i

THE EFFECTIVENESS OF FINANCIAL DERIVATIVES IN

MANAGING FOREIGN EXCHANGE EXPOSURE AMONG

COMMERCIAL BANKS LISTED AT THE NAIROBI SECURITIES

EXCHANGE

By

AARON NASURUTIA

A RESEARCH PROJECT SUBMITTED IN PARTIAL

FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF BUSINESS ADMINISTRATION, SCHOOL OF

BUSINESS, UNIVERSITY OF NAIROBI

OCTOBER, 2013

ii

DECLARATION

This research project is my original work and has not been presented for any award of

degree or diploma in any other university.

Signed…………………. Date………………….

AARON NASURUTIA D61/60646/2010

This management research project has been submitted for examination with our approval

as the University Supervisors.

Signed………………… Date…………………..

Mr. Duncan Elly, Lecturer, School of Business

University of Nairobi.

Moderator

Signed………………… Date…………………..

Dr. Fredrick Ogilo, Lecturer, School of Business

University of Nairobi.

iii

DEDICATION

To my children Ethan and Hazel

And to my wife Winnie

Thank you for walking with me through this journey

iv

ACKNOWLEDGEMENTS

I thank the Almighty God I serve for bringing me this far. Without Him none of this

would be possible.

I would also like to extend my utmost appreciation to my parents, Mr. Arthur IyadiMbati

and Mrs. Margaret Mbati for the sacrifices they made to educate me and to encourage me

pursue this degree. I am indebted to my wife Winnie, and my children Ethan and Hazel

who braved countless hours of my absence and sacrificed numerous occasions in order to

enable me finish this journey. My earnest appreciation goes to my supervisors, Mr.

HerickOndigo and Mr. Duncan Elly for their invaluable support, guidance and patience.

I would also extend my gratitude to my moderator, Dr. Fredrick Ogilo who corrected and

set straight my academic shortcomings with modesty and humility.

I am sincerely grateful to my former employer Chase Bank for availing the initial

financing and subsequent employers NIC Bank and Commercial Bank of Africa for

allowing me to effortlessly pursue this degree within the constraints of employment.

Last but not least, I would like to thank Mr. Duncan Muga and Mr. Simon Muteke for all

the support he gave me in compiling this study.

To all I say, thank you

v

TABLE OF CONTENTS Declaration .......................................................................................................................i

Dedication ..................................................................................................................... ii

Acknowledgements ....................................................................................................... iii

Table of contents ............................................................................................................ iv

List of tables ..................................................................................................................vii

List of abbreviations .................................................................................................... viii

Abstract .......................................................................................................................... ix

CHAPTER ONE: INTRODUCTION ........................................................................... 1

1.1 Background ............................................................................................................... 1

1.1.1 Financial derivatives ........................................................................ 2

1.1.2 Foreign currency exposure ............................................................... 4

1.1.3 Effect of financial derivatives on foreign currency exposure ............ 7

1.1.4 Commercial banks in Kenya ............................................................ 8

1.2 Research problem ...................................................................................................... 9

1.3 Study objective ........................................................................................................ 11

1.4 Value of study.......................................................................................................... 12

CHAPTER TWO: LITERATURE REVIEW ............................................................ 13

2.1 Introduction ............................................................................................................. 13

2.2 Theoretical review ................................................................................................... 13

2.2.1 The purchasing power parity theory ............................................... 13

2.2.2 International Fischer effect theory .................................................. 14

2.2.3 Expectation theory of forward rates................................................ 15

2.2.4 Interest rate parity theory ............................................................... 16

2.3 Foreign exchange exposure management ................................................................. 18

2.4 Measurement of foreign Exchange exposure and derivative usage ........................... 20

2.5 Empirical review...................................................................................................... 23

2.6 Summary of the literature......................................................................................... 26

CHAPTER THREE: RESEARCH METHODOLOGY ............................................ 28

3.1 Introduction ............................................................................................................. 28

vi

3.2 Research design ....................................................................................................... 28

3.3 Population of the study ............................................................................................ 28

3.4 Data collection ......................................................................................................... 28

3.5 Data analysis............................................................................................................ 29

3.5.1 Analytical model ............................................................................ 29

3.5.2 Operationalization of the variables ................................................ 30

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND FINDINGS ..................... 32

4.1 Introduction ............................................................................................................. 32

4.2 Descriptive statistics ................................................................................................ 32

4.3 Regression analysis .................................................................................................. 32

CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS . 37

5.1 Introduction ............................................................................................................. 37

5.2 Summary of findings ............................................................................................... 37

5.2.1 The effectiveness of derivatives in managing forex exposure ................................ 37

5.3 Conclusions ............................................................................................................. 38

5.4 Limitations of study ................................................................................................. 38

5.5 Recommendations.................................................................................................... 39

5.6 Suggestions for further studies ................................................................................. 39

REFERENCES ............................................................................................................ 40

APPENDICES ............................................................................................................. 45

Appendix I: Banks listed at the NSE as at 2012 ............................................................. 45

Appendix III: Residual outputs ...................................................................................... 46

vii

LIST OF TABLES

Table 4.1: Descriptive statistics of model variables ....................................................... 32

Table 4.2: variables correlation matrix .......................................................................... 33

Table 4.3: Model summary of derivative usage on forex exposure ................................. 34

Table 4.4: Anova for derivative usage on forex exposure .............................................. 34

Table 4.5: Coefficients of the model ............................................................................. 35

viii

LIST OF ABBREVIATIONS

CBK Central Bank of Kenya

ERV Exchange rate volatility

FOREX Foreign Exchange

FX Foreign Exchange

IFE International Fisher Effect

IFX Income from foreign currencies as a percentage of total income

IRP Interest Rate Parity

MST Market Segmentation Theory

NA Net Assets

NFXNA Net Foreign Currency Exposure Relative to Net Assets

NFX Net Foreign Currency Exposure

NSE Nairobi Securities Exchange

OS Ownership Status or Nature of Ownership

PPP Purchasing Power Parity

SSA Sub Saharan Africa

ix

ABSTRACT

This study was conducted with the aim of establishing the effectiveness of derivatives in managing foreign exchange exposure among commercial banks in Kenya. The study adopted a descriptive research design. The population of the study constituted all 10 listed commercial banks that were in operation during the study period from January, 2008 to December, 2012. A census of the population was conducted. Analysis was conducted through the use of regression analysis. The results indicated that derivative usage was found to have a negative relationship with foreign exchange exposure meaning that an increase in derivative usage resulted in a corresponding decrease in foreign exchange exposure. The relationship was in addition found to be significant as indicated by the p-value (0.0357>.05). The study consequently concluded that there was a negative, significant relationship between derivative usage and foreign exchange exposure for banks’ listed in the NSE. Consequently, therefore, derivative usage was found to be effective in management of foreign exchange exposure. On the basis of the findings, the study recommended that since derivative usage and foreign currency exposure are significantly and negatively related, derivatives can effectively be used by banks in Kenya to manage foreign exchange exposure.

1

CHAPTER ONE: INTRODUCTION

1.1 Background

The traditional role for commercial banks has been perceived to be the reduction of

transactions costs and the provision of information. However, given the technological,

information, and financial innovations of the last decade, risk sharing and risk

management are increasingly being viewed as a major source of value creation in

banking. Allen and Santomero (1997) argued that these changes have increasingly shifted

banks away from their traditional activities. Instead, they suggested that banks are

making increasing use of financial markets to transfer, transform, and redistribute risk.

