CAMELS Capital adequacy, Assets quality, Management, Earnings, Liquidity, Sensitivity to market
risk
CAR Capital adequacy ratio
CBS Central Bank of Swaziland
CEBS Committee of European Banking Supervisors
CGFS Committee on the Global Financial System
EBA European Banking Authority
EDF Expanded default frequencies
ELR Expanded liquidity ratio
EUR Euro
FC Foreign currency
FDI Foreign-direct investment
FSAP Financial System Assessment Program
GDP Gross domestic product
IMF International Monetary Fund
KMV Kealhofer, McQuown and Vasicek
LCR Liquidity coverage ratio
LGD Loss given default
LLP Loan loss provision
LR Liquidity ratio
II
LSDV Least square dummy variable
LST Liquidity stress test
MEFMI Macroeconomic and Financial Management Institute for Eastern and Southern Africa
MZN Metical
NII Net interest income
NPL Non-performing loan
NR Non-resident
OLS Ordinary least square
PD Probability of default
PL Performing loan
RBZ Reserve Bank of Zimbabwe
REER Real effective exchange rate
RWA Risk-weighted asset
SCAP Supervisory Capital Assessment Program
SLFR Standing lending facility rate
SRM Systemic Risk Monitor
UK United Kingdom
USD American Dollar
VAR Vector autoregressive
VEC Vector error correction
ZAR South African Rand
III
ABSTRACT
The study reviews the stress testing practices of some selected MEFMI member countries and the existing
literature on credit risk determinants to put forward areas of improvement to the current micro-prudential
stress testing framework of the Banco de Moçambique. The study also suggests a methodological approach
to link a credit quality variable used in the micro-prudential stress testing framework with the
macroeconomic environment. With regard to the former, the study found that the currently available dataset
in the Banco de Moçambique does allow for the adoption of interest rate risk stress tests (using maturity gap
analysis to measure NII exposures to interest rate risk, and duration to assess the impact of interest rate
changes on the trading book), combined scenarios to assess the resilience of banks to multiple instantaneous
shocks, and the inclusion of local regulation requirements to increase plausibility of the simulation exercises.
The study found that the limited span of data series of NPL ratio – used as a proxy for probability of default
– constrain the extent to which macro-to-micro stress tests can be implemented because no full economic
cycle is present. Therefore, a proper identification of macroeconomic and bank specific factors that affect
credit quality in Mozambique is crucial for using the proposed methodological approach that combines the
forward-looking macroeconomic perspective, a focus on both the individual banks and the banking system
as whole, and a uniform approach to the assessment of risk exposures across banking institutions.
IV
ACKNOWLEDGEMENT
I owe my gratitude to the Banco de Moçambique and MEFMI for giving me the opportunity to undertake
the MEFMI Fellows Development Programme.
I would like to extend my sincere thanks to my mentor, Mr. Dirk Jan Grolleman for all his invaluable
guidance in coming up with this technical paper. His mentorship made this research an interesting and
enriching experience.
I am grateful also for all the encouragement and support that I received from my colleagues in the Prudential
Supervision and Human Resources Departments of the Banco de Moçambique, and I appreciate the MEFMI
secretariat family for believing in me and offering me all the support during the research.
Finally I would like to extend my gratitude to God and my family for their love and encouragement.
V
CONTENTS 1 INTRODUCTION ..................................................................................................................................................... 1
1.1 Background .................................................................................................................................................... 1
1.2 Research Problem and questions .................................................................................................................. 3
1.3 Limitations ..................................................................................................................................................... 4
1.4 Structure of the Study ................................................................................................................................... 4
2 STRESS TESTING FRAMEWORK IN MOZAMBIQUE ................................................................................................ 6
2.1 Introduction ................................................................................................................................................... 6
2.2 Risk Factors and Calibration of Shocks .......................................................................................................... 6
2.2.1 Credit risk ............................................................................................................................................... 6
2.2.2 Foreign Exchange Risk ......................................................................................................................... 11
2.2.3 Direct Contagion Risk .......................................................................................................................... 14
2.2.4 Liquidity Stress Testing ........................................................................................................................ 14
2.3 Comparison of the BdM’s Framework with the Original Čihák Model ....................................................... 20
3 STRESS TESTING FRAMEWORKS IN SELECTED MEFMI COUNTRIES .................................................................... 22
3.1 Introduction ................................................................................................................................................. 22
3.2 Swaziland and Zimbabwe – Sensitivity tests ............................................................................................... 22
3.3 Uganda – From sensitivity analysis to stress tests based on scenarios ....................................................... 28
4 LITERATURE REVIEW............................................................................................................................................ 33
4.1 Introduction ................................................................................................................................................. 33
4.2 The Stress Testing Process ........................................................................................................................... 33
4.2.1 Designing the Macroeconomic Stress Scenario .................................................................................. 34
4.2.2 Designing the Credit Risk Satellite Models .......................................................................................... 36
4.2.3 Balance Sheet Implementation of Shock Scenario .............................................................................. 42
4.2.4 Impact Measures ................................................................................................................................. 43
5 METHODOLOGY AND DATA................................................................................................................................. 45
VI
5.1 Introduction ................................................................................................................................................. 45
5.2 Credit Risk Macro Stress Testing in Mozambique ....................................................................................... 45
5.3 Modelling Approach and Specification........................................................................................................ 47
5.4 Dependent Variable ..................................................................................................................................... 50
5.5 Explanatory Variables .................................................................................................................................. 51
5.6 Expectations ................................................................................................................................................ 52
5.7 Limitations ................................................................................................................................................... 53
6 EMPIRICAL REVIEW OF FINDINGS ....................................................................................................................... 54
6.1 Introduction ................................................................................................................................................. 54
6.2 Impulse Response of NPL Ratio ................................................................................................................... 54
6.3 After‐shock Macro‐financial Variables Development ................................................................................. 55
6.4 Results of the Panel Data Regression .......................................................................................................... 57
6.5 Capital Adequacy ......................................................................................................................................... 58
7 CONCLUSIONS AND RECOMMENDATIONS ......................................................................................................... 61
7.1 Summary of Conclusions ............................................................................................................................. 61
7.2 Recommendations ....................................................................................................................................... 62
7.3 Further Research ......................................................................................................................................... 63
VII
LIST OF FIGURES
FIGURE 3.1: A SCHEMATIC OVERVIEW OF THE BOU'S STRESS TESTING FRAMEWORK ........................................................................................ 29
FIGURE 4.1: A TYPICAL MACRO SCENARIO STRESS TESTING PROCESS ............................................................................................................. 34
FIGURE 5.1: STATIONARY MACRO‐FINANCIAL VARIABLES ............................................................................................................................ 48
FIGURE 6.1: IMPULSE RESPONSE OF NPL RATIO (INCREASE IN QRGDPG, CG, INFL, AND SLFR) ............................................................................. 55
FIGURE 6.2: MODEL FORECASTING ABILITY USING HISTORICAL DATA ............................................................................................................. 56
LIST OF TABLES
TABLE 2.1: CREDIT RISK SENSITIVITY SHOCKS .............................................................................................................................................. 7
TABLE 2.2: SECTORAL SHOCKS ASSUMED ................................................................................................................................................ 10
TABLE 2.3. FOREIGN EXCHANGE RISK SENSITIVITY SHOCKS .......................................................................................................................... 12
TABLE 3.1. CALIBRATION OF CREDIT RISK SHOCKS ..................................................................................................................................... 23
TABLE 3.2. CALIBRATION OF INTEREST RATE RISK SHOCKS ........................................................................................................................... 25
TABLE 3.3. CALIBRATION OF FOREIGN EXCHANGE RATE RISK SHOCKS ............................................................................................................. 26
TABLE 3.4. CALIBRATION OF LIQUIDITY RISK SHOCKS .................................................................................................................................. 27
TABLE 3.5. LIQUIDITY RISK SHOCK LEVELS ................................................................................................................................................ 27
TABLE 5.1: A PRIORI AUTHOR'S EXPECTATION .......................................................................................................................................... 52
TABLE 6.1: FORECASTED 2014 CAR BY BANK .......................................................................................................................................... 60
1
1 INTRODUCTION
1.1 Background
The 2008/09 financial crisis has highlighted the relevance of financial stability assessment and the need for
developing improved analytical tools to better quantify the systemic risk. Especially, the stability of the
financial system and its ability to withstand unanticipated shocks has become the centre of attention of
various studies in recent years. Quite a bit of work has been done recently to develop an operational
macroprudential policy toolkit that may be employed to assess the stability of the financial sector (see
Osinski, et al. (2013), CGFS (2012), Mitra, et al. (2011), Borio & Drehmann (2009)), and other work
building upon Borio (2003) for general theory and/or best practices on the range of macroprudential
instruments that can be considered as possible components of the toolkit).
Even though the set of indicators that would be necessary and sufficient for operationalizing a
macroprudential policy to attain the tangible goal of detecting both the slow build-up and the sudden
materialization in systemic risk, as argued by the International Monetary Fund (IMF) in its Global Financial
Stability Report, is still in phase of design and definition, some of its main elements have already been
applied with wide acceptability. In particular, stress tests have recently been widely used to assess the
resilience of banking systems (Mitra, et al., 2011).
Stress tests were first carried out by internationally active banks for strategic purposes as part of their risk
management tools. The banks would design and implement stress tests for business analysis to ensure that
management policies are synchronised with bank risks for better allocation of funds and to improve the
quality of business in anticipation of potential adverse shocks (Montes & Artigas, 2013).
Based on the initial impulse provided by banks, bank supervisors realized that they could also use stress
tests as a tool to assess the overall resilience of individual entities and more recently to produce an overall
picture of the resilience of the banking sector as a whole. Therefore, despite their shortcomings as a tool for
detecting vulnerabilities during the lead-up period of the 2008/09 global financial crisis, stress tests
2
reclaimed their credibility as a core macroprudential tool as they made a key contribution in restoring
confidence in the financial system, as demonstrated by the successful Supervisory Capital Assessment
Program (SCAP) exercise carried out by the United States authorities in 2009 (Bernanke, 2010), or crises
management exercises performed by the Committee of European Banking Supervisors (CEBS)/European
Banking Authority (EBA) in countries like Greece, Ireland, Portugal, Spain and Cyprus. Moreover, a recent
study by Borio, et al. (2014) shows that stress testing could be used in effective way as a crisis management
and resolution tool and it could raise the discipline of thinking about financial stability.
In addition to the reasons considered above, the design and implementation of stress tests has taken on
particular importance in recent years due to the recommendations of the IMF and the World Bank. These
institutions recommend running stress tests regularly and these exercises have been assigned a major role in
the Financial Sector Assessment Program (FSAP) to assess the stability of international financial systems in
both developed and emerging economies.1 Moreover, principle 20 of the Basel Committee on Banking
Supervision’s (BCBS) “Principles for sound stress testing practices and supervision” requires supervisors to
perform stress test exercises based on common scenarios for banks in their jurisdiction as a complement for
the stress testing exercises performed by banks (BCBS, 2009).
The recommendations from the above mentioned institutions and the concerns in relation to financial system
stability reviewed above highlight the need to enhance the rigour of the micro- and macro-prudential stress
tests performed by the Central Banks.
Since June 2013, Banco de Moçambique (BdM) adopted a simple sensitivity analysis stress testing model
adapted from Čihák (2007) with additional techniques derived from Worrell (2008). This model focuses on
testing the impact of stressed variables on the solvency and liquidity of banks based on ad-hoc shocks on
1 IMF, Integrating Stability Assessments Under the Financial Sector Assessment Program into Article IV Surveillance (2010) details the incorporation of the financial stability tests to the FSAP exercises. IMF, Macro-financial Stress Testing - Principles and Practices (2012) provides guidelines for this exercise and a summary of the experience of the IMF in its application.
3
balance sheet items that directly or indirectly affect capital adequacy, not establishing any link between
banks’ losses and the macroeconomic environment.
This study discusses how the existing Čihák/Worrell based micro-prudential stress testing framework of the
BdM can be improved and investigates the possibilities of linking the variables stressed in this framework
to the macroeconomic environment, while focusing on the variables related to the main risk in the
Mozambique banking system, credit risk, with the objective of bringing the practises of the BdM more in
line with the internationally developed practises regarding stress testing.
1.2 Research Problem and questions
The problem of this paper is set out as follows:
How can the existing Čihák/Worrel based stress testing framework of the BdM be improved taking
into account literature and regional stress testing practises?
In order to answer the research problem, the following more detailed research questions have been
formulated:
1. What is the BdM’s current micro-prudential stress testing framework?
a. What is the risk spectrum covered by the stress testing framework?
b. What approaches are used for the different risks covered by the existing framework?
c. How does the implemented framework compare to the Čihák/Worrell framework as
described in literature, and what improvements could be made?