Thus the financial markets’ perception of bank activities has taken on increasing

importance. Especially, after global financial crisis in 2008, banks’ derivative activities

have become increasingly controversial. In fact, the effect of derivative use on risk

measure and value is especially important in banking since banks dominate most

derivative markets.

Despite the use of derivatives contracts by banks having increased over the past two

decades, the effect of derivatives on the risks and market value of banks is still largely

unknown. Despite more widely available data on derivatives usage, the evidence obtained

from empirical research on its effects is mixed(Peek and Rosengren, 1997). One possible

answer to such contradictory results is whether banks use derivatives for trading or

hedging purpose. Previous studies have used data disclosed by all kinds of firms,

2

including non-financial firms and banks industries, in trying to improve understanding of

how firms use derivatives (Bartram, Brown and Conrad, 2011).

The unifyingassumption in nearly all of the literature has been that firms, including

financial firms, use derivatives for hedging as distinct from speculation. However,

Chernenko and Faulkender (2011) demonstrated non-financial firms use derivatives not

only for hedging but also for speculation. In practice, banks’ involvement in the

derivatives market has been considerably asymmetric with respect to trading and hedging

activities with banks more likely to speculate with derivatives (Minton et al. 2009).

Therefore, the primary objective of this study is to empirically investigate whether

commercial bank’s use of derivatives to manage forex exposure has been effective.

1.1.1 Financial Derivatives

Aderivativeis a financial instrument whose value depends on or is derived from the value

of some other financial instrument, called the ‘underlying asset’. There are a wide range

of financial assets that have been used as underlying, including equities or equity index,

fixed-income instruments, foreign currencies, commodities, credit events and even other

derivative securities. Depending on the types of underlying assets, the values of the

derivative contracts can be derived from the corresponding equity prices, interest rates,

exchange rates, commodity prices and the probabilities of certain credit events(Anderson

and McKay, 2008).

3

There are four main types of derivatives contracts - forwards; futures; options and swaps.

These allow users to meet the demand for cost-effective protection against risks

associated with movements in the prices of the underlying security.In other words, users

of derivatives can hedge against fluctuations in exchange and interest rates, equity and

commodity prices, as well as credit worthiness. Specifically, derivative transactions

involve transferring those risks from entities less willing or able to manage them to those

more willing or able to do so. Derivatives transactions are now common among a wide

range of entities, including commercial banks, investment banks, central banks, fund

managers, insurance companies and other non-financial corporations(Nystedt, 2004).

Participants in derivatives markets are typically classified as either “hedgers” or

“speculators”. Hedgers enter a derivative contract to protect against adverse changes in

the values of their assets or liabilities. Hedgers enter a derivative transaction so that a fall

in the value of their assets will be compensated by an increase in the value of the

derivative contract. By contrast, speculators attempt to profit from anticipating changes in

market prices or rates or credit events by entering a derivative contract. According to this

definition, activities of speculators are inherently more risky and should warrant close

monitoring by financial regulators (Jarrow and Turnbull, 2000). However, it is difficult to

differentiate the two in practice. As pointed out by Jarrow and Turnbull (2000), hedging,

risk reduction, speculation and risk augmentation are flip sides of the same coin.

Hedging and speculating are not the only motivations for trading derivatives. Some firms

use derivatives to obtain better financing terms. For example, banks often offer more

4

favourable financing terms to those firms that have reduced their market risks through

hedging activities than to those without. Fund managers sometimes use derivatives to

achieve specific asset allocation of their portfolios. For example, passive fund managers

of specific index-tracking funds may need to use derivatives to replicate exposures to

some not so liquid financial assets(Ansi and Ouda 2009).

1.1.2 Foreign Currency Exposure

Foreign exchange exposure is defined by Adler and Dumas (1984) as the degree to which

the value of a firm is affected by changes in exchange rate. The literature generally

identifies four channels of foreign exchange exposure: Translational exposure,

transactional exposure, operational exposure and contingency exposure. Translation

exposure occurs through currency mismatch and it is also related to assets or income

derived from offshore enterprise (Glaum, 1990; Grant and Soenen, 1991; Madura, 2003).

Transactional exposure arises from future cash flows such as trade contracts and also

occurs where the value of existing obligations are affected by changes in foreign

exchange rates. Operational exposure occurs where the market position of a firm changes

as a result of the effect of exchange rate changes on competition, prices and demand.

Contingency exposure occurs from possible revaluations arising from future liabilities.

The total or economic exposure of a firm refers to all exchange rate effects through all

four channels (Salifu et al., 2011).

Commercial banks, actively dealing in foreign currencies holding assets and liabilities in

foreign denominated currencies, are continuously exposed to Foreign Exchange Risk.

5

Foreign Exchange Risk of a commercial bank comes from its very trade and non-trade

services. Foreign exchange trading activities include the purchase and sale of foreign

currencies to allow customers to partake in and complete international commercial trade

transactions. They also involve the purchase and sale of foreign currencies to allow

customers (or the bank) to take positions in foreign real and financial investments. Other

forex trading activities may involve the purchase and sale of foreign currencies for

hedging purposes to offset customer (or the bank) exposure in any given currency and

purchase and sale of foreign currencies for speculative purposes based on forecasting or

expecting future movements in foreign exchange rates (Saunders & Cornett, 2003).

The above mentioned trade activities do not expose a commercial bank to foreign

exchange risk as a result of all of the above. The commercial bank is exposed to foreign

exchange risk only up to the extent to which it has not hedged or covered its position.

Wherever there is any uncertainty that the future exchange rates will affect the value of

financial instruments, there lies the foreign exchange risk of a commercial bank. Foreign

Exchange risk does not lie where the future exchange rate is predefined by using different

instruments and tools by the bank (Barton et al., 2002).

Any un-hedged position in a particular currency gives rise to FX risk and such a position

is said to be Open Position in that particular currency. If a bank has sold more foreign

currency than he has purchased, it is said to be Net Short in that currency, alternatively if

it has purchased more foreign currency than it has purchased than it is in Net Long

position. Both of these positions are exposed to risk as the foreign currency may fall in

6

value as compared to local or home currency and becomes a reason for substantial loss

for the bank if it is in Net Long position or the foreign currency may rise in value and

cause losses if the bank is Net Short in that currency. Long Position is also known as

overbought or Net Asset Position and Short Position is also known as Net Liability or Oversold

Position. The sum of all the Net Asset positions and Net Liability positions is known as

Net Open Position orNet Foreign Currency Exposure. “Net Foreign Currency Exposure” gives

the information about the Foreign Exchange Risk that has been assumed by the bank at

that point of time. This figure represents the un-hedged position of bank in all the foreign

currencies. A negative figure shows Net Short Position whereas positive figure shows

Net Open Position (Saunders & Cornett, 2003).

A number of studies; Adler and Dumas (1984); Choi et al. (1992); Bodnar and Gentry

(1993); Choi and Prasad, (1995); Chamberlain et al(1997) have been conducted to

determine the exposure of firms to exchange rate movements. Most of these studies have

however been inconclusive on the nature of exchange rate exposure. Again these studies

have been largely conducted on industrialized economies. Even when developing

economies are considered, Sub-Saharan African (SSA) economies remain absent in such

samples. It is however important to note that firms in SSA may also be significantly

exposed to exchange rate movements. Indeed since the 1990s, most SSA economies have

undergone economic stabilization reforms, comprising largely of exchange rate

liberalization from fixed to floating regimes. The liberalization of exchange rates and the

subsequent rapid changes in exchange rates in SSA certainly have implications for

7

African firms. It is therefore important to determine the exposure of such firms to

exchange rate movements (Salifu et al., 2011).

1.1.3 Effect of Financial Derivatives on foreign exchange exposure

Banks use Derivatives to manage foreign currency exposure. Hedging allows the

commercial banks to manage foreign exchange risk but hedging itself poses additional

risk to banks. Gandhi (2006) mentioned that currency derivatives like currency futures,

currency forwards, currency swaps and currency options help in hedging foreign

exchange risk of firms as well as other ways of hedging including; off-setting positions

against the underlying assets and money markets are themselves risky. Hedging and

hedging rights are two different things.If the hedging is not done properly in the right

way, it can become a serious source of risk and can lead to a serious financial loss to the

firm.

Foreign exchange risk can be managed if the diversification of portfolio is done across

the assets in different currencies. Cash flows of a portfolio can be affected or changed by

the usage of derivative securities. The usage of currency derivatives additionally reduces

the risk of whole diversified portfolio (Abken&Shrikhande, 1997). Currency Derivatives

are not only helpful in hedging the foreign exchange risk, but due to the resultant

information currency derivatives, makes the currency markets more efficient and

exchange rates less forecast able (Liu, 2007).

8

Notably, foreign currency options are one of the best tools available for hedging foreign

exchange exposures in different foreign exchange market conditions, like volatile market

conditions, stagnant, bullish or bearish (Gandhi, 2006). Foreign Currency options are

derivative instruments that give the buyer of that option the right but not the obligation to

exercise a specific transaction in the currency pair underlying the respective derivative

contract. It entitles the buyer of the option the flexibility of exercising settlement of that

option or not.

1.1.4 Commercial Banks in Kenya

The banking sector in Kenya is governed by the company’s Act, the Banking Act and the

Central Bank Act and the various prudential guidelines issued by Central Bank of Kenya.

The banking sector was liberalised in 1995 when exchange controls were lifted. The

Central Bank of Kenya is responsible for formulating and implementing monetary policy

directed to achieving stability in the general level of prices and fosters the liquidity,

solvency and proper functioning of a stable market based financial system while

supporting the economic policy of the Government (Central Bank of Kenya, 2012). As at

31st December 2012, the banking sector comprised of the Central Bank of Kenya, as the

regulatory authority, 44 banking institutions (43 Commercial banks and 1 Mortgage

finance company), 2 representative offices of foreign banks, 5 Deposit-Taking

Microfinance Institutions and 126 Forex Bureaus. 31 of the banking institutions are

locally owned while 13 are foreign owned (CBK, 2012).

9

The locally owned financial institutions comprise of 3 banks with public shareholding, 27

privately owned commercial banks, 1 mortgage finance company while 5 Deposit-Taking

Microfinance Institutions and 126 forex bureaus are privately owned (Central Bank of

Kenya, 2012). The foreign owned financial institutions comprise of nine locally

incorporated foreign banks and four branches of foreign incorporated banks. The sector

was dominated by local private institutions with 27 institutions accounting for 58.0

percent of the industry’s total assets and 64% of total financial institutions (CBK, 2012).

A major development in the bankins sector has been the development of mobile money

transfer services.In 2012, a number of banks responded to the growing need of

convenient straight-through payments using mobile solutions. As a result, a number of

banks continued to sign up partnerships with money transfer service providers as they

improve their banking-on-the-move menus. In only four years of existence of mobile

phone money transfer services, four mobile operators have enrolled over 15 million

customers (Central Bank of Kenya, 2012).

1.2 Research Problem

Exchange rate movements have been a big concern for investors, analysts, managers and

shareholders since the abolishment of the fixed exchange rate system of Bretton Woods

in 1971. This system was replaced by a floating rates system in which the price of

currencies is determined by supply and demand of money. Given the frequent changes of

supply anddemand influenced by numerous external factors, this new system is

responsible for currency fluctuations (Abor, 2005). These fluctuations expose companies

to foreign exchange risk. Moreover, economies are getting more and more open with

10

international trading constantly increasing and as a result companies become more

exposed to foreign exchange rate fluctuations (Adler and Dumas, 1984). Derivatives have

emerged as useful tools of managing foreign exchange exposure. The size and

complexity of derivatives transactions has continued to concern regulators, academics,

and commercial banks.

According to the Kenya Bankers Association (KBA), the derivative market in Kenya is

not fully developed and is not effectively regulated and properly supervised. Commercial

banks in Kenya offer a limited number of derivatives including foreign currency-

denominated forward contracts, interest rates and cross-currency swaps. The KBA

recently observed that greater understanding and more specific and stringent regulations

(which are currently ambiguous) will offer proper guidelines for banks that are already

involved in the trade (KBA, 2013). It noted that the risk involving derivative trade

necessitated greater understanding and closer oversight stressing that as the derivative

market in Kenya continues to evolve, there will be a need for CBK to first understand the

various derivative products and then effectively prescribe; guidelines on best practices;

and to regulate and supervise them better, the absence of which could result in stress in

the financial systems (KBA 2013).

Locally, Cherutoi (2006) did a study on the extent of commercial banks exposure to

foreign exchange risk and found that commercial banks in Kenya are not significantly

exposed to foreign exchange rate risk. Ubindi (2006) conducted a survey of foreign

exchange risk management practices by forex bureaus in Kenya and found that most

11

forex bureaus, regardless of their size, extensively utilized most of the conventional

hedging instruments. Mumoki (2009) did a study on foreign exchange risk management

strategies and techniques used by banks in Kenya to manage foreign exchange risk

exposure and found that the forward contract was the most frequently used instrument.

The money market hedge and the currency swap were also frequently used. Parallel loans

(Back-to-back loan), foreign currency denominated debt and cross hedging techniques

were moderately used. Futures contract, foreign currency option and leading and lagging

techniques were occasionally used.The effectiveness of derivatives as a tool of managing

foreign exchange exposure, however, has rarely been studied locally.

In theory, derivatives should allow companies and banks to manage exposure better, but

that it is not clear to what extent derivatives have achieved the objective ofmanaging the

inherent exposure in the banking industry. Given the importance of commercial banks in

the Kenyan economy and the volatility of the Kenyan shilling against the world’s major

currencies, it is important to investigate how effective derivatives have been in managing

foreign exchange exposure in the Kenyan commercial banks.

1.3 Objective of the Study

The aim of the study was to establish whether financial derivatives are effective in

managing foreign exchange exposure by commercial banks in Kenya

12

1.4 Value of the study

The study has following practical values: The study has provided useful insights into the

effectiveness of derivatives as an exposure management tool. To the scholars and

researchers, this study has provided a basis for future studies on the area of foreign

exchange rate risk management.

The study has the following theoretical value: The study has added to the body of

empirical literature on use of derivatives to deal with exchange rate exposure of

commercial banks. The study has also indicated the extent of effectiveness of derivative

usage in the local banking context.

The policy value of this study is that it provides the regulators with deeper understanding

that can be used to facilitate best practices and regulatory policy formulation. It also

provides useful management information on the extent to which derivatives as a hedging

instrument should be adopted in the banking sector.

13

CHAPTER TWO: LITERATURE

REVIEW

2.1 Introduction

This chapter considers literature relevant to the subject under study. The main issues

under review are; the theoretical review,management of foreign exchange risk, empirical

review, measurementsand summary to the literature.