2. How do the BdM stress testing practices compare with regional practices?
a. What are the stress testing practices of MEFMI region member countries?
b. How would these regional practices be usable by BdM to improve its stress testing
framework?
4
3. How does the literature suggest linking the variables used in the micro-prudential stress testing
framework with macroeconomic environment?
a. How does literature suggest to link the Čihák/Worrell based micro-prudential framework to
the macroeconomic environment?
b. When focusing on the main risk for the Mozambican banking system (credit risk) how could
this link be established/modelled?
1.3 Limitations
The main shortcoming of stress tests – mainly the macroeconomic stress tests – are the frequent data
limitations. Severe historical shocks are rare in many countries including Mozambique, which limits the
predictive power of historical data. Therefore, adjustment of stress testing models by additional assumptions
that are set by expert judgment is needed.
Other limitations are related to the inability of models to capture correlation of risks and risk measures over
time and across institutions, and the limitations to interpret results in longer time horizon, which originates
the problem of disregarding endogenous responses of the system under simulation. Lastly, the incorporation
of stress testing models’ implications in policy decision-making is only partial.2
1.4 Structure of the Study
This study consists of seven sections. In this section an introduction to the research is presented, and the
research purpose and questions are stated. The research questions and sub questions are discussed in the
subsequent sections. Section two describes the current micro sensitivity stress testing framework of the BdM
and points out some improvements that could be made. Section three discusses the stress testing practices
of some selected MEFMI countries, and highlights possible enhancements that could be imported to the
BdM’s stress testing framework. In the fourth section relevant literature on “macro-to-micro” credit risk
2 Comprehensive discussion of the limitations and challenges of stress tests can be found in Hardy & Schmieder (2013), Drehmann (2008), Čihák (2007), or Sorge & Virolainen (2006).
5
stress tests, exploring the possible link between Čihák’s microprudential stress test framework with macro-
financial variables, is presented. In section five, the methodology and data used to build a satellite model to
link macro-financial drivers of stress with credit risk in Mozambique is discussed. In the sixth section
empirical results are analysed and discussed. In section seven conlusions and recommendations of the study
are discussed.
6
2 STRESS TESTING FRAMEWORK IN MOZAMBIQUE
2.1 Introduction
This section describes the current micro sensitivity stress testing framework of the BdM in terms of the risks
covered, shock types, and deviations from the Čihák/Worrell framework. The section ends by pointing out
some improvements that could be made to the BdM’s current stress testing framework.
2.2 Risk Factors and Calibration of Shocks
The BdM’s stress testing framework is implemented through an Excel-based tool that captures bank-by-
bank data to assess the impact of adverse instantaneous shocks in some risk factors, keeping the others
constant. Four risk categories are considered: credit risk, foreign exchange risk, interbank contagion risk,
and liquidity risk. After impacts are calculated on individual institutions, the results are compared and
aggregated to get the system resilience picture.
2.2.1 Credit risk
Under credit risk, the model assesses the impact of increases in non-performing loans (NPLs3) and its effect
on the required provisions for loan losses, which are deducted from current regulatory capital in line with
international stress test practice. Two main assumptions are considered, which exacerbate the effect of the
assumed shocks: 100 percent provisioning rate for additional NPLs regardless of the collateral associated
with the loan and its classification4; and profits are not taken into account. The nature and magnitude of the
shocks are shown in Table 2.1.
3 From first quarter 2014, by NPLs BdM means loans whose repayments are more than 90 days overdue. From third quarter 2008 to fourth quarter 2013, by NPLs BdM meant impaired loans.
4 BdM has five classes of quality to describe bank loans and determine specific provisions: Class I for loans repayments which are not more than 30 days overdue; Class II for loan repayments in arrears between 31 – 90 days; Class III for loan repayments in arrears between 91 – 180 days; Class IV for loan repayments which are in arrears between 181 – 360 days; and Class V for loans at least 361 days in arrears. The probability of loss increases with each class of loan quality, up to 100 percent in Class V. If loans are assumed with no collateral, BdM requires provisions of 5 percent, 15 percent, 50 pecent, 85 percent, and 100 percent for the five classes of loans, in that order.
7
In terms of presentation and analysis of results, firstly the results are presented in terms of the magnitude of
the change in the capital adequacy ratio (CAR). Secondly, the results are presented in terms of the effective
solvency levels before and after the shocks, allowing the analysis of the banks capacity to withstand the
shocks.
Table 2.1: Credit risk sensitivity shocks
Shock Magnitude (i) Adjustment for underprovisioning (ii) Proportional increase in the nonperforming loans (NPLs)
Gradual increases varying from 10% to 350%
(iii) Increase in the level of default in some economic sectors – Sectoral shocks
Varying dimensions of shocks depending on the economic sector. Range [5% to 20%]
(iv) Default of the five largest borrowers – Concentration risk
(i) Adjustments for underprovisioning
The objective of this shock is to determine provisioning shortfall to be adjusted in the assets value and capital
so that the exercise is focused on the economic value of the asset. Performing loans are assumed to have 0
percent provisioning rate. Provisions needed are determined by summing the reported values on the NPLs
category, since these are assumed to have 100 percent provisioning rate. Provisions held are taken from
banks’ balance sheets.
New capital adequacy ratios are calculated using the following formulas:
∑ (2.1)
, 0 (2.2)
, 0 (2.3)
(2.4)
8
Where:
“i” is an individual bank
“k” is the loan class
RWA are the risk-weighted assets
(ii) Proportional increase in NPLs
The objective of this shock is to assess the extent to which the deterioration in asset quality affects the level
of capitalization of institutions in the banking system.
General NPL shocks from 10 percent to 350 percent to the stock value of existing overdue loans in steps of
10 percent are made. New NPLs lead to new provisions which impact both capital and risk weighted assets
(RWA). Results are measured in the form of stressed capital adequacy ratios (CARs) as follow:
, ∗ 1 (2.5)
, , ∗ ,
, ∗ – , (2.6)
If total provisions calculated based on the new NPLs are less than the existing amount of specific provisions
already held by the bank, no extra provisions are required. System results are measured and presented in the
form of a chart. For each shock j, assets of banks who have stressed CARs below the regulatory minimum
of 8 percent are summed and compared to the system’s total assets. These are called Noncompliant Banks.
The same methodology is used to compare insolvent banks within the system. Banks are considered
insolvent if their stressed CARs are below zero. The following formulas are used:
∑
(2.7)
9
∑
(2.8)
Where:
“i” is an individual bank
“j” is the shock size
“n” is the total number of banks considered in the stress test
“k” is a bank which has its stressed CAR below 8 percent
“ ” is a bank which has its stressed CAR below 0
(iii) Sectoral shocks
This shock aims to determine how each bank will be affected by ad-hoc adverse events in different economic
sectors where it has exposures. Shocks on specific economic sectors using loans by sector and by bank are
simulated. For each sector a specific impact factor, so that new provisions have to be made to cover new
NPLs, is considered. Table 2.2 shows the magnitude of shocks assumed per sector.
Results are measured in the form of stressed CARs by bank and shown in the form of a chart representing
CAR changes by bank.
10
Table 2.2: Sectoral shocks assumed
(percent of performing loans in the sector becoming NPLs)
Agriculture 20 Livestock 5 Forestry 5 Fishing 5 Mining 5 Manufacturing 5 Electricity, Gas and Water 5 Construction and Public Infrastructure 20 Tourism 20 Commerce 20 Transport and Communication 5 Non-monetary Institutions 5 Household 20 Mortgage 20 Other 5
The following formulas are used:
, , ∗ , (2.9)
∑ , (2.10)
,
, (2.11)
Where:
“i” is an individual bank
“s” is the sector
11
(iv) Concentration risk
Concentration risks are assessed by large exposures default tests, which are captured from risk concentration
returns5. Since there is no information on collateral, one hundred percent loan given default (LGD) is
considered. The Top 5 large exposures by bank are simulated to default one by one. Results are shown in a
chart where decreasing CARs can be compared. The following formulas are used:
, ∑ , (2.12)
, . ,
, , (2.13)
Where:
“i” is an individual bank
“b” is the borrower
The contagion effect that results from the fact that some top borrowers have loans in more than one
institution is not taken into account.
2.2.2 Foreign Exchange Risk
The objective of this stress test is to assess the impact of foreign exchange changes in capital and therefore
in the solvency ratio.
The transmission channels are the foreign exchange positions institutions hold at the end of the month under
analysis. Increases in foreign exchange may result in losses or gains depending on the net position of the
institution, whether it is long or short. In cases of losses, these are directly deducted from capital, meanwhile
gains are considered in capital with 50 percent regulatory haircut.
5 The risk concentration returns take into consideration provision calculation exemptions and reductions as established by articles 16 and 17 of Governor’s Notice No. 16/GBM/2013, of 31st December.
12
It is assumed that there is a positive correlation between the three main currencies (American Dollar, Rand
and Euro), so that an exchange rate variation of one of those currencies results in a similar variation on the
exchange rates of the remaining currencies. The nature and magnitude of the shocks are shown in Table 2.3
Table 2.3. Foreign exchange risk sensitivity shocks
Shock Nature Magnitude Increase in foreign exchange rate MZN/USD, ZAR, EUR:
a) Direct foreign exchange rate risk Gradual depreciation of MZN varying from
10% to 200% in the actual value.
b) Indirect foreign exchange rate risk Gradual depreciation of MZN varying from
10% to 200% in the actual value with
assumed increase of NPLs in foreign
denominated loans with half of the assumed
percentage point exchange rate
depreciation.
The impact on each bank’s capital is estimated by simulating shocks from 10 to 200 percent to the exchange
rate level and using reported data on net open positions in foreign exchange. Indirect induced credit risk is
simulated by assuming an increase of the NPLs of foreign exchange denominated loans with half of the
percentage point exchange rate depreciation assumed. Results are shown by a chart with the same measures
used for the sensitivity stress tests for credit risk. Average system CARs are also shown. The following
formulas are used:
(i) Direct shock
∆ , ∑ ∗ (2.14)
13
∆ , (2.15)
if ∆ , 0
then ∆ , ∗ 0.5 (2.16)
, (2.17)
Where:
∆ are the gains or losses arising from the foreign exchange variation;
“i” is the currency (USD, ZAR, EUR)
“j” is the assumed Metical depreciation rate. j=1.1, for 10% depreciation rate
“n” represent the number of the main currencies
is the foreign exchange position per currency “i”; and
(ii) Indirect shocks
∆ , ∗ ∗ 1 ∗ 0.5 1 (2.18)
, ∆ , (2.19)
∆ , ∆ , , 0 (2.20)
,∆ ,
, (2.21)
∑ ∆ ,
∑ , (2.22)
Where:
14
∆ , is the NPL variation arising from the assumed “j” Metical depreciation rate for
each bank “i”
is the NPL for foreign denominated loans
∆ , is the capital variation from the foreign exchange variation;
“n” represent the banks assessed
is the foreign currency denominated loans value for each bank “i”; and
“j” is the Metical depreciation rate
2.2.3 Direct Contagion Risk
While the other stress simulations assume that there is no direct interbank contagion between the banks in a
failure situation, this stress test assumes that the failure of one institution has implications in other
institutions through interbank exposures.
Through the interbank exposures matrix the net position of each institution against the others is determined,
and only the net borrowers are considered. The direct contagion is simulated by removing a bank from the
market and measuring the domino effect on the system. For this exercise, interbank exposures received from
the Financial Markets Department are used to draw a chart6 in which it is possible to see an interconnectivity
diagram. Capital below zero is the measure chosen to trigger insolvency and propagate the domino effect.
2.2.4 Liquidity Stress Testing
Liquidity Stress Tests (LST) aim to produce information for the analysis of liquidity risks arising from
foreign currency cash flows, non-resident customers’ cash flows, and a proxy for the stress scenario for the
Liquidity Coverage Ratio (LCR) established in the Basel III liquidity paper, “Basel III: The Liquidity
Coverage Ratio and liquidity risk monitoring tools”, issued in January 2013
6 An Excel based network analysis and visualization tool called NodeXL is used to draw and analyse the graphs.
15
The tests are applied to balance sheet data and to data on main foreign positions (by currency and by
customer’s country of origin) monthly received from the banks. These stress tests are run in Excel
spreadsheets and cover Currency Stress Scenario, Non-Resident Stress Scenario and LCR Stress Scenario.