2.2 Theoretical review

Movements in exchange rates tend to be influenced by two important variables; the

relative prices of goods in two countries and relative interest rates. This section gives a

brief overview of the foreign exchange theories which include the purchasing power

parity (PPP) theory; international fisher effect theory; expectation theory of forward

rates; and interest rate parity theory (Shapiro and Rutenberg, 1996; Cumby and Obstfeld,

1981; Robert, 1998; Yuhang, 2007).

2.2.1 The Purchasing Power Parity Theory

The Purchasing power parity (PPP)theory propounds that under a floating exchange

regime, a relative change in purchasing power parity for any pair of currency calculated

as a price ratio of traded goods would tend to be approximated by a change in the

equilibrium rate of exchange between these two currencies (Shapiro and Rutenberg,

1996).The relationship between relative interest rates and foreign exchange rates is

explained within the interest rate theory of exchange rate expectations. Nominal interest

rate differentials between two countries tend to reflect exchange rate fluctuations.

14

Purchasing power parity (PPP) is an economic theory and a technique used to determine

the relative value of currencies, estimating the amount of adjustment needed on the

exchange rate between countries in order for the exchange to be equivalent to (or on par

with) each currency's purchasing power.It asks how much money would be needed to

purchase the same goods and services in two countries, and uses that to calculate an

implicit foreign exchange rate. Using that PPP rate, an amount of money thus has the

same purchasing power in different countries(Lawrence, 1992).

2.2.2 International Fisher Effect Theory

Named after its proposer, the U.S. economist Irving Fisher (1867-1947), the International

Fisher Effect (IFE) theory suggests that foreign currencies with relatively high interest

rates will tend to depreciate because the high nominal interest rates reflect expected rate

of inflation (Madura, 2010). Available evidence is mixed as in the case of PPP theory. In

the long-run, a relationship between interest rate differentials and subsequent changes in

spot exchange rate seems to exist but with considerable deviations in the short run. The

international Fisher effect is known not to be a good predictor of short-run changes in

spot exchange rates (Cumby and Obstfeld, 1981).

IFE states that the currency of a nation with a comparatively higher interest rate will

depreciate in value in comparison to the currency of a nation with a comparatively lower

interest rate. It further implies that the extent of depreciation will be equal to the

difference in interest rates in those two nations. It is based on the observation that the

15

level of real interest rate in an economy is closely linked to the level of local inflation rate

and is independent of a government's monetary policies. Thus, in general, the higher the

inflation rate, the lower the value of currency (Hill, 2004).

2.2.3 Expectation Theory of Forward Rates

Expectations theory suggests that the forward rates in current long-term bonds are closely

related to the bondmarket's expectation about future short-term interest rates.

Expectations theory attempts to explain the term structure of interest rates. There are

three main types of expectations theories: pure expectations theory, market segmentation

theory and preferred habitat theory (Robert, 1998.)

In pure expectations theory, it is assumed that any maturity of debt can substitute for any

other through the miracle of compounding. For instance, if you have a view as to what

the one-year interest rate will be one year from now (the forward rate), then you can

determine the current two-year interest rate as the compounded sum of the current one-

year rate and the one-year forward.

The market segmentation theory (MST) acknowledges that different maturities of debt

cannot be substituted for each other. This results in separate demand-supply relationships

for short-term and long-term debt. Since investors (assumed to be risk-adverse) prefer

the less risky short-term maturities, the demand for short-term debt is higher than that for

long-term debt, and thus prices of the former are higher, driving down their yields. This

helps to explain the normal shape of the yield curve, but not the fact that long and short

16

term rates tend to change in unison, since they are supposed to be two separate and

independent markets (Mishkin, 1999).

Preferred Habitat Theory is an extension of MST which posits maturity preferences, or

habitats, for debt investors: some investors like 3-year bonds; some prefer 6-year

maturities, etc. If you want to sell an investor a bond outside the investor’s preferred

investment horizon, you must offer the investor a premium. Since it is assumed that

more investors have short-term habitats, it explains the higher yields on long-term debt,

and is consistent with the tendency of short and long-term debt yield curve segments to

retain their shape when overall yields change.

Expectations theories are predicated upon the idea that investors believe forward rates, as

reflected (and some would say predicted) by future contracts are indicative of future

short-term interest rates. In foreign exchange, a theory that forward exchange rates for

delivery at some future date are equal to the spot rates for that date. The theory only

functions in the absence of a risk premium (Frank, 1997).

2.2.4 Interest Rate Parity Theory

Interest Rate Parity (IPR) theory is used to analyze the relationship between the spot rate

and a corresponding forward (future) rate of currencies.The IPR theory states interest rate

differentials between two different currencies will be reflected in the premium or

discount for the forward exchange rate on the currency if there no arbitrage - the activity

17

of buying shares or currency in one financial market and selling it at a profit in another

(Yuhang, 2007).

The theory further states size of the forward premium ordiscount on a foreign

currencyshould be equal to the interest rate differentials between the countries in

comparison.If IRP theory holds then arbitrage in not possible. No matter whether an

investor invests in domestic country or foreign country, the rate of return will be the same

as if an investor invested in the home country when measured in domestic currency.If

domestic interest rates are less than foreign interest rates, foreign currency must trade at a

forward discount to offset any benefit of higher interest rates in foreign country to

prevent arbitrage (Jonathan, 2005).

If foreign currency does not trade at a forward discount or if the forward discount is not

large enough to offset the interest rate advantage of foreign country, arbitrage opportunity

would exist for domestic investors. Domestic investors can benefit by investing in the

foreign market.If domestic interest rates are more than foreign interest rates, foreign

currency must trade at a forward premium to offset any benefit of higher interest rates in

domestic country to prevent arbitrage. However, if foreign currency does not trade at a

forward premium or if the forward premium is not large enough to offset the interest rate

advantage of domestic country, arbitrage opportunity exists for foreign investors and

foreigninvestors can benefit by investing in the domestic market (Bruce, 2011).In recent

years the interest rate parity model has shown little proof of working. In many cases,

18

countries with higher interest rates often experience it's currency appreciate due to higher

demands and higher yields and has nothing to do with risk-less arbitrage (Bruce, 2011).

2.3 Foreign Exchange Exposure Management

Foreign currency exchange risk is the additional riskiness or variance of a firm‘s cash

flows that may be attributed to currency fluctuations (Brigham and Ehrhardt, 2005).

Foreign currency risk management involves taking decisions which aim at minimizing or

eliminating the negative effects of currency fluctuations on balance sheet and income

statement values, a firm's receipts and payments arising out of current transactions, and

on long term future cash flows of a firm. Creativity by managers and innovations in

financial instruments have made available to firms mitigating tools that can be followed

in managing the impact of foreign currency rate fluctuations. These tools are commonly

known as hedging techniques.

Foreign exchange risk management typically takes the following steps: the exposure is

first determined; a forecast of the market trend is then developed with particular attention

to what the main direction/trend is going to be on the FX rates. The period for forecasts is

typically 6 months. Based on the forecast, a measure of the Value at Risk (the actual

profit or loss for a move in rates according to the forecast) and the probability of this risk

should be ascertained. The risk that a transaction would fail due to market-specific

problems should be taken into account. Finally, the Systems Risk that can arise due to

inadequacies such as reporting gaps and implementation gaps in the firms’ exposure

management system should be estimated. Given the exposures and the risk estimates, the

19

firm has to set its limits for handling foreign exchange exposure (Brigham and Ehrhardt,

2005).

The literature on the choice of hedging instruments is very scant. Among the available

studies, (Geczy et al., 1997; Marshall, 2000) argues that currency swaps are more cost-

effective for hedging foreign debt risk, while forward contracts are more cost-effective

for hedging foreign operations risk. This is because foreign currency debt payments are

long-term and predictable, which fits the long-term nature of currency swap contracts.

Foreign currency revenues, on the other hand, are short-term and unpredictable, in line

with the short-term nature of forward contracts.

Research in the area of determinants of hedging separates the decision of a firm to hedge

from that of how much to hedge. There is conclusive evidence to suggest that firms with

larger size, R&D expenditure and exposure to exchange rates through foreign sales and

foreign trade are more likely to use derivatives. According to the risk management

literature, firms with high leverage have greater incentive to engage in hedging because

doing so reduces the probability, and thus the expected cost of financial distress. Firms

with highly liquid assets or high profitability have less incentive to engage in hedging

because they are exposed to a lower probability of financial distress (Allayanis and Ofek,

2001).

20

2.4 Measurement of foreign Exchange exposure and derivative usage

Earlier studies used a monthly, contemporaneous horizon to measure exposure. In an

early study, Adler and Dumas (1984) presented a method of estimating the foreign

exchange exposure using a single-factor market model to estimate the elasticity of firm

equity returns to exchange rate changes. Jorion (1991) estimated exposure using a two-

factor model that thereafter became the norm for estimating foreign exchange exposure

controlling for market risk. For a sample of firms drawn from the Fortune 500, he found

that the degree of exposure varied directly with the degree of foreign involvement. Other

studies have re-confirmed these basic findings regarding the foreign exchange exposure

faced by internationally involved and multinational companies, and explored in greater

detail various issues that arise in the procedures used for estimating such exposure -

issues that are important considerations in this study.

Given the difficulty in anticipating exchange rate movements, corporate managers,

investors and analysts have to devise means of managing or investing optimally to

neutralize exchange rate risks. Due to the adverse effects of exchange rate exposure, and

the difficulty in anticipating exchange rate movements, there have been increased

attempts by researchers to determine the level and effect of exchange rate exposure of

firms (Salifu et al., 2011).

The foreign exchange risk exposure literature is replete with a plethora of studies

regarding the exposure of firms and countries to frequent changes in the exchange rates

of domestic currencies in relation to the currencies of trading partner countries. These

21

studies have documented evidence of exposure by many of the firms with significant

assets and cash flow denominated in foreign currencies. In many of these studies,

exposure is measured by estimating the sensitivity of stock returns to exchange rate

changes (Adler and Dumas, 1984; Jorion, 1990; Choi et al., 1992; Bodnar and Gentry,

1993; Bartov and Bodnar, 1994; Choi and Prasad, 1995; Chamberlainet al., 1997; He

andNg, 1998).

Whilst there have been many empirical studies which have examined the relationship

between foreign exchange exposure and firm value, their results have however been

mixed. Jorion, 1990, 1991; Bartov and Bodnar, 1994; and Choi and Prasad, 1995, using

USA data and Loudon (1993), and Khoo (1994) using Australian data find firm values to

be insensitive to exchange rate movements. Choiet al.(1992) also examined USA banks

and found about 20 per cent to be significantly exposed to exchange rate risk over the

1975-1987 period. Other studies such as Booth and Rotenberg (1990) who used Canadian

data and He and Ng (1998) who employed Japanese data found some evidence that

foreign exchange risk affects firm value (Salifu et al , 2011).

Nguyen and Faff (2002) classified a firm as a ‘derivative user’ if it used any of the

following derivative instruments - swaps, futures/forwards and options (as reported in the

firm’s notes to the financial statements). Extent of derivative use is typically calculated as

the total derivative contract value scaled by firm size. Where the ratio exceeds 100%

(which happens most often to gold and mining companies), the value is restricted to

100%. The minimum value of 0% applicable to derivative users indicates the fact that the

22

firm does use derivatives in the course of business but as of reporting date there is no

contract outstanding (Nguyen and Faff, 2002).

The use of notional value as a measure of the extent of derivative usage is not a perfect

construct since notional value does not indicate the direction of a transaction; that is, it

does not indicate whether a firm is holding a long or a short position (Nguyen and Faff,

2002). Nevertheless, notional value measurements are chosen for at least three reasons:

First, as argued by Hentschel and Kothari (2001) ‘except for options and leveraged

swaps, it is not unreasonable to assume a general proportionality between contract size

and exposure’. Second, despite the various problems that might arise from the use of

notional value, it is undeniable that such a measure is widely used in the literature. For

example, see Allayannis and Ofek (2001), Henschel and Kothari (2001), Hardwick and

Adams (1999), and Berkman and Bradbury (1996). Third, a readily available alternative

construct that is clearly superior to notional value simply does not exist (Nguyen and

Faff, 2002).

Bharmornsiri and Schroeder (2004) indicated that in June 1998 the Financial Accounting

Standards Board (FASB) issued the Statementof Financial Accounting Standards No.

133, “Accounting for derivative instrumentsand hedging activities” (SFAS No. 133)

(FASB, 1998). The pronouncement was issuedin response to the large derivative losses

that were incurred by companies in the early 1990s, such as Showa Shell Sekiya’s $1,580

million loss on currency derivatives, Procter & Gamble’s $157 million loss on leveraged

currency swaps and Arco’s employee earnings losses of $22 million on money market

23

derivatives. A 1994 article in Fortunediscussing derivative risks indicated that the total

notational value of all derivative contracts outstanding at that time was $16 trillion

(Bharmornsiri and Schroeder, 2004).

After postponing the effective date twice, SFAS No. 133 requires publicly held

companies to recognize all derivatives as either assets or liabilities in the statement of

financial position and measure those instruments at fair value for all fiscal periods

starting after 15 December 2000. Consequently, 31 December 2001 was the first

reporting date for most companies under the provisions of SFAS No. 133.The underlying

philosophy of SFAS No. 133 is that derivatives are contracts that create rights and

obligations that meet the Statement of Financial Accounting Concepts No. 6’s definition

of assets and liabilities. SFAS No. 133 indicates a company may designate a derivative as

a hedge of the exposure to changes in the fair value of a recognized asset or liability or an

unrecognized firm commitment (fair value hedge), a hedge of the exposure to variable

cash flows of a forecasted transaction (cash flow hedge), or a speculative hedge

(Bharmornsiri and Schroeder, 2004).

2.5 Empirical Review

Initial research in this area focused on whether corporations are exposed to foreign

exchange risk (Bodnar and Gentry, 1993, Bartov and Bodnar, 1994, 1995, and Chow, Lee

and Solt, 1997a, b). Allayannis and Ofek (2001) and Simkins and Laux (1996)

investigated the effect of financial hedging on foreign-exchange exposure. More recently,

24

Pantzalis, Simkins, and Laux (2000) examine the ability of operational hedges to reduce

exposure.

More recently: El-Masry (2006) sought to shed light on derivatives use and risk

management practices in the UK market. His approach was to conduct a survey via

questionnaire, which focused on determining the reasons for using or not using

derivatives for 401 UK nonfinancial companies. The results indicated that larger firms

are more likely to use derivatives than medium and smaller firms; public companies were

more likely to use derivatives than private firms, and derivatives usage was greatest

among international firms.

Shen and Hartarska (2013) sought to estimate the impact of financial derivatives on

profitability in agricultural banks. Agricultural banks are new to the derivatives market

and are unlikely to use financial derivatives for risk speculation. They used call report

data from Federal Reserve Bank of Chicago for 2006, 2008 and 2010 to estimate an

endogenous switching model to evaluate how profitability of derivatives user and non-

user agricultural banks is affected by different risk factors. Results indicated that risk

management through financial derivatives in agricultural banks was effective and

profitability of derivatives user agricultural banks was less affected by credit risk and

interest risk in the sample period.