The tests measure the banks’ capacity to withstand specific scenarios of stressed cash flows over the
following 30 days period in all scenarios, in order to allow comparative analysis among results. The metrics
consist of comparing the amount of unencumbered liquid assets with the stressed cash flow. “Liquidity
Shortfall” and “Liquidity Surplus”, expressed in Meticais units, are the amount of liquidity under or above
the amount of resources needed to settle the 30-day stressed cash flow, respectively. ”Liquidity Ratio” is the
ratio between the unencumbered liquid assets and the stressed cash flow. Ratios7 under 100 percent indicate
lack of liquidity to face the adverse situations hypothesized in the scenario.
Most Mozambican banks are subsidiaries of foreign banks hence the exposure to foreign currency funding
is substantial, mainly from their parent banks. Also, not only do banks accept deposits but give credit in
domestic and foreign currencies, to residents and non-residents. In the case of LST, liquidity risk is assessed
by currency and by customer’s country of origin. Additionally, the LST tool offers a proxy for the calculation
of the Basel III LCR, as a means of comparing the results among the scenarios. The objective and the
premises of each scenario are:
Currency Stress Scenario – this test estimates the bank’s capacity to settle its obligations in foreign
currency under adverse circumstances that involve (a) partial loss of foreign currency capacity; (b)
early settlement of non-maturing obligations in foreign currency; (c) unexpected withdrawal of credit
and liquidity lines in foreign currency; (d) difficulties in attaining foreign currency inflows (maturing
assets and loans payment); and (v) difficulties in performing foreign currency exchange operations.
7 Ratios are comparable among banks.
16
Non-Resident Stress Scenario – this test estimates the bank’s capacity to settle its obligations with
foreign counterparties under adverse circumstances that involve (a) partial loss of foreign customer
funding capacity; (b) difficulties in attaining assets issued by foreign entities or in receiving loan
payments from foreign customers; (c) early settlement of non-maturing obligations with foreign
counterparties; (d) unexpected withdrawal in irrevocable credit and liquidity lines to foreign
customers.
LCR Stress Scenario – based on BCBS (2013, p. 19) this test estimates the banks capacity to “survive
under a significantly severe liquidity stress scenario, which entails a combined idiosyncratic and
market-wide shock that would result in (a) the run-off of a proportion of retail deposits, including
the run-off of one to a hundred of the top 100 deposits; (b) a partial loss of unsecured wholesale
funding capacity; (c) a partial loss of secured, short-term financing with certain collateral and
counterparties; (d) additional contractual outflows that would arise from a downgrade in the bank’s
public credit rating by up to and including three notches, including collateral posting requirements;
(e) increases in market volatilities that impact the quality of collateral and thus require larger
collateral haircuts or additional collateral, or lead to other liquidity needs; (f) unscheduled draws on
committed but unused credit and liquidity facilities that the bank has provided to its clients; and (g)
the potential need for the bank to buy back debt or honor non-contractual obligations in the interest
of mitigating reputational risk.”
Defining the LCR scenario for Mozambican banks involves some proxies to adjust the Basel metrics to the
available data. For the metrics components, each item from the balance sheet is classified as a liquid asset,
outflow or inflow component, in domestic or foreign currency. Resident or non-resident counterparties are
classified as retail, corporate, financial institution, central bank, etc.; for every position taken, it is indicated
if it was in Mozambique or abroad, and the possibility of settlement in 30 calendar days. The stressed amount
is calculated by multiplying the amount in the balance sheet with the stress factor.
17
∗ (2.23)
Stress factors estimate the behaviour of each component under a stressed environment. The calibration of
the stress factors is the estimation of the position behaviour during stress, for example, haircut factors
indicate loss of value of liquid assets positions, as well as run-off factors applied on deposit positions, to
estimate deposits runs.
(i) Currency Stress Scenario
The Currency Scenario approach estimates the amount of domestic currency needed to face foreign currency
obligations in a currency stressed environment. The scenario compares liquid assets with stressed cash flow,
in terms of both foreign and domestic currency. In the event that foreign currency liquid assets are not
sufficient (i.e. liquid assets are less than the stressed cash flow amount), the test results compare the foreign
currency stressed cash flow with the total amount of liquid assets. To calculate the amounts of the stress
components (liquid assets, outflows and inflows), Stressed Amount is summed by Item and Currency.
(2.24)
If 0 => no further analysis is undertaken, else:
If 0 there is a Liquidity Gap in foreign
currency stressed cash flow;
If 0 the Liquidity Gap in foreign
currency stressed cash flow cannot be covered by domestic currency liquid assets;
If 0 there is a Liquidity Surplus in foreign
currency stressed cash flow.
(2.25)
18
(2.26)
Where:
“FC” is the foreign currency
“Total Liquid Assets” = Liquid Assets in foreign currency + Liquid Assets in domestic
currency
"LRFC" is the Liquidity Ratio in foreign currency
" ” = Expanded Liquidity Ratio in foreign currency
(ii) Non-Resident Stress Scenario
Non-Resident Scenario approach estimates the amount of domestic assets needed to meet obligations to
foreign counterparties in a foreign-country-stressed environment. The scenario compares liquid assets with
stressed cash flow, both held or due to foreign customers and counterparties. In the event that liquid assets
from the bank’s positions abroad are not sufficient (i.e. liquid assets are less than the stressed cash flow
amount), the test results compare the stressed cash flow of foreign customers and counterparties with the
amount of total liquid assets. To calculate the amounts of the stress components (liquid assets, outflows and
inflows), Stressed Amount is summed by Item and Counterparty Country.
(2.27)
If 0 => no further analysis is undertaken, else:
19
If 0 there is a Liquidity Gap in non-
residents cash flow;
If 0 the Liquidity Gap in non-residents
cash flow cannot be covered by domestic currency liquid assets;
If 0 there is a Liquidity Surplus in non-
residents cash flow.
(2.28)
(2.29)
Where:
“NR” is non-resident
“CA” is the custody abroad => liquid asset custody is not in Mozambique
“Total Liquid Assets” = Liquid Assets (custody abroad) + Liquid Assets (custody in
Mozambique)
" " is the Liquidity Ratio for non-residents
" ” is the Expanded Liquidity Ratio for non-residents
(iii)LCR Stress Scenario
LCR Scenario approach estimates the amount of liquid assets needed to survive for 30 days in a systemic
and idiosyncratic stressed environment. According to BCBS (2013 para. 144) recommendations, even banks
with negative liquid cash flows (inflows < outflows) are expected to maintain a minimum level of liquid
assets equal to 25 percent of the total cash outflows. An adjustment to the LCR is done to test a sudden
withdraw of one to a hundred of the top 100 deposits (demand and time deposits). To calculate the amounts
of the stress components (liquid assets, outflows and inflows), Stressed Amount is summed by Item.
20
; 25% ∗ (2.30)
%
(2.31)
Where:
" %"is the Liquidity Coverage Ratio
2.3 Comparison of the BdM’s Framework with the Original Čihák Model
The current BdM’s stress testing framework covers four individual risk factors, namely credit risk, foreign
exchange, interbank contagion risk, and liquidity risk. Though possible to simply determine the impact of
interest rate changes on NII using maturity gap analysis, the efforts of the BdM were towards assessing the
impact of interest rate changes on capital using the Macaulay duration, more specifically the modified
duration. However, the quality of the data provided by the banks on bonds structure prompts BdM to make
significant approximations on portfolio maturities, which ends up producing results that need to be
interpreted very carefully.
On the risks to solvency that are tested, the most relevant difference from the original Čihák’s model stems
from the inclusion of Worrel’s concept of gradually increasing the magnitudes of shocks to put the system
on a trajectory to reach its breaking point as a means to visualize the inflection point as stress intensifies.
Further, in the BdM’s framework profits are not taken into account as the first line of defence before eroding
the banks’ capital; data on collateral value is not easily available, and when available it is not reliable, which
vindicates the application of 100 percent LGD on the credit risk stress test. Moreover, provisioning
regulation is not taken into account in the assumptions that are considered for credit risk stress test, i.e.
performing loans (Class I and Class II) are assumed to be provisioned at 0 percent rate, while non performing
loans, regardless of their classes are provisioned at 100 percent level. With regard to the foreign exchange
risk, the severity of the indirect shock, i.e. the severity of the effect of foreign exchange rates changes on
foreign positions taken by borrowers, is highly dependent on the magnitude of the actual NPLs. Furthermore,
21
hedged foreign currency denominated borrowers (e.g. exporters) will keep performing in case of local
currency depreciation. This means that if banks mostly have hedged foreign currency denominated
borrowers, the indirect effect will be on the domestic currency denominated portfolio as the depreciation
will result in more expensive imports and increasing prices.
The current BdM’s stress testing framework does not cover combined scenarios, which are explained in the
Čihák’s model.
Another major difference between the BdM’s stress testing framework and the Čihák’s model is on the
liquidity risk stress testing. The BdM’s framework measures the banks’ ability to withstand a liquidity run
during a period of 30 calendar days based on three scenarios of stress: (i) foreign currency stressed cashflow,
(ii) non-resident customers’ stressed cashflow and (iii) a proxy for the stress scenario for LCR, which
includes a test for large deposit concentration risk. For the LCR, Basel metrics or some proxies (due to data
limitations) are considered after classifying each balance sheet item as liquid asset, inflow, or outflow. In all
scenarios it is determined a liquidity ratio of unencumbered liquid assets over stressed cashflow, which
should be equal or over 100 percent to indicate bank’s ability to face the adverse scenario under test. This
models differs from the Čihák’s one on the metrics considered and on the time horizon of the test. Čihák
provides a tool to analyse the survival period considering an outflow of deposits and the liquidation of liquid
assets.
From the aforementioned BdM’s credit risk sensitivity analysis is limited in several ways. Required data are
not easily available and part of the assumptions used make the results of the test less realistic; besides, the
instant shocks considered assume that the market agents do not change their behaviour in the light of a crisis,
which in reality is usually not valid. However, since the BdM’s framework was implemented in a modular
manner, implementing a satellite model to translate macroeconomic shocks into an impact on financial risks,
mainly the credit risk, would contribute to bring more plausibility to the exercise.
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3 STRESS TESTING FRAMEWORKS IN SELECTED MEFMI COUNTRIES
3.1 Introduction
This section highlights the stress testing practices of the Central Bank of Swaziland (CBS), Bank of Uganda
(BOU) and Reserve Bank of Zimbabwe (RBZ) with the purpose of exploiting aspects that could improve
the BdM’s experience.8 In particular, the section starts by describing the micro sensitivity tests implemented
by CBS and RBZ, and the credit risk macro scenario stress test implemented by BOU, and finishes by
highlighting the possible enhancements that could be adopted by BdM.
3.2 Swaziland and Zimbabwe – Sensitivity tests
Stress testing in Swaziland and Zimbabwe is a supervisory tool that complements other tools used by these
central banks such as the risk-based supervision, CAMELS rating system, and off-site monitoring. It is based
on Čihák’s framework, and tests the impact of stressed variables on the solvency and liquidity of banks
based on ad-hoc shocks on balance sheet items that affect capital adequacy or regulatory liquidity
requirements, not establishing any link between banks losses and the macroeconomic environment. The risks
covered are credit risk, interest rate risk, foreign exchange risk and liquidity risk. Additionally, CBS and
RBZ conduct a combined instantaneous stress test by looking at the collective effect of a percentage of
performing loans (PL) becoming NPL, depreciation of the local currency (Emalangeni, for Swaziland only)
by a certain percentage, and an increase by a certain percentage of interest rates CBS (2012) and RBZ (2011).
(i) Credit risk stress testing
Under credit risk, CBS and RBZ simulate the following common shocks: a deterioration in quality of
impaired loans; and a sudden migration of a percentage of PL to NPLs categories. CBS also shocks large
exposures; and industries and/or sectors, while RBZ also simulates a general downgrade of a percentage
of loans by one grade.
8 Efforts to get other countries stress testing frameworks encountered reluctance from the respective authorities.
23
The shock levels considered by both central banks are shown in Table 3.1.
Table 3.1. Calibration of credit risk shocks
Shocks Minor Moderate Major CBS RBZ Deterioration in quality of NPLs
5% 10% 20%
A sudden migration of PL to NPL/downgrade of a percentage of loans
5% 10% 20%
Impairment of large exposures
Default of government debt
Source: CBS Stress Testing Guidelines, 2012 & RBZ Stress Testing Framework, 2011
In both central banks, minimum provisioning requirements for the different loan categories are taken into
account on the determination of additional provisions resulting from each shock. However, profits are not
taken into account, which means that any increase in provisions directly reduces capital that in turn results
in a deterioration of a bank’s capital ratios.
Equation (3.1) is used by both central banks to determine additional provisions resulting from deterioration
in quality of the three categories of loan impairment.
∗ 1 % ∗ ∗
1 % ∗ ∗ 1 % ∗ , 0 (3.1)
On the migration of loans, CBS assumes that a percentage of all performing loans become special mention.
Special mention loans become substandard, and all NPLs migrate to the next category of impairment (special
mention to substandard, substandard to doubtful, and doubtful to loss). CBS expresses this stress using
Equation (3.2).
∗ % ∗ ∗ % ∗
∗ % ∗ ∗ % ∗
, 0 (3.2)
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RBZ measures a general downgrade of a percentage of loans by one grade using Equation (3.2) and, on the
other hand, a sudden increase in NPLs caused by a direct shift from PLs (sum of pass and special mention
loans) using Equation (3.3).
∗ %∗ ∗
∗ %∗ ∗ ∗
%∗ ∗ , 0 (3.3)
For the large exposures test, CBS assumes the impairment of one or more large exposures that absent the
stress are classified as PLs. The variations in implementation would reflect particular concerns that CBS
may have.
The RBZ also assesses the effect of a certain percentage default on government debt. Shocks levels are
defined according to RBZ’s assessment of the current sovereign debt management situation. The amount of
loss is calculated using Equation (3.4).
%*(Treasury bills+Investments in Other Government Securities) (3.4)
In both central banks, the additional provisions arising from the stress tests described above are fed into the
capital to risk-weighted assets ratio calculation using Equation (3.5). For the Zimbabwean government
default shock, capital adequacy ratio is determined using Equation (3.6)
∗ 100 (3.5)
∗ 100 (3.6)
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(ii) Interest rate risk stress testing
To measure the interest rate risk on banks’ balance sheets, CBS and RBZ have adopted the repricing gap
model through the distribution of interest sensitive assets and liabilities into buckets according to their time
to re-pricing. These central banks use the gap between assets and liabilities in each bucket to determine the
NII exposure to interest rate changes. The shock factors they use to calculate sensitivity of income to changes
in interest rate are shown in Table 3.2.
Table 3.2. Calibration of interest rate risk shocks
Shock Level CBS RBZ Low +/- 1% +/- 3% Moderate +/- 2% +/- 5% High +/- 5% +/- 10%
Source: CBS Stress Testing Guidelines, 2012 & RBZ Stress Testing Framework, 2011
Using Equation (3.7), CBS and RBZ assess the impact of changes in interest rates on earnings, for the one
year cumulative repricing gap. And for the impact on capital and capital adequacy, they use Equations (3.8)
and (3.9).
% ∗ 12 (3.7)
%∗ (3.8)
∗ 100 (3.9)
The model of CBS and RBZ only look at the direct interest rate risk, not taking into account the impact that
an increase in interest rates is likely to have on credit.
26
(iii)Foreign exchange rate risk stress testing
To stress test foreign exchange risk, CBS assesses the direct impact of exchange rate movements on each
bank’s net open positions. To model the impact of foreign exchange rate risk the overall net open position
of one or more currencies is separately assessed using the shocks shown in Table 3.3.
Table 3.3. Calibration of foreign exchange rate risk shocks
Low level shock 5% Moderate level shock 10% High level shock 15%
Source: CBS Stress Testing Guidelines, 2012
The impact of shocks in Table 3.3 on income and on capital adequacy is assessed using Equations (3.10)
and (3.11).
% ∗ (3.10)
%∗ ∗ 100 (3.11)
Loss stemming from appreciation of the Emalangeni when a bank has surplus of assets over liabilities in
foreign currencies is not assessed. Moreover, the effect of foreign exchange rate changes on foreign positions
taken by borrowers or counterparties is also not taken into account9.
(iv) Liquidity risk stress testing
CBS liquidity risk stress testing assesses the banks’ resilience to funding liquidity by determining a ratio of
liquid assets over short term liabilities based on four shocks: a sudden withdrawal of top depositors; a sudden
termination of contractual long-term deposits; a sudden non-availability of credit line; and a decline of the
9 This would serve to stress test the indirect foreign exchange risk.
27
deposit base across the banking sector. These shocks are separately assumed using the calibration shown in
Table 3.4.
Table 3.4. Calibration of liquidity risk shocks
Shocks Minor shock Moderate shock Major shock Sudden withdrawal of top depositors
top 5 depositors
top 10 depositors Top 20
depositors
Sudden termination of contractual long-term deposits.
10% 15% 30%
Sudden withdrawal/non-availability of credit lines
20% 50% 75%
A decline of the deposit base across the banking sector
10% 20% 50%
Source: CBS Stress Testing Guidelines, November 2012 According to local regulations Savings and Development Banks are expected to maintain a minimum level
of liquid assets equal to 17 percent of total liabilities to the public, while all other banks are expected to
maintain a minimum level of liquid assets equal to 20 percent of total liabilities to the public. So, the liquid
liabilities coverage ratio under the four shocks is assessed in light of these minimum liquidity requirements
using Equation (3.12).
%
∗ 100 (3.12)
The Zimbabwean liquidity stress test model considers the effect of both a systemic and an idiosyncratic
liquidity crisis on banks. It shocks a daily run-off of deposits, and the assessment of a banking institution’s
resilience to the liquidity risk shock is done considering the existence of a stock of liquid assets than can
sustain it for at least 5 calendar days. Table 3.5 shows the shock levels on liabilities daily run-off rates
applied.
Table 3.5. Liquidity risk shock levels
Deposits shock Minor shock Moderate shock Major shock Demand 2% 3% 5% Savings 2% 3% 5% Interbank 5% 10% 20% Other 1% 1% 1%
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Source: RBZ Stress Testing Framework
The assessment assumes a static balance sheet with liquid assets being considered with different haircuts,
based on the nature of the crisis i.e. bank specific or systemic. From day one, the model assesses if deposits
held and available liquid assets satisfy the Equation (3.13). On subsequent days remaining deposits and
liquid assets are considered.
% ∗ % ∗ % ∗ % ∗ 0 (3.13)
Where p, q, r, and s are the shock levels for minor, moderate and major shocks.
Stress testing frameworks of CBS and RBZ are similar, and cover interest rate risk and combined scenarios,
which are not covered in the BdM’s framework. However, the stress testing frameworks of CBS and RBZ
do not cover contagion effects and do not consider the indirect effects of changes of foreign exchange rate
and interest rate on positions taken by borrowers or counterparties. Furthermore, these frameworks do not
establish a link between banks’ losses and the macroeconomic environment.
3.3 Uganda – From sensitivity analysis to stress tests based on scenarios
BOU carries out quarterly stress tests to assess the resilience of the banking sector to systemic risks. The
Commercial Banking Department of BOU uses sensitivity stress tests based on Čihák’s model to assess
credit risk, interest rate risk, and foreign exchange risk. The shocks included in these sensitivity stress tests
are: increase in non-performing loans; 100 percent loan loss of each bank’s largest borrower; decline in net
interest margin; decrease in interest income on government securities; and depreciation of the Ugandan
Shilling against the US dollar. The Worrel’s concept of applying incremental magnitude of shocks to
selected variables until banks fail to meet minimum requirements is used in these sensitivity stress tests.
Since July 2013, BOU introduced, through its Financial Stability Department, a macro-financial stress
testing framework that can be used for both micro- and macro-prudential purposes. This stress testing
framework is modular, and comprises three-pillars: the first pillar is the scenario design, which involves the
29
design of the macro-financial scenario to be imposed on the Ugandan banking sector; the second pillar is
the credit risk satellite model, which translates the scenarios designed on the first pillar into variable
affecting the banks’ loss absorption capacity; the third pillar is the balance sheet module, which applies the
projected losses derived from the satellite model to individual bank balance sheets with the objective of
calculating the resulting impact on each bank’s solvency position BOU (2013). Figure 3.1 shows an
overview of the BOU’s macro-financial stress testing framework.
The description below focus on the BOU’s macro-financial stress testing framework for credit risk.
Figure 3.1: A schematic overview of the BOU's stress testing framework
Source: Background paper on macro stress testing at BOU, pp. 4
(i) Pillar 1 - Designing a macro-financial scenario and shock calibration
The first part of the BOU’s framework is the design of a macro-financial scenario, which serves as a basis
for defining a set of adverse macroeconomic shocks to apply to the banks. In its paper on macro stress
testing, BOU considered a “price shock”, where effects of an increase in international prices on food and
energy are assessed in terms of direct impact on economic output through increased commodity prices and
production costs, which in turn triggers domestic inflation to rise above the policy target, causing interest
rates to increase, and as a consequence of the rising interest rates credit defaults soar, generating a severe
credit tightening, as supply falls far below demand.
30
After mapping external shocks to systemic risks, BOU uses random shifts in the relevant economic or
financial variable to calibrate shock sizes. Using the calibrated shocks as inputs, BOU generates the macro-
financial scenario using the Economic Research Department’s macroeconomic models. The output of these
models is a link between the external shocks and a range of country-specific macro-financial variables.
(ii) Pillar 2 – Estimating a satellite model
The BOU’s satellite model is a set of equations that translates the generated macroeconomic scenario into
an impact on banks’ risks, focusing on those developments that can adversely affect the banking sector, and
on credit growth.
The set of equations of the BOU’s satellite model consists of a small macro model and a micro data-base
model for banks, which uses outputs from macro models built for Ugandan monetary policy forecasting
purposes. Quarterly banking sector data series, which spans a period from first quarter 2000 to the reporting
quarter, is employed. The analysis focuses on the following variables: private credit, deposits, real GDP
growth, inflation, banks’ average lending rate, the 91-day treasury bill rate (as a proxy for the monetary
condition), and the NPL ratio. The target forecast variables are the aggregate profitability and solvency of
the banking sector. The model measures profits by the aggregate after-tax earnings of the banking sector,
and solvency in terms of capital adequacy and buffers against losses.
The credit model:
BOU’s credit satellite model has two key equations: real credit growth and aggregate credit risk. The
equation used to model real credit growth considers both the demand and the supply sides of the credit
market. In estimating the credit growth model, BOU puts emphasis on obtaining the relationship between
credit risk, represented by the NPL ratio, and selected domestic macroeconomic indicators in an effort to
link the model to the results of BOU’s macroeconomic forecast. Specifically, credit growth rate cg is
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explained by real GDP annual growth rgdp, the NPL ratio npl, the average lending rate lr, annual growth
rate on deposits depg, and the quarterly change in annual inflation ∆ .
∆ (3.14)
On the demand side, BOU uses GDP growth, since it assumes that higher GDP growth translates into higher
credit growth. The use of lagged GDP growth in the regression is to avoid the problem of reverse causality,
namely higher credit growth leading to higher GDP growth.
On the supply side, BOU considers deposit growth, inflation, NPL ratio and the average lending ratio.
Expectation is that higher deposit growth would lead to more credit availability as banks would have more
funds. Moreover, inflation is expected to be detrimental to real credit growth. Average lending rate is
expected to lower credit growth should monetary policy stance be tight. BOU uses a lagged 91-day treasury
bill rate because there usually will be a lag for monetary policy decisions to take effect and there may also
be reverse causality problem.
The equation BOU uses to model aggregate credit risk takes NPL ratio as the dependent variable. The NPL
ratio is then expressed as a function of a linear combination of lagged real GDP growth and nominal interest
rates. The model assumes that a decline in economic activity and a rise in nominal interest rates would lead
to a rise in credit defaults.
(3.15)
BOU defines the banks’ lending rate lr as a function of the interest rate on the 91-day treasury bill and
the lending margin. The lending rate in a given period depends on contemporaneous changes in the policy
rate and one period lag of the same, and also on its deviation from observed changes in the lending margin.
(3.16)
32
(iii)Pillar 3 – Analysing banks’ solvency using balance sheet implementation
After scenarios have been fed through the satellite model, BOU calculates individual bank solvency
positions using granular information about the balance sheets and income statements of the banks included
in the analysis using the Čihák model. Key balance sheet items projected include cash and balances with
central bank, holdings of government securities, loans and deposits. In the basic Čihák model the balance
sheets are static, so BOU does not take into account the effect of any expected future capital injection.
However, it takes into account previous interest income on NPL when calculating the NII. End-horizon
solvency ratio is calculated as the summation of the existing capital stock and earnings accumulated over
the stress test period in relation to the end-horizon risk-weighted assets.
33
4 LITERATURE REVIEW
4.1 Introduction
This section reviews the literature on stress test methods used to link macro-financial drivers of stress with
bank-specific measures of risks, which is the main risk for the Mozambican banking system. It explores the
possible link between Čihák’s microprudential stress test framework with macroeconomic variables through
the use of credit risk “satellite” models.