Emm and Ince (2011) sought to examine the extent of systemic risk and competition

inover-the-counter (OTC) derivatives dealing. They used the event-study methodology

25

with crudedependence adjustment to examine the wealth effect for the involved

derivatives dealers. They re-estimated the parameters using the market-adjusted model to

check for robustness. In addition,a multivariable regression framework was used to

estimate the determinants of the abnormal returns. The findings were that OTC

derivatives dealers experienced negative returns when their clients announced derivatives

losses. In contrast, rival dealers uninvolved in the loss event exhibit positive returns.

Afza and Alam (2011) sought to identify the factors affecting firms’ decision to use

foreignexchange (FX) derivative instruments by using the data of 86 non-financial firms

listed on KarachiStock Exchange for the period 2004-2007.Required data were collected

from annual reports of listedfirms of Karachi Stock Exchange. Non-parametric test was

used to examine the mean difference between users and non-users operating

characteristics. Logit model was applied to analyze the impact of firm’s financial distress

costs, underinvestment problem, tax convexity, profitability, managerial ownership and

foreign exchange exposure on firms’ decision to use FX derivative instruments for

hedging.Results explained that firms having higher foreign sales are more likely to use

FX derivative instruments to reduce exchange rate exposure. Moreover, financially

distressed large-size firms with financial constraints and fewer managerial holdings are

more likely to use FX derivatives.

Locally,Cherutoi (2006) did a study on the extent of commercial banks exposure to

foreign exchange risk. The results from the study indicated that commercial banks in

Kenya are not significantly exposed to foreign exchange rate risk. Ubindi (2006)

26

conducted a survey of foreign exchange risk management practices by forex bureaus in

Kenya. The findings were that forex bureaus, regardless of their size, extensively utilized

most of the conventional hedging instruments.

Mumoki (2009) did a study on foreign exchange risk management - strategies and

techniques used by banks in Kenya to manage foreign exchange risk exposure. The

results of the study showed that the forward contract was the most frequently used

instrument. The money market hedge and the currency swap were also frequently used.

Parallel loans (Back-to-back loan), foreign currency denominated debt and cross hedging

techniques were moderately used. Futures contract, foreign currency option and leading

and lagging techniques were occasionally used. Prepayment was the least used technique.

Gitogo (2012) conducted a study on the relationship between derivatives and financial

performance of commercial banks in Kenya. The study concluded that there exists

relationship between derivatives and financial performance of commercial banks in

Kenya.

2.6 Summary of Literature review

In the literature, four theories have attempted to explain interaction of currencies in the

foreign exchange market. They include: the purchasing power parity (PPP) theory;

international fisher effect theory; expectation theory of forward rates; and interest rate

parity theory.Foreign currency risk management involves taking decisions which aim at

27

minimizing or eliminating the negative effects of currency fluctuations on balance sheet

and income statement values.

A number of both international and local studies empirical studies have been conducted

on different aspects of derivatives hedging and forex exposure. Locally though, there is a

rarity on studies that explore the relationship of the two. The effectiveness of using

derivatives as a hedge to exposure has seldom been studied locally.

In many of empirical studies, exposure is measured by estimating the sensitivity of stock

returns to exchange rate changes. The use of notional value as a measure of the extent of

derivative usage is not a perfect construct since notional value does not indicate the

direction of a transaction. However, a readily available alternative construct that is clearly

superior to notional value simply does not exist (Nguyen and Faff, 2002).

28

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction

This chapter looks at the methodology that was employed to tackle the study. The

research design is first described and then the population and sample are discussed. Next,

data collection is described. Finally, data analysis is explained.

3.2 Research Design

This study adopted a descriptive research design which generally describes the

characteristics of a particular situation, event or case. As defined by Glass and Hopkins

(1984), descriptive research design involves gathering data that describes events and then

organizes, tabulates, depicts, and describes the data collection and often uses visual aids

such as graphs and charts to help the reader in understanding data distribution.

3.3 Study Population

The population of the study constituted all 10 listed commercial banks that were in

operation during the study period from January, 2008 to December, 2012 (NSE, 2013).

The study conducted a census (Refer to appendix 1).

3.4 Data Collection

Data was collected from secondary sources only as this was readily available from the

commercial banks financial reports, Central Bank of Kenya reports and the Nairobi

Securities Exchange (NSE). Data collection for extent of foreign exchange exposure

constituted individual bank quarterly average exchange rates for the following currencies:

29

US Dollar; Sterling Pound; and the Euro. It also included individual bank as well as

market returns during the study period. Data for forex derivative usage constituted

quarterly trading component value of derivative contracts and bank size. According to

International Accounting Standards 32 and 39, it is mandatory for firms to disclose their

usage of hedging instruments and their respective fair value in the notes of annual reports

in a uniform manner (Afzaand Atia, 2011). The data was therefore obtained from the

respective financial statements of the banks.The data spanned 5 years from January, 2008

through to December, 2012.

3.5 Data analysis

SPSS and Excel software were used to analyze the data. Data analysis was conducted by

synthesizing the forex exposure and derivative usage and its control variables into a

regression model which was subjected to a correlation analysis as well. Descriptive

statistics was used to describe the data while t tests were used to measure the significance

of the relationships.

3.5.1 Analytical model

The theoretical framework for the exchange rate exposure of firms was based on the fact

that, exchange rate exposure has potentially positive or negative impact on the

profitability and value of the firm. This is captured in the valuation process in terms of

the firm’s stock returns. Thus, the approach to modeling the exchange rate exposure has

been to regress the exchange rate on firm’s returns (Salifu et al, 2007; Fornes and

Cardoza, 2009).

30

The model adopted is that of (Jorion , 1990; Salifu et al, 2007; Fornes and Cardoza,

2009). The motivation for adopting this model was due to the fact that it incorporates the

market exposure which controls for the market’s own exchange rate exposure.

The model postulates the following relationship among the return on a firm, exchange

rate changes and return on the overall market:

푅 =∝ + 훽 퐷푒푟.푈 + 훽 퐾퐸푆푈푆퐷 + 훽 퐾퐸푆퐸푈푅푂 + 훽 퐾퐸푆퐺퐵푃 + 훿 푅 + 휀

Where:

∝ = Constant term

푅 =The stock return for bank 푖 at period 푡

푅 = Returns on the market

퐷푒푟.푈 = Derivative usage

퐾퐸푆푈푆퐷 =the log of the USD relative to the Kes.

퐾퐸푆퐸푈푅푂 =the log of the Euro relative to the Kes.

퐾퐸푆퐺퐵푃 =the log of the Sterling Pound relative to the Kes.

훽 and 훿 represent the sensitivity of bank 푖’s returns to exchange rate movement and the

returns on the market respectively

3.5.2 Operationalization of the variables

The returns for bank 푖 for each period 푡 was computed as:

31

푅 =푃 − 푃푃

Where:

푅 =the holding period return in time 푡 of asset 푖

푃 = the share price of bank 푖 at the end of period 푡

푃 =the share price of bank 푖 at the end of period 푡 − 1

The equations shall be run separately for each of the 10 banks in the sample using

monthly data for the period January 2008 to December 2012.