4.2 The Stress Testing Process
The second half of the last decade has witnessed the development of various macro stress testing frameworks
by central banks and supervisory authorities. Geršl, et al. (2013) describe the stress testing framework used
at the Czech National Bank to test the resilience of the banking sector. Foglia (2009) presents a detailed
overview of how central banks and supervisory agencies have built their own stress testing tools in order to
assess the stability of the financial system. The paper from Allesandri, et al. (2009) proposes a quantitative
framework for modelling systemic risk through banks’ balance sheets and allows for macro credit risk,
interest income risk, market risk, network interactions, and asset-side feedback effects. Åsberg &
Shahnazarian (2009), Duellmann & Erdelmeier (2009), and Simons & Rolwes (2009) present models linking
macroeconomic variables and the default behaviour of banks. Breuer, et al. (2009) present a methodological
paper on choosing scenarios that satisfy the BCBS’s requirement of plausibility and severity.
In all of these frameworks stress testing is seen as a multi-stage process with four steps as shown in Figure
4.1.
34
Figure 4.1: A typical macro scenario stress testing process
Source: Adapted from Čihák (2007)
4.2.1 Designing the Macroeconomic Stress Scenario
A considerable challenge in stress testing is the choice of a plausible, severe, and suggestive of counteraction
scenario (or stress event). Breuer, et al. (2009) suggest that there is a need of replacing the traditional hand-
picking of scenarios with a systematic worst-case search over a given region of plausibility.10
Čihák (2007) distinguishes between two ways how to design a consistent scenario. The first way is the
“worst case” approach that answers the question which scenario has the worst impact on the financial system,
with a given level of plausibility. Alternatively, there is the “threshold approach", which for a given impact
on the system answers what is the most plausible scenario that would lead to that impact. Level of plausibility
can be set according to historical observations. Alternatively, scenarios can be drawn from a data-generating
process or some variables can be set expertly.
10 For details about the systematic worst-case search approach reference is made to the work by Breuer et al. (2009).
35
Extreme historical observations are easy to communicate and to implement. Under historical scenarios the
behaviour of the market participants can be more properly estimated, because their behaviour could be
similar to that observed in the past. Also, the historical scenarios are severe but plausible, as they have
already happened in the past. One direct option for utilising historical data is to plot observed risk factors
against selected measure of financial system soundness (e.g., CAR, NPLs) and to pick the most adverse
combination of the risk factors. This method can, however, lack consistency as the identified most stressful
observations can be from completely different historical periods. The main disadvantage of using the
historical scenarios is that it is uncertain that the same situations would repeat in the future.
For developing scenarios through a data-generating process, Drehmann (2008), identifies three main
methods that can be employed: (i) a structural macroeconomic model, (ii) vector autoregressive (VAR)
models or vector error correction (VEC) models, and (iii) pure statistical approaches. For communication
purposes macroeconomic models are more suitable because they can show important macroeconomic
transmission channels, but can be relatively complex. On the other hand, autoregressive models do not
include interdependences of the systemic risk factors and, as Van den End, et al. (2006, p. 3) argue, do not
provide the economic foundation structure of the scenario. However, the choice of the model depends on
the objectives of the stress test and on the systematic risk factors that are assumed.
Most of the stress testing approaches developed11 since the latter half of the last decade use an existing
structural macroeconomic model (i.e. one used by the central banks for forecasts and policy analysis) to
project the levels of key macroeconomic indicators under the stress conditions assumed. In this regard, a set
of initial shocks are taken as exogenous inputs, and their interactions with the other macroeconomic
11 The BOU macro stress testing framework described in section 3 is one example in the MEFMI region.
36
variables are projected over the scenario horizon. The simulations produce a range of economic and financial
variables as outputs, such as GDP, interest rates, the exchange rate, and other variables.12
Sometimes macroeconomic models are not available or are not considered feasible to generate consistent
relevant shocks. In those cases VAR or VEC models are employed, which as presented by Åsberg &
Shahnazarian (2008) are flexible and relatively simple, but lack the economic structure that is incorporated
in the macroeconomic modelling approach. In these models, a set of macroeconomic variables are jointly
affected by the initial shock, and the vector process is used to project the stressed scenario’s combined
impact on this set of variables. As presented by Foglia (2009), these models were used in the early studies
developed at the central banks of England, Spain, and the Netherlands as well as the European Central Bank.
In contrast to structural macroeconomic and VAR/VEC models, the Austrian Central Bank, based on
theoretical work by Elsinger, et al. (2006) and Boss (2002), developed a pure statistical approach to scenario
design – the Systemic Risk Monitor (SRM). However, according to Schmieder, et al. (2011), certain
limitations (e.g. a limited time horizon, partial coverage of the consolidated Austrian banking system) led
the Austrian Central Bank to switch to a macro stress testing approach more in line with general practice by
other central banks and supervisory authorities.
4.2.2 Designing the Credit Risk Satellite Models
Both the structural macroeconomic and the VAR approaches require a method to translate macroeconomic
variables into indicators that can be used to estimate the implications of the stress scenario for banks’ balance
sheets. Macroeconomic models usually do not include a measure of credit risk, so the second stage of a
12 Often macro models do not include key financial variables such as credit growth or asset price behaviour in their specification. However, these variables are typically found to be significant in explaining credit quality. An example of such “satellite” macro model is proposed by BOU (2013). Credit growth is modelled as a function of real GDP annual growth, the NPL ratio, the average lending rate, annual growth rate on deposits, and the quarterly change in annual inflation. Lending rate, in turn, is determined by the interest rate on the 91-day Treasury bill and the lending margin.
37
stress testing process normally involves estimating satellite models that link a measure of credit risk to the
macroeconomic model variables, therefore mapping exogenous shocks onto banks’ asset quality shocks.
In these credit quality models, loan performance measures are typically related to measures of
macroeconomic conditions. In one of the initial studies employing a satellite model, Blaschke, et al. (2001)
present an example in which NPL ratio is regressed against the nominal interest rate, the inflation rate, the
change in real GDP, and the change in the terms of trade. The coefficients of the regression provide an
estimate of the sensitivity of loan performance to those macroeconomic factors. Hoggarth, et al. (2005)
employ a VAR in order to capture the link between the UK banks’ fragility (expressed as the loan write-off
rates) and the GDP gap. Their results suggest that in the event of a GDP decrease, both corporate and
aggregate write-off rates increase substantially. Pesaran, et al. (2006) and Alves (2004) design a global VAR
and co-integrated VAR models, respectively, in their cross-country studies. Pesaran et. al. (2006) examine
how strongly corporate default probabilities are correlated with the nation-wide and international economic
cycles, and arrive at the conclusion that the impact of adverse shocks on expected losses is non-symmetric,
thus highlighting the non-linearity in the estimated model.
It is assumed that loan performance is sensitive to the economic cycle. The estimation strategy normally
requires the selection of an initial set of macroeconomic and financial variables that, according to theory and
empirical evidence, affect credit risk. Variables such as economic growth, unemployment, interest rates,
equity prices, and corporate bond spreads contribute to credit risk. In particular, interest rates are a crucial
variable, as they represent the direct cost of borrowing. Among alternative specifications, the preferred one
is selected on the basis of the consistency of macroeconomic variables with economic theory, i.e. the
variable’s sign has to be “right”, otherwise it is dropped; and on the specifications’ goodness of fit (Foglia,
2009).
38
A satellite model treats the macroeconomic variables as exogenous and therefore ignores the feedback
effects from a situation of distress in the banking system to the macroeconomy. This is one of the many
limitations of traditional stress testing (Castrén, et al., 2008).
Unlike the macroeconomic model, the credit risk satellite model can be estimated on data for individual
banks and even individual borrowers. Applicable modelling techniques depend mostly on data availability.
Čihák (2007) divides approaches into two categories: one based on data on loan performance, such as NPLs,
loan loss provisions (LLPs), and historical default rates; the other based on micro-level data related to the
default risk of the household and/or the corporate sector. The ultimate objective of many credit risk models
developed so far is the estimation of the credit portfolio (or aggregate) loss distribution, which summarizes
its overall risk profile and permits a thorough assessment of the impact of a shock.
(i) Models based on loan performance
In this approach the key dependent variables are the NPL ratio, the LLP ratio, and historical default
frequencies. As described in Foglia (2009) and shown in Table 4.1, these models include various
macroeconomic factors, ranging in a number from two to five depending on the country. In some cases
variables more directly related to the creditworthiness of firms are added, such as measures of indebtedness;
in other cases, market-based indicators of credit risk, such as equity prices and corporate bond spreads, are
also used.
Depending on the availability of data, models based on loan performance data can be run at the aggregate
level, at the industry level, or even at the level of individual banks.
Marcucci & Quagliariello (2008) model credit quality using observed default frequencies at the
household/corporate level of aggregation. Aggregate data allow Marcucci and Quagliariello to use a VAR
approach to estimate a satellite credit risk model, while previously VAR models had been used in the first
39
stage of the stress testing process.13 Their model for the corporate sector includes the default rate and four
macroeconomic variables (output gap, inflation, short-term interest rate, and real exchange rate). In the
identification scheme, the default rate is assumed to be exogenous to the output gap and all the other
macroeconomic variables. The impulse-response functions indicate significant impact of the various
macroeconomic variables (except inflation) on the default rate.
The credit risk models of Lehmann & Manz (2006) and Van den End, et al. (2006) use the LLP ratio to
measure credit quality at the individual bank level, with panel data estimation. The panel estimation of
individual banks’ LLPs controls for individual bank characteristics that affect credit risk and captures the
banks’ different sensitivities to macroeconomic developments.
Vazquez, et al. (2010), Fiori, et al. (2009) and Jiménez & Mencía (2007) model historical default rates
clustered by industry. The sectoral breakdown allows the use of different macroeconomic variables to
explain default frequencies in different industry sectors and the inclusion of sector-specific explanatory
variables to improve the goodness of fit.
In such models, macroeconomic variables that are found to be significant for many sectors represent the
systematic risk component; intersectoral default correlation is due to the common dependence on the
systematic component. The idiosyncratic risk component is measured by potential sector-specific variables
and/or by the residuals of the sectoral equations. When systematic risk is taken into account, default events
should be independent, and the cross-equation residuals should be uncorrelated (conditional independence).
If that is not the case, macroeconomic factors do not fully explain the default correlations across sectors; an
important implication is that a portfolio’s credit risk can be significantly underestimated (Foglia, 2009).
13 Åsberg & Shahnazarian (2008) also use a similar approach, estimating a VEC model (as described in subsection (ii) of the current section)
40
According to Foglia (2009) the use of loan performance data to measure credit quality raises some important
questions: loan performance is a lagged indicator of asset quality, in that it reflects past defaults; loan loss
provisioning rules may vary across jurisdictions, and legal protocols may determine whether or not
institutions actually write-off nonperforming loans or keep them on their financial statements with
appropriate provisioning; variations in loan loss provisions, in addition, may be only partly driven by
changes in credit risk; other bank-specific factors, such as income-smoothing policies, might also come into
play.
Another frequent problem in interpreting macroeconomic models of credit risk concerns the use of linear
statistical models: the linear approximation may be reasonable when shocks are small, but when they are
large, nonlinearities are likely to be important. As Van den End, et al. (2006) argue, nonlinear
transformations of the default rate extend the domain of the dependent variable to negative values and take
into account the possible nonlinear relationships between macroeconomic variables and the default rate that
are likely in stress situations.
To address nonlinearities, most of the studies reviewed here, following Wilson (1997) have used nonlinear
specifications, such as the logit and probit transformation, to model the default rate.
(ii) Models based on data for individual borrowers
In this approach the credit risk satellite model is estimated on individual borrower data. In this case, the
model specification may also include macro-financial data as explanatory variables. When no
macroeconomic variables are included, an additional satellite model may be used to link the macro-financial
variables to borrower-specific data.
Using a database of yearly accounting data for all limited liability companies in Norway, Eklund, et al.
(2001) relate the probability of default to borrower characteristics such as firm age, size, industry, and
accounting variables measuring corporate earnings, liquidity, and financial strength. In this model, the
41
projected figures for the main macroeconomic variables are used to estimate the future income statement
and balance sheet of each company and on this basis to calculate individual probabilities of default (PDs).
These PDs are then aggregated to estimate the banking sector’s total loan loss.
Individual measures of credit quality can be exploited to estimate a direct relationship with macroeconomic
variables. Castrén, et al. (2009) and Åsberg & Shahnazarian (2008) use Moody’s KMV14 expected default
frequencies (EDFs) to model the average credit quality of listed companies. The EDF is a forward-looking,
market-based measure of credit risk that gauges a firm’s probability of defaulting within a year, based on
the volatility of its share price.
In the paper by Åsberg & Shahnazarian (2008), the median EDF of all Swedish non-financial listed
companies proxies for the probability of default. The authors estimate a VECM for this aggregate EDF and
three macroeconomic variables (industrial production index, consumer price index, and short-term interest
rate). Assuming a long-term correlation between variables, a VECM can discern shared trends between
series as well as short-term fluctuations. The results indicate that the macroeconomic variable with the
strongest (positive) impact on EDF is the interest rate, and that a fall in manufacturing output and an increase
in inflation lead to a higher EDF.
The model by Castrén, et al. (2009) also measures credit risk by the median EDF of euro area companies,
but at the sector level (eight economic sectors). The model relates the credit quality of European companies
to five macroeconomic variables, including real equity prices, measured for the whole euro area; the
parameters are statistically significant and with the expected sign for real equity prices and, in four of the
eight sectors, for GDP.
The surveyed approaches to credit risk modelling in terms of measures of credit quality, level of aggregation,
and estimation methodology have one common feature, which is that the macroeconomic variables used as
14 Moody’s (Kealhofer, McQuown and Vasicek)
42
explanatory variables are not numerous. As for the level of aggregation, models based on individual data
can in principle lead to more accurate results, if these data are not available, there can still be benefits
associated with the use of models based on more aggregate data, as noted by Åsberg & Shahnazarian (2008).
4.2.3 Balance Sheet Implementation of Shock Scenario
In the third stage of the stress testing process, the macroeconomic models (structural, vector autoregressive,
or purely statistical) are used to calculate individual bank solvency positions using granular data about the
balance sheets and income statements of banks included in the analysis.
As noted, most of the studies reviewed used a macroeconometric structural model to design stress test
scenarios. According to Geršl, et al. (2013), as these models are calibrated and not estimated, confidence
intervals are not available and therefore the scenarios represent central forecasts given the shocks assumed
for the selected variables in the model. Conversely, the VAR/VECM framework can generate stress
scenarios that do allow for probabilistic interpretations. Shock sizes are specified in terms of the
unconditional standard deviation of the innovation in an autoregressive series, and under a normality
assumption they can be given a probabilistic interpretation. In consequence scenarios do not follow from the
economic reasoning behind a structural macro model but are based only on a probabilistic method. Tail
outcomes of such simulations present extreme scenarios (Foglia, 2009).
Pesaran, et al. (2006) were the first to present a VAR model to generate a probabilistic scenario for credit
risk analysis. In their study they use impulse-response functions to examine how an isolated shock to one
macroeconomic variable affects all the others. Impulse-response functions assume that the other variables
are displaced according to their historical covariances with the variable being shocked, so that the
correlations across shocks are accounted for in an appropriate manner. The authors examine the impact on
a hypothetical corporate loan portfolio and its exposure to a range of macroeconomic shocks. For example,
they find that a 2.33 standard deviation drop in real U.S. equity prices causes an expected loss of 80 basis
43
points over four quarters. This approach is particularly valuable in addressing specific risk management
questions and, in particular, producing a rank order of the possible shock scenarios.
In their study, Åsberg & Shahnazarian (2008) use the impulse-responses of the Swedish National Bank’s
macroeconometric model to a given shock (e.g., a supply shock) to estimate stressed values for the three
macroeconomic variables of a VEC model that also include EDFs. The VEC model is then used to forecast
the stressed EDFs conditional on the stressed values of the macroeconomic variables.
4.2.4 Impact Measures
The last step in the stress testing process is evaluating the impact on the banks’ loan portfolio and judging
whether banks can withstand the shock assumed. This means comparing the loss with an appropriate
benchmark. Three issues arise in this stage: the choice of the variable to measure the banking systems’
resilience to shocks, the estimation of a loan portfolio’s loss distribution, and the assessment of the impact
for the system-wide portfolio as well as at the level of individual banks (Foglia, 2009).
Depending on the satellite model used, the results of the simulation can be expressed in terms of either
provisions or projected default rates. In the latter case, given a (generally ad-hoc) figure for recovery rate,
one can estimate banks’ expected losses, which determines the volume of provisions to be set aside. As
observed in Čihák (2007), in a normal situation, banks would normally be profitable. When carrying out
stress tests, it is important to evaluate impacts against such a baseline, as banks would use profits as a cushion
before undergoing reductions in their regulatory capital position. However, to accommodate the views that
it is prudent to disregard profits, one can measure losses directly against capital.
As is noted by Bonti, et al. (2006), stress tests performed within a portfolio credit risk model enable one to
assess the outcomes of a stress scenario consistently with the quantitative framework used in a normal, non
stressed situation, because the stress scenario is translated into movements of internal risk drivers (the
macroeconomic risk factors). The risk measures of the model (expected loss, value-at-risk) can be studied
44
relative to the baseline simulation derived from the unconditional (non stressed) risk-factor distribution.
Using the same quantitative framework for normal and stressed situations implies that the relationships
between non stressed risk factors remain intact and the experience gained in the day-to-day use of the model
can be used to interpret the results from stress testing.
Finally, depending on the availability of micro data, it is important to calculate the impact at the individual
bank level and not only for an aggregate system-wide portfolio. In fact, this is where the link of credit risk
satellite models and the Čihák’s microprudential stress test framework can be implemented. The model by
Čihák (2007) is modular, and can be linked to structural macroeconomic or VAR/VEC models’ estimations.
45
5 METHODOLOGY AND DATA
5.1 Introduction
This section presents the study methodology and data used to build a satellite model for linking macro-
financial drivers of stress with credit risk in Mozambique. Firstly, the relationship between macroeconomic
and financial variables that supposedly affect banks’ loan quality to forecast their evolution over four
quarters is estimated. Secondly, the relationship between loan portfolio quality and the selected set of macro-
financial indicators is estimated. Lastly, resultant capital adequacy ratios for banks, taking into account
increases in NPLs, are calculated using the Čihák/Worrell template.
5.2 Credit Risk Macro Stress Testing in Mozambique
Taking into account the lack of data to proxy for probability of default for individual borrowers in
Mozambique, this study designs a satellite model based on loan performance data run at aggregate level,
using a VAR model with NPL ratio15 as the dependent variable. The scenario is based on historical events
of real GDP growth because it is relatively easy to communicate a behaviour similar to one observed in the
past. The projected aggregate losses derived from the satellite model are applied to individual bank balance
sheets using the Increase in NPL shock in the “Credit Risk” worksheet of the Čihák’s to determine the post-
shock capital CARs.
To arrive at a proper specification, the study considers the existing practices in credit risk stress testing
models by central banks presented in section four and the summary of other approaches presented in Table
4.1.
15 Among other proxies for the probability of default, the ratio of NPLs to gross loans remains the most canonical.
46
Table 5.1: Overview of credit risk models
Author(s) Country of
Analysis Per
iod
Methodology
Model Dependent Variable Independent Variable(s)
Festic, et al. (2011)
Central and Eastern Europe 19
95-
2009
Panel The first difference of the share of NPLs in gross loans
Deposit to loan ratio Net foreign assets to net
assets ratio FDI
Vazquez, et. al. (2010)
Brazil
2001
-200
9
Panel, time series, VaR
The logit-transformed default rate
ln ,
,)
NPLi,t – the ratio of non-performing loans to gross loans of industry i at time t
Lagged dep. var. Growth in real GDP Unemployment rate Interest rates
Nordal & Syed (2010)
Norway
1988
-200
8 Panel The logit-transformed debt-weighted default rate
ln )
DWPDt – debt-weighted probability of default at time t
GDP growth Real exchange rate Growth in household
wages Rise in house prices
Bank’s lending rate Loans-to-enterprises
index
Hoggart, et. al. (2005)
The UK
1988
-200
4
VAR The loan write-off ratio (write-off-to-loan ratio)
GDP gap CPI Nominal short-term
interest rate Change in REER
Pesola (2005) Nordic Countries
1983
-199
8
OLS Banks’ losses to outstanding lending stock
Lagged dep. var. GDP growth Indebtedness
(lending/GDP) Regulation dummy
Pesaran, et. al. (2006)
10 Regions / countries
1979
-199
9
Global VAR
Corporate probabilities of default GDP Inflation Short-term interest rates REER Real money balances Equity prices
47
Author(s) Country of
Analysis Per
iod
Methodology
Model Dependent Variable Independent Variable(s)
Virolainen (2004)
Finland
1986
-200
3
Panel The logit-transformed default rate
ln ,
,)
Pi,t – probability of default of industry i at time t
Real GDP growth Nominal short-term
interest rate The ratio of debt/value
added of a given industry
5.3 Modelling Approach and Specification
The study employs a three-stage approach in order to test the resilience of the Mozambican banking system
to adverse macro-financial scenarios through linkage with Čihák/Worrel template. Firstly, it is estimated the
relationship between the chosen macro-financial indicators to forecast their evolution over a four quarters
period. Secondly, the relationship between the banking loan quality and the macro-financial variables used
in the first stage is estimated, with determination of coefficients. Further, the study assesses how banks
would react to an adverse but plausible macro-financial shock over the four quarters by looking at the change
in the ratio of non-performing loans to total loans. Finally, new capital adequacy ratios for banks and the
banking system are calculated, from the Čihák/Worrel template, taking into account the increase in loan loss
provisions to account for the increase in NPLs.
(i) First stage:
The first stage in this methodological approach is estimation of the relationship between macroeconomic
and financial variables that supposedly affect the banks’ loan portfolio quality. To forecast their
development, the reduced VAR16 model that uses four variables (namely, quarterly real GDP growth qrgdpg,
credit growth cg, inflation infl, and BdM´s standing lending facility rate slfr)17 is used. The regression
16 This choice is justified by the fact that theoretical relations among the variables are unknown and there is suspicion that they may have feedback effects (MEFMI, 2012 pp. 87). In this modeling approach, each variable is expressed as a function of lagged values of itself and of each of the other selected variables.
17 These variables were chosen for illustration purpose. The recommended approach would be to use variables obtained from the country’s macroeconomic model or small scale macroeconomic model.
48
specification contains four lags of every explanatory variable, according to the suggestion of the LR, FPE,
AIC and HQ lag selection criterions (Appendix 2).
At the outset, there is an assumption of stationarity for the variables that enter the time series model. In a
nutshell, the assumption of stationarity means that the variance and mean of a series are constant over time
and the covariance of the series in different periods is zero (MEFMI, 2012). Subsequently, the canonical
Augmented Dickey-Fuller test for unit root in order to check for stationarity is carried out. The results show
that, except for infl, all the variables contain unit root and, thus, have to be differenced in order to obtain
stationary series. After differencing, these variables exhibited stationarity (Appendix 3).
Figure 5.1 is a plot showing the stationary series obtained.
Figure 5.1: Stationary macro‐financial variables
-40
-30
-20
-10
0
10
20
30
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
D(QRGDPG) D(CG)INFL D(SLFR)
Source: Author’s estimations
49
The model is run using the basic ordinary least square (OLS) regression of qrgdpg, cg, infl and slfr. Residuals
present no serial correlation, no heteroskedasticity, and the VAR stability check reveals a stable system
(Appendix 4).
Lastly, system-wide shocks are introduced to one of the variables of the model to forecast the behaviour of
others:
∑ ∑ ∑ ∑∑ ∑ ∑ ∑∑ ∑ ∑ ∑∑ ∑ ∑ ∑
(5.2)
(ii) Second stage:
In the second stage, panel data regression is used to estimate the relationship between probability of default
and the set of macro-financial indicators:
, 4 , (5.3)
Where:
, is the change in the ratio of NPLs to gross loans of bank i at time t
is the quarterly real GDP growth at time t
is the credit growth at time t
is the inflation at time t
is the BdM’s policy interest rate at time t
4 is the lag value of NPL of bank i
, is the white noise process, which is assumed to be independently and identically
distributed with a mean of 0 and a variance of 1.
50
(iii)Third stage:
At the last stage, using the predicted change in NPLs and potential judgemental assumptions, banks’ new
capital adequacy ratios are estimated and compared to the regulatory minimum of 8 percent using individual
banks’ balance sheets18. The following equation is used for this calculation:
∗ ∆
∆ (5.4)
Here, Capital is bank’s regulatory capital; RWA is risk-weighted assets; and ∆P are the expected additional
loan loss provisions after deducting the provisions in excess. It is calculated as ∆ = Max (0; ∆npl * Loss
given default – provisions in excess), meaning that the amount of the additional loan loss provisions that the
bank is supposed to make will equal to the amount of the loans that it does not expect to recover subtracted
by the existing provisions in excess. In the absence of recovery rate estimations in Mozambique, it is
assumed a conservative 100 percent LGD. When performing this calculation the study makes a conservative
assumption that a bank’s profit for the forecasted year equals zero, thus own capital is not increased by the
amount of profit, and the impact of the increase in NPLs on RWAs is 100 percent.