The extent of derivative use shall be calculated as the total derivative contract value

(trading component) scaled by bank size. Where the ratio exceeds 100% (which happens

most often to gold and mining companies), the value is restricted to 100%. The minimum

value of 0% applicable to derivative user banks indicates the fact that the bank does use

derivatives in the course of business but as of reporting date there is no contract

outstanding (Nguyen and Faff, 2002).Distinction shall have to be made between

derivatives used for speculative purposes and trading motives since banks deal in both.

The component of trading related derivatives is what shall be considered (Afza and Atia,

2011).

32

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND FINDINGS

4.1 Introduction

This chapter detailed the data analysis, findings and interpretations of the research study.

Descriptive statistics and regression analysis are respectively discussed. Analysis results

and findings are also discussed.

4.2 Descriptive statistics

Table 4.1 below gives a summary of the descriptive statistics of regression data.

Table 4.1: Descriptive statistics of model variables

Returns usage/size US$ British £ European € Market R. Mean 0.14808 0.05176 1.88163 2.11325 2.02515 0.02820 Standard Error 0.10514 0.00395 0.00376 0.00243 0.00356 0.00984 Median -0.00594 0.02609 1.88587 2.10892 2.01924 0.01878 Mode -0.87234 0.00088 1.82588 2.13481 1.98372 0.02473 Standard Dev. 1.41061 0.05301 0.05050 0.03263 0.04771 0.13196 Sample Variance 1.98981 0.00281 0.00255 0.00106 0.00228 0.01741 Kurtosis 61.93390 -0.76504 -0.17220 -0.59601 0.36075 5.88161 Skewness 7.32347 0.69518 0.43960 0.36093 0.85130 1.94170 Range 15.29991 0.18730 0.21572 0.13321 0.19395 0.69506 Minimum -1.24813 0.00022 1.79169 2.05358 1.95100 -0.19810 Maximum 14.05178 0.18752 2.00741 2.18679 2.14495 0.49697 Sum 26.655 9.317 338.694 380.385 364.527 5.075 Count 180 180 180 180 180 180 Largest(1) 14.05178 0.18752 2.00741 2.18679 2.14495 0.49697 Smallest(1) -1.24813 0.00022 1.79169 2.05358 1.95100 -0.19810 Conf. L. (95.0%) 0.20747 0.00780 0.00743 0.00480 0.00702 0.01941

Source: Computation from raw data obtained from the NSE and CBK

4.3 Regression analysis

Regression analysis was used to test the effectiveness of derivatives in managing foreign

exchange exposure among commercial banks in Kenya. Foreign exchange exposure was

33

the dependent variable while derivative usage (surrogated by proportion of derivative

usage; and other control variables - log of US$; British £; European €; and market

returns) were the predictor variables. Data for the above variables was generated for 10

listed banks in the NSE that spanned the years 2008 to 20012(Refer appendix i). The data

was subjected to a regression analysis, the findings of which are discussed below:

Table 4.2: variables correlation matrix

Returns (Usage/Size) US$ British £ European € Market R. Returns 1 usage/size 0.098415 1 US$ 0.057291 0.0913538 1 British £ 0.071411 0.2215557 0.271651 1 European € 0.128477 0.0980307 0.883173 0.421627 1 Market R. 0.378941 0.1826655 0.364085 0.589496 0.49071173 1

Source: Computation from raw data obtained from the NSE and CBK

A correlation matrix was used to check for multi-collinearity that is if there is a strong

correlation between two predictor variables (correlation coefficient > 0.8). As shown in

table 4.2 above, none of the variables is strongly correlated with each other. Thus a model

of the predictor variables (derivative usage; log of US$; British £; European €; and

market returns) could be used in forecasting of foreign exchange exposure of banks listed

at the NSE during the period 2008-2012.

34

Table 4.3: Model summary of derivative usage on forex exposure

Regression Statistics Multiple R 0.438773015 R Square 0.192521759 Adjusted R Square 0.169318361 Standard Error 1.285651926 Observations 180

Source: Computation from raw data obtained from the NSE and CBK

From table 4.3 above, the study used Coefficient of determination to measure the

percentage variation in the dependent variable being explained by the changes in the

independent variables). Coefficient of determination (R2) of 0.1925 shows that only

19.25% of the variation in foreign exchange exposure is explained by the changes in the

predictor variables leaving 80.75% unexplained. The regression model obtained for this

study therefore cannot be used to reliably forecast foreign exchange exposure variability.

Table 4.4: Anova for derivative usage on forex exposure

ANOVA Df SS MS F Significance F

Regression 5 68.57172 13.71434 8.297137 4.70641E-07 Residual 174 287.6048 1.652901 Total 179 356.1765

Significance F on table 4.4 demonstrates the usefulness of the overall regression model at

a 5% level of significance. Since the p-value of the F test is less than alpha (4.70641E-07

< .05), it demonstrated that there was a significant relationship between the dependent

and independent variables used in the study. Table 4.3 also clearly indicates that the

regression only accounted for a less than dominant number of variations in leverage;

68.5717 (19.2 %) out of 356.1765; the rest of the variations being accounted for by other

35

factors external to the model (Residual) as indicated by the sum of the squares (SS).

Residual (or error) represents unexplained (or residual) variation after fitting a regression

model. It is the difference (or left over) between the observed value of the variable and

the value suggested by the regression model.

Table 4.5: Coefficients of the model

Coefficients Standard Error t Stat P-value Intercept 24.82705093 8.463060349 2.933578387 0.003802451 usage/size -1.724118697 1.867385912 0.923279268 0.035714025 US$ -5.949112261 4.193002324 -1.418819214 0.157740476 British £ -11.21292089 3.81859692 -2.936398139 0.003769693 European € 4.921136197 4.816654803 1.021691692 0.308345238 Market R. 5.51463029 0.959755429 5.74587038 4.01004E-08

Source: Computation from raw data obtained from the NSE and CBK

Table 4.5 depicts the numerical relationship between the independent variable and the

predictor variables in the following resultant equation:

푅 = 24.827− 1.724퐷푒푟.푈 − 5.949퐾퐸푆푈푆퐷 − 11.213퐾퐸푆퐸푈푅푂 + 4.921퐾퐸푆퐺퐵푃

+ 5.515푅

Using P-Values to test on the individual significance; a predictor variable is said to be

linearly related with the response variable if it’s P-Value < 0.05 (5% significance level).

The findings in table 4.5 show that derivative usage; changes in the Sterling Pound; and

the market returns have a significant linear relationship with foreign exchange exposure.

The coefficients and their signs are of particular importance.The regression coefficients

shows that ∝ (the value of foreign exchange exposure when derivative usage; change in

36

the US Dollar; changes in the Sterling Pound; changes in the European Euro; and market

returns were all rated zero) is equal to -0.657. A unit increase in derivative usage led to a

decrease in foreign exchange exposure by 1.724 units. Likewise a unit increase in US

Dollar exchange rate led to decrease in forex exposure by 5.949 units. A unit increase in

the Sterling Pounds exchange rate led to a decrease in forex exposure by 11.213 units. On

the contrary, a unit increase in the exchange rate of the European Euro led to an increase

in forex exposure by 4.92 units. Similarly, a unit increase in market returns led to an

increase in foreign exchange exposure by 5.515 units. From the findings above,

derivative usage has a significant impact on the level of foreign exchange exposure at 5%

level of significance as indicated by the p values; 0.0357>.05.

37

CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This chapter summarized the analysis in chapter four and underlined the key findings. It

also drew conclusions and implications from the finding. Limitations of the study were

discussed. Finally, recommendations and suggestions for further studies were outlined.