5.4 Dependent Variable
One of the most commonly used proxies for the probability of default is the ratio of non-performing loans
to gross loans (NPL ratio). By non-performing loans regulators and practitioners usually mean impaired
loans and loans whose repayments are 90 or more days overdue. The definition may vary from country to
country; however, the conventional notion recommended by IMF (2006) is 90+ days overdue. Originally,
this metric characterizes the loan quality of a lender: the higher the ratio, the worse the quality of his/her
loan portfolio. If the amount of non-performing loans increases, a bank has to increase its loan loss
provisions, allowance for doubtful and bad loans. However, given that LLPs are not comparable across
18 The predicted change in NPL is applied to individual bank balance sheets using the NPL increases shock of the Čihák’s microprudential framework.
51
banks like the NPLs, LLPs would capture less information about the overall loan quality. Thus, the study
finds it reasonable to employ the share of non-performing loans to the gross amount of loans as a proxy for
the probability of default.
In case of Mozambique, data on non-performing loans is very limited. Quarterly data on NPLs classified as
90+ days overdue is available from first quarter of 2014 as a result of the introduction of Notice
16/GBM/2013, of December 31. Quarterly data on NPLs classified as impaired loans is available from third
quarter 200819 to fourth quarter 2013. However, since data is missing for some banks, to include data for the
longest period possible and get the maximum number of observations, 15 banks that have balanced data on
NPLs from third quarter 2008 to fourth quarter 2013 were picked, leaving the study with 330 observations.
The list of the banks in the sample can be found in Appendix 1.
5.5 Explanatory Variables
There is consensus among economists that the probability of default is associated with changes in
macroeconomic and financial indicators (Hamerle, et al., 2011), (Festic, et al., 2011). As suggested by
Figlewski, et al. (2012), there are three groups of macro-factors that influence a firm’s/system’s
creditworthiness:
1. Factors related to general macroeconomic conditions (e.g., unemployment, inflation, etc.);
2. Factors characterizing real economy (e.g., growth of real GDP, terms of trade, etc.); and
3. Factors reflecting financial market conditions (e.g., interest rates, equity returns, etc.).
Most of the stress testing approaches reviewed use an existing structural macroeconomic model (e.g. one
used by central banks for forecasts and policy analysis) to project the levels of key macroeconomic indicators
under the stress condition assumed. For illustration purpose, the study’s specification uses the macro-
financial variables that most literature considers to be significant indicators of default probability, which are
19 In 2008, BdM has introduced the International Financial Reporting Standards for the whole banking sector.
52
the quarterly real GDP growth, credit growth, inflation, and changes in the benchmark interest rate (the
Standing Lending Facility Rate – SLFR – in the case of the BdM). These variables enter the VAR model in
order to generate forecasts over four quarters.
As findings of other research papers demonstrate, GDP growth is often amongst the most significant
determinants of NPL development, and it is expected to also hold in the Mozambican case, because in a
small economy GDP developments have a pervasive effects on corporate as well as household income. A
rapid credit expansion is deemed one of the most important causes of problem loans because, most of the
times, it is a result of a fierce competition for market share in loans and the integration of borrowers of lower
credit quality. As economic theory argues, increase in consumer prices compels monetary regulators to use
contractionary measures by increasing the interest rates to control inflation, which later increases the cost of
borrowing and ultimately causes NPLs to rise. The rise in the standing lending facility rate (policy
rate/SLFR), in its turn, can have a direct impact on borrowers’ probability of default as it increases the
burden they should return to a bank in the form of interest payments. However, decrease in this policy rate
does not consistently suggest a reduction in interest payments by the borrowers.
The data have been retrieved from the Research Department of BdM. All data series, spanning quarter four
of 2004 to quarter four of 2013, are seasonally adjusted and are collected at both monthly and quarterly
frequencies.
5.6 Expectations
Table 5.1: A priori author's expectation
Variable A priori Expectations Real GDP growth - Credit growth + Inflation + Interest rate + Lagged NPL ratio +
53
Quarterly real gdp growth (-): the higher growth in total output would increase corporates’ and
individuals’ financial conditions, thus leading to greater loans repayment rates.
Credit growth (+): high credit growth would generate higher NPLs.
Inflation (+): an increase in consumer prices would erode a part of the individual/corporate budget,
thus limiting the loan repayment ability.
Interest rate (+): higher interest rates mean higher interest payments on loans for borrowers;
therefore they should be positively related with NPLs.
5.7 Limitations
This is the first macro-financial credit risk stress test model being developed for the Mozambican banking
system. It is subject to several pitfalls. First and foremost, it fails to consider the full economic cycle because
of the inexistence of severe macroeconomic shocks in the data series available for NPL ratio. Second, it fails
to account for the nonlinear associations between the variables of interest. Third, it disregards the feedback
effects, which are unattainable due to data restrictions.
54
6 EMPIRICAL REVIEW OF FINDINGS
6.1 Introduction
In this section, the proposed methodological approach for macro-to-micro linkages is tested. First, impulse
response is used to show how selected independent variables affect NPL ratio. Subsequently, the VAR model
is used to forecast baseline and after shock end-of-period values of the independent variables for a historical
GDP shock assumed. Panel data regression is used to determine coefficients of the regressors. Lastly,
resultant capital adequacy ratios for banks, taking into account increases in NPLs, are calculated using the
Čihák/Worrell template.
6.2 Impulse Response of NPL Ratio
A positive shock to real GDP growth causes NPL ratio to fall from the first quarter, with the effect peaking
after three quarters. This supports economic theory that a higher GDP growth induces NPL ratio to decrease.
A positive shock on policy interest rates results in an increase of NPL ratio from the first quarter, with the
effect picking after quarter three. Also, a positive shock in inflation causes NPL ratio to increase from quarter
one. This result supports economic theory that an increase in consumer prices compels monetary regulators
to use contractionary measures by increasing the interest rates to control inflation, which later increases the
cost of borrowing and ultimately causes NPLs to rise.
The effect of an unexpected increase in credit growth on NPL ratio appears to contradict economic theory.
Supposedly, a rapid credit expansion is a result of a fierce competition for market share in loans and the
relaxation of credit underwriting standards, thus increasing NPLs. Figure 6.1 shows the impulse response of
NPL ratio to increases in the four independent variables considered in the study.
55
Source: Author’s estimations
6.3 After-shock Macro-financial Variables Development
The study implemented a historical scenario in order to test the resilience of the Mozambican banking
system. A dramatic fall in annual real GDP growth to 1.09 percent20 over the year 2014, with equal
performance during all the quarters was assumed. Figure 6.2 shows forecast values over the four quarters of
2013 using known data (from quarter four 2004 to quarter four 2012) to demonstrate the model’s forecasting
ability.
20 According to the World Bank database on annual GDP growth, the annual GDP growth of Mozambique in the year 2000 has been equal to 1.09% due to devastating floods. So the underlying scenario driving GDP growth down is a potential occurrence of the same historical magnitude.
-.004
-.002
.000
.002
.004
.006
1 2 3 4
Response of NPL to D(Q RGDPG )
-.004
-.002
.000
.002
.004
.006
1 2 3 4
Response of NPL to INFL
-.004
-.002
.000
.002
.004
.006
1 2 3 4
Response of NPL to D(CG)
-.004
-.002
.000
.002
.004
.006
1 2 3 4
Response of NPL to D(SLFR)
Figure 6.1: Impulse response of NPL ratio (increase in qrgdpg, cg, infl, and slfr)
56
Figure 6.2: Model forecasting ability using historical data
Source: Author’s estimations
The forecast figure of GDP growth at the end of 2013 is 5.7 percent, which is slightly above the actual value
of 5.3 percent. The same applies to the other variables: forecast figure of credit growth is 37 percent, which
is above the actual value (35 percent), inflations’ end-of-period forecast is in line with actual value with
huge divergence in quarter three 2013, and forecast of the BdM’s standing lending facility rate is 7.1 percent,
which is below the actual 8.4 percent.
Appendix 7 presents the end-of-period predicted values of the variables in both scenarios, baseline and after
shock. The figure of GDP growth at the end of 2014, according to expert forecasts by BdM, the IMF, and
World Bank, is 8.1 percent, which provides the baseline forecast value. Under the stress scenario (the abrupt
fall in the real GDP growth in 2014), credit growth would diminish to 17.1 percent, indicating an
unfavourable macro-financial situation; inflation would increase to 8.8 percent, as reaction to the downturn
in economic activity; and BdM’s policy interest rates would slightly increase to 8.3 percent. As a result,
-6
-4
-2
0
2
4
6
8
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
QRGDPG QRGDPG_FCAST
0
10
20
30
40
50
60
70
80
2 004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
CG CG_FCAST
-2
0
2
4
6
8
10
2 004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
INFL INFL_FCAST
6
8
10
12
14
16
18
20
2 004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
SLFR SLFR_FCAST
57
predicted NPL ratio of the banking system, under the shock scenario, is 15.74 percent (forecasted bank by
bank post-shock NPLs are presented in Appendix 7).
6.4 Results of the Panel Data Regression
The study estimated an equation with NPL ratio as dependent variable using data of 15 Mozambican banks
from third quarter of 2008 to fourth quarter of 2013. The chosen 15 banks are those ones that have balanced
NPL data during the period under consideration, and they comprise more than 94% of total Mozambican
banking sector’s assets as of 2013. The regressions were run using three different approaches. First, OLS
regression was used to estimate the coefficients of the independent variables. Secondly, taking an advantage
of panel data properties, fixed effects regression was run with individual fixed effects using least square
dummy variables (LSDV) regression. Lastly, cross-section random effects was used to estimate the
coefficients. As OLS may suffer from omitted variables bias, fixed effects approach was compared with
random effects using the Hausman Test, which suggested the choice of the random effects approach. The
cross-sectional random effects approach considers that all the banks have a common mean value for the
intercept.
Coefficients of the regressors are reported in Appendix 6. Taking into account the length of the data
considered (third quarter 2008 to fourth quarter 2013), the results of the regression are slightly in line with
the study’s expectations, since they suggest that the NPL ratio in the Mozambican banking system can be
explained to a certain extent by the variances of some of the variables selected for this study (as shown by
the results of the impulse response of NPL ratio in section 6.2). A priori expectation was that there should
be a close relationship between the variance of NPL ratio and the economic cycle: during recessions NPLs
should increase as a result of firms’ and households’ financial distress; and during periods of economic
growth, the income of non-financial firms and households would expand contributing to a decline in banks’
NPL ratio as a result of their better loans repayment ability. This expectation is met because a rise in real
GDP growth leads to a decline in NPL ratio, however the magnitude is marginal – a one percentage point
increase in GDP growth results in 0.0113 percentage point decrease in NPLs. Similarly, changes on inflation
58
and interest rate also showed a marginally positive impact on borrowers' ability to repay loans – a one
percentage point increase in inflation results in 0.0004 percentage point increase in NPLs, and a one
percentage point increase in interest rates results in 0.0043 percentage point increase in NPLs. Arguably,
these findings lend support to the observation that the study could not capture a full economic cycle21, and
management quality issues and some other microeconomic variables not considered here may play a
determining role.
Previous studies have found evidence that credit growth generates NPL increases, however, as shown by the
impulse response of the NPL ratio to credit growth changes, in this study this variable presented an
unexpected negative sign, therefore is not considered for further analysis. The coeffiencients and p-value of
lagged NPL ratio have a significantly positive impact on the NPLs, which shows inertia built on previous
NPLs. The model explains 75.7% of variation of the dependent variable.
6.5 Capital Adequacy
At the final stage of the analysis the resilience of the Mozambican banking system to the specified shock on
the quarterly real GDP growth is assessed using capital adequacy ratio (CAR) as a metric. According to the
Mozambican regulation, the minimum requirement for CAR is 8%, meaning that each bank’s core capital
expressed as a percentage of its risk-weighted assets should at least be 8%.
Using the coefficients acquired in the panel data regression of NPL ratio on forecast macro-financial
variables, the NPL ratio for the fourth quarter of 2014 for each bank and for the system aer calculated. Since
the model suffers from momentum built in the previous NPL ratio due to the fact that GDP growth, inflation
and interest rates have been demonstrated to lack substantial influence on the variations of NPLs, the
materialisation of the baseline scenario, according to which the macro-financial environment is expected to
develop in a favourable way, would lead to an increase in NPL ratio to 15.66%, which contradicts other
21 Appendix 5 depicts the behaviour of the study’s variables during the period under consideration (i.e. from fourth quarter 2004 to fourth quarter 2013).
59
research results and the study’s expectation. If, however, a shock in the form of a sharp decline in real GDP
growth to 1.09% materialises, NPL ratio is expected to rise to 15.74%. While impulse response results
confirm the adequacy of the VAR model, the lack of data covering a full economic cycle may be
undermining the model.
Next the effect of the new NPL ratios on banks’ CAR (Table 6.1) is studied. Using the formula described in
section 5.2 and existing banks’ regulatory capital, CAR, RWA and total loans data, as of the end of 2013,
the new CAR are generated for all banks in response to the new bank by bank NPLs from the section of NPL
increases of the BdM’s Čihák/Worrell model (“Credit Risk” worksheet).
The results of the CAR calculation indicate that only four banks: BIM, MCB, ECOBANK, and BOM can
withstand this shock scenario, which means that 64.2% of the total banking system assets would be
compromised. However, from the weakness of the simulation model (macroeconomic variables selected are
statistically not significant to explain NPL ratio variations), these results do not satisfactorily indicate the
overall resilience of the Mozambican banking system to a shock in the macroeconomic environment,
simulated by a huge decrease in the real GDP growth.
60
Table 6.1: Forecasted 2014 CAR by bank
Banks Capital CAR (%)
RWA Total loans (net)
Additional NPLs
Additional Provisions
CAR* (%)
BIM 10,843,049 22.6 47,955,172 46,369,393 6,579,271 5,030,906 13.5 BCI 4,434,937 11.6 38,251,747 43,841,780 9,941,607 9,731,859 -18.6 STB 2,774,174 10.4 26,626,058 18,150,118 1,795,546 1,672,427 4.4 BBM 1,525,312 12.8 11,957,421 7,183,052 1,129,239 956,516 5.2 ABC 802,361 16.4 4,885,219 4,698,939 1,123,759 1,123,759 -13.7 FNB 619,814 10.8 5,738,478 4,806,865 699,944 647,384 -0.5 CPC 192,909 13.6 1,417,599 1,286,406 96,090 84,485 4.0 MCB 472,197 38.6 1,222,752 1,095,787 152,047 152,047 26.5 ECOBANK 271,767 24.8 1,094,884 606,736 115,936 95,774 17. SOCREMO 166,932 19.9 838,169 684,945 101,041 101,041 5.3 CBM 129,262 60.8 212,537 142,531 110,623 110,623 -238.7 TCHUMA (83,277) -55.6 149,746 (22,462) 13,678 13,678 -85.7 BOM 76,505 26.4 290,202 179,616 31,951 26,674 18.9 BTM 21,907 1.8 1,220,006 643,784 351,073 351,073 -96.6 MOB 987,667 9.6 10,324,864 7,666,909 689,113 689,113 1.6 SYSTEM -8.4
Source: Austhor´s estimations
61
7 CONCLUSIONS AND RECOMMENDATIONS
7.1 Summary of Conclusions
After describing the current BdM’s micro sensitivity stress testing framework, this study reviewed the stress
testing practices of some selected MEFMI member countries and the existing literature on credit risk
determinants, and made a contribution by identifying areas of improvement of the current stress testing
framework and by modelling a link between the current Čihák/Worrell micro-prudential framework with
the macroeconomic environment through credit risk.
On the improvements to the current stress testing framework, the study found that, with the prevailing data
constraints, BdM could:
a) adopt the repricing gap model to measure NII exposure to interest rate risk, and use duration to assess
the impact of interest rate changes on the trading book;
b) assume an increase in NPLs on foreign currency denominated loans as proportional to the current
stock of foreign currency performing loans when NPLs are very low; or, since banks that mostly
have hedged foreign currency denominated borrowers will keep performing in case of local currency
depreciation, assume an increase in NPLs on domestic currency denominated loans as a result of
expensive imports and increasing real prices;
c) cover combined scenarios to assess the resilience of banks to multiple instantaneous shocks;
d) and improve “plausibility” of assumptions on solvency stress testing through consideration of profits,
cash collaterals, and the minimum provisioning requirements for the different loan categories on the
determination of additional provisions resulting from the various shocks that are currently applied.
Based on the review of the BOU’s macro-financial stress testing framework and existing literature on macro-
to-micro linkages, the study developed a methodology based on which some credit risk determinants were
chosen and justified for use in the Mozambican context. Subsequently, the study built a macro-financial
VAR model that allows the examination of relationships between macroeconomic and financial variables,
62
and make their forecasts for future periods. Thirdly, it used panel data of 15 banks, comprising more than
94% of total Mozambican banking sector’s assets as of December 2013, to identify how responsive the ratio
of NPLs to gross loans is to changes in macro-financial variables. Lastly, the study introduced a severe, but
plausible, shock into the system and checked how banks’ CARs react to it using the available BdM’s
Čihák/Worrell balance sheet model for stress testing. Empirical results showed that real GDP growth,
inflation and interest rates explain NPL ratio, but their coefficients inexpressive, and that credit growth
presented unexpected negative sign. Lagged NPL revealed the most significant coefficient, which is due to
inertia.
The analysis undertaken showed that the predictive power of the VAR, based on the credit determinants
chosen, is satisfactory, but other macroeconomic and, mainly, bank specific factors need to be considered to
test their influence on the behaviour of NPLs in Mozambique since NPLs data span from the third quarter
2008 to fourth quarter 2013 – period during which no major economic swings have been observed, which
the author believes has a pervasive influence on the outcomes of the model.
By performing each of the aforementioned, the study was able to get the answer to each of the research
questions stated in section 1: What is the BdM’s current micro-prudential stress testing framework?; How
do the BdM stress testing practices compare with regional practices?; and How does the literature suggest
to link the variables used in the micro-prudential stress testing framework with macroeconomic
environment?
7.2 Recommendations
Notwithstanding the fact that the overall macroeconomic outlook for the Mozambican economy is positive,
the importance of credit risk for the stability of the banking sector suggest that credit monitoring should stay
among top priorities in BdM’s prudential supervision. It is recommended therefore that, apart from
improving the current Čihák/Worrell based micro-prudential framework, BdM considers moving towards a
macro-financial model for credit risk stress testing.
63
Cognisant of the data limitation, BdM could improve the solvency side of its sensitivity stess testing
framework by:
a) adopting scenarios from the original Čihák’s model that were not considered in the current
implementation, namely by applying shocks to interest rate risk and by using combined scenarios to
assess the resilience of banks to multiple instantaneous shocks.
b) improving the “plausibility” of the assumptions used in the simulations by considering profits, cash
collaterals, and the minimum provisioning requirements for the different loan categories on the
determination of additional provisions resulting from the various shocks that are currently applied.
c) assuming an increase in NPLs on foreign currency denominated loans as proportional to the current
stock of foreign currency performing loans when NPLs are very low; or by assuming an increase in
NPLs on domestic currency denominated loans if banks mostly have hedged foreign currency
denominated borrowers.
On the other hand, BdM could use the improved Čihák/Worrel based stress testing methodology to transmit
the effects of macro-financial shocks, resulting from VAR and panel data regressions, to the banking system
by using the methodological model built in this paper.
7.3 Further Research
The developed approach for macro stress testing allows to study the relationships between banking system’s
credit risk and macro-financial variables and examine its resilience to adverse shocks. The study used the
adapted Čihák/Worrel stress testing methodology to transmit the effects of macro-financial shocks, resulting
from VAR and panel data regressions, to the banking system.
As the results of the VAR and panel regression indicated, future research could further explore relevant
variables to find a parsimonious model that explains NPLs in the Mozambican context by estimating a small
scale macroeconomic model which would feed into the stress tests.
64
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APPENDICES
Appendix1–SampleofBanksusedintheanalysis
Bank Name Bank’s Assets (%, as of December 2013) BIM 28.90 BCI 28.96 STB 15.20 BBM 6.10 ABC 3.21 FNB 2.79 CPC 0.60 MCB 0.77 ECOBANK 0.48 SOCREMO 0.36 CBM 0.23 TCHUMA 0.06 BOM 0.13 BTM 0.72 MOB 6.01 SYSTEM 94.52
C
Appendix2–OutputoftheVARoptimalLagselection
Source: Author’s estimations VAR Lag Order Selection Criteria Endogenous variables: QRGDPG CG INFL SLFR Exogenous variables: C Date: 02/07/15 Time: 17:29 Sample: 2004Q4 2013Q4 Included observations: 33
Lag LogL LR FPE AIC SC HQ
0 -370.2063 NA 83094.55 22.67917 22.86056 22.74020 1 -283.6363 146.9067 1164.557 18.40220 19.30917* 18.70737 2 -272.6593 15.96651 1655.339 18.70662 20.33918 19.25593 3 -250.2452 27.16858 1269.661 18.31789 20.67603 19.11133 4 -219.0355 30.26399* 652.6926* 17.39609* 20.47980 18.43367*
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
D
Appendix3–OrderofintegrationofvariablesoftheVARsystem
Source: Author’s estimations ADF Test
Variable Prob.*
At levels 1st difference 2nd difference Order of integration
Intercept Trend & Intercept
None Intercept Trend & Intercept
None Intercept Trend & Intercept
None
qrgdpg 0.0000 0.0402 0.1188 0.0000 0.0000 0.0000 I(1) cg 0.0087 0.0429 0.2225 0.0072 0.0403 0.0004 I(1) infl 0.0006 0.0195 0.0028 I(0) slfr 0.2736 0.1226 0.5097 0.0066 0.0133 0.0003 I(1)
E
Appendix4–OutputoftheVARstabilityconditioncheck
Source: Author’s estimations
Roots of Characteristic Polynomial Endogenous variables: D(QRGDPG) D(CG) INFL D(SLFR) Exogenous variables: C Lag specification: 1 4 Date: 02/07/15 Time: 18:32
Root Modulus
0.023680 - 0.938913i 0.939211 0.023680 + 0.938913i 0.939211 0.786651 - 0.415547i 0.889662 0.786651 + 0.415547i 0.889662 -0.880796 0.880796 -0.626206 - 0.554111i 0.836166 -0.626206 + 0.554111i 0.836166 0.670658 - 0.453435i 0.809559 0.670658 + 0.453435i 0.809559 -0.364415 - 0.714496i 0.802061 -0.364415 + 0.714496i 0.802061 -0.218798 - 0.686254i 0.720290 -0.218798 + 0.686254i 0.720290 -0.701444 0.701444 0.472461 - 0.182865i 0.506616 0.472461 + 0.182865i 0.506616
No root lies outside the unit circle. VAR satisfies the stability condition.
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
F
Appendix5–Modelvariablesbehaviour
Source: Author’s estimations
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
dez/04 2005 2006 2007 2008 2009 2010 2011 2012 2013
Credit Growth
GDP Growth
Inflation
Interest Rates
NPLs
G
Appendix6–OutputpanelregressiononNPLs
Source: Author’s estimations
Dependent variable: change in ratio of NPLs to gross loans (NPL)
Explanatory variables Regression coefficients GDP growth -0.011337*
(0.000659) Credit growth -0.000386**
(0.000100) Inflation 0.000362
(0.000616) Interest rate 0.004396***
(0.001159) NPL(-4) 0.540136***
(0.043966) Constant 0.104186***
(0.016106) Summary statistics
R2 0.757 N 315
Note: The table exhibits results of the regression of changes in
ratio of NPLs to gross loans. Regression is run on the data from
15 banks from 2008Q3 to 2013Q4. Standard errors are given in
parenthesis.
* - significant at 10% significance level
** - significant at 5% significance level
*** - significant at 1% significance level
H
Appendix7–Macro‐financialvariablesandchangeinNPLratioforecastforyear2014:baselineandshockscenarios
Source: Author’s estimations Variable 2013
Values Baseline forecast value for 2014
Shock forecast value for 2014
GDP growth 7.40% 8.10% 1.09%22 Forecasted NPL ratio of the system (baseline)
Forecasted NPL ratio of the system
(shock)
Credit growth
29.3% 30.9% 17.1%
Inflation 3.5% 6.0% 8.8%
Interest rate 9.02% 8.00% 8.3% 15.66% 15.74%
Bank Name NPL(-4) Baseline
NPL Forecasts
Shock NPL Forecasts
Assumed Increase in
NPLs BIM 2.55% 11.71% 11.79% 463% BCI 1.26% 11.01% 11.09% 880% STB 3.18% 12.05% 12.13% 381% BBM 9.92% 15.69% 15.77% 159% ABC 7.38% 14.32% 14.40% 195% FNB 2.19% 11.51% 11.59% 529% CPC 1.09% 10.92% 11.00% 1010% MCB 8.23% 14.78% 14.86% 180% ECOBANK 6.38% 13.78% 13.86% 217% SOCREMO 8.67% 15.01% 15.09% 174% CBM 16.81% 19.41% 19.49% 116% TCHUMA 28.81% 25.89% 25.97% 90% BOM 7.42% 14.34% 14.42% 194% BTM 32.62% 27.95% 28.03% 86% MOB 11.44% 16.51% 16.59% 145%
22 According to the World Bank database on annual GDP growth, the annual GDP growth of Mozambique in the year 2000 has been equal to 1.09%. So the shock simulates a repetition of this scenario.