5.2 Summary of findings

This study was conducted with the aim of establishing the effectiveness of derivatives in

managing foreign exchange exposure among commercial banks in Kenya. To achieve the

above objective, a regression analysis was conducted whereby foreign exchange exposure

was regressed against derivative usage and other control variables - log of US$; British £;

European €; and market returns). Data for the above variables was generated for 10 listed

banks in the NSE that spanned the years 2008 to 2012 (Refer appendix i). Data for both

the dependent and predictor (and control) variables were obtained from the NSE and

CBK. The two sets of data were then subjected to a regression analysis.

5.2.1The effectiveness of derivatives in managing forex exposure

When the analysis of the relationship between the individual independent variables and

foreign exchange exposure was carried out, derivative usage was found to have a

negative relationship with foreign exchange exposure meaning that an increase in

derivatives resulted in a corresponding decrease in foreign exchange exposure and vice

versa (refer to table 4.5). The relationship between derivative usage and forex exposure

38

was in addition found to be significant at as indicated by the p-value (0.0357< 0.05).This

crucial finding is consistent with the literature and is in particular consonance with Shen

and Hartarska (2013); Emm and Ince (2011); Mumoki (2009); among others.

5.3 Conclusion

The results of the study indicated that there was a significant relationship foreign

exchange exposure and derivative usage. Additionally, given the negative nature of the

relationship it means that a unit change derivative usage will significantly influence a

decrease in foreign exchange exposure.Consequently, therefore, derivative usage is

effective in management of foreign exchange exposure.

5.4 Limitations of the study

The study was unable to obtain data for one out of the ten study banks in the population.

This was occasioned by the unavailability of data from I&M bank. This study also only

used four control variables for derivative usage including three hard currencies: the

Dollar; the Pound; and the Euro, whereas other possible currencies and derivative usage

control variables that the study may not have used exist. Finally, this study is based on

2008-2012 bank returns; derivative usage; US Dollar; British Pound; European Euro; and

91 day treasury bills data for the respective 10 banks and thus interpretations deviating

from the findings of this research may occur if period is outside the study period or if

regression variables are not study variables.

39

5.5 Recommendation

This study found that the causal relationship between derivative usage and foreign

currency exposure was significant at the 5% level. On the basis of the findings, the study

recommended that since derivative usage and foreign currency exposure are significantly

and negatively related, derivatives can effectively be used by banks in Kenya to manage

foreign exchange exposure.

5.6 Suggestions for further studies

Further investigation may be done to establish the effect of other derivative usage control

variables. In addition, further inquiry may be done into why the predictor variables

exhibited the specified relationships, significance and coefficient magnitude against

foreign exchange exposure. Finally, an investigation may be done to establish the key

foreign exchange exposure variables that constitute the residuals in this study.

40

REFERENCES Adler, M., and Dumas, B. (1984). Exposure to currency risk: Definition and

measurement. Financial Management 12 (2), 41-50. Afza, T. and Atia, A. Corporate derivatives and foreign exchange risk management. The

Journal of Risk Finance, 12 (5), 409-420. Allen, F. and Santomero, A. M., (1998).The Theory of Financial Intermediation.Journal

of Banking & Finance, 21 (11), 1461-1485. Allayannis, G., &Ofek, E. (2001).Exchange Rate Exposure, Hedging, and the Use of

Foreign Currency Derivatives.Journal of International Money and Finance 20 (2001) 273–296.

Allayannis, G., & Weston, J. P. (2001).The Use of Foreign Currency Derivatives and

Firm Market Value.Review of Financial Studies 14, 243-276. Amato, J.D. & Jacob G. (2005).CDS index tranches and the pricing of credit risk

correlations. BIS Quarterly Review, March 2005, pp 73–87. Anderson, R. W. and McKay, K. (2008).Derivatives marketsinFreixas, X, P Hartmann

and C Mayer (eds), Handbook of European financial markets and institutions, Oxford University Press, Oxford, UK.

Ansi, A. &Ouda, B. (2009). How Option Markets affect price discovery on the spot markets: A survey of the empirical literature and synthesis.International Journal of Business and Management, 4 (8), 155-169.

Bartov, E. and Bodnar, G.M. (1994). Firm valuation, earnings expectations, and the

exchange-rate exposure effect. Journal of Finance, 49 (5), 1755-1785. Barton, J. (2001). Does the use of financial derivatives affect earnings management

decisions? The Accounting Review, 71(1), 1-26. Berkman, H. & Bradbury, M. E. 1996.Empirical evidence on the corporate use of

derivatives. Financial Management, vol. 25 (2), 5-14.

Bharmornsiri and Schroeder (2004).The disclosure of information on derivatives under

SFA’s No.133.Managerial Auditing Journal, 19(5), 669-680. Bodnar, G.M. and Gentry, W.M. (1993). Exchange rate exposure and industry

characteristics: evidence from Canada, Japan, and the USA. Journal of International Money and Finance, 12, 29-45.

41

Bodnar, G., Hayt, G., Marston, R., (1998). Wharton survey of financial risk management

by US non-financial firms. Financial Management 27, 70-91. Bodnar, G.M., & Wong, G. M. (2003).Exchange rate exposure and industry

characteristics:Evidence from Canada, Japan and the USA: Journal of International Money and Finance (vol.12, no.1, pp. 29–45).

Booth, L. and Rotenberg, W. (1990). Assessing foreign exchange exposure: theory and

application using Canadian firms. Journal of International Financial Management and Accounting, 2, 1-22.

Brealey, R. A., & Myers, S. C. (2003).Financing and Risk Management.McGraw-Hill

Professional. Brigham, E. & Houston, J. (2010).Fundamentals of financial management. Mason, Ohio:

South-Western Division of Thompson Learning. Carlson, J.B., Craig, B., Higgins, P. and Melick, W.R. (2006).FOMC Communications

and the Predictability of Near-Term Policy Decision, Federal Reserve Bank of Cleveland Economic Commentary, June 2006.

Central Bank of Kenya, (2012).Risk Management Survey for the Banking Sector. CBK,

Nairobi. Chamberlain, S., Howe, J. and Popper, H. (1997).The exchange rate exposure of US and

Japanese banking institutions.Journal of Banking and Finance, 21, 871-92. Chernenko, S. &Faulkender, M. (2011).The Two Sides of Derivatives Usage: Hedging

and Speculative with Interest Rate Swaps.Journal of Financial and Quantitative Analysis 46, 1727-1754.

Cherutoi , J.R. (2006).Extent of Commercial Banks Exposure to Foreign Exchange Risk.

Unpublished MBA Dissertation, University of Nairobi, Nairobi. Choi, J.J. and Prasad, A.M. (1995). Exchange risk sensitivity and its determinants: a firm

andindustry analysis of US multinationals. Financial Management, 24 (3), 77-88.

Choi, J.J., Elyasiani, E. and Kopecky, K.J. (1992). The sensitivity of bank stock returns to market, interest and exchange rate risks. Journal of Banking and Finance, 16, 983-1004.

Dufey, G., &Srinivasulu, S. (2002). The Case for Corporate Management of Foreign Exchange Risk, Financial Management, Winter ( pp. 54-62).

42

El-Masry, A. A. (2006). Derivatives use and risk management practices by UK Nonfinanacial companies.Management Finance, 32 (2), 137-159. Emm, E. E. and Ince, U. (2011). Systemic risk and competition in OTC derivatives

dealing: Evidence from client failures. Management Finance, 37 (12), 1161-1189.

Fornes, G. and Cardoza, G. (2009).Foreign exchange exposure in emerging markets.

International Journal of Emerging Markets, 4 (1), 6-25. Geczy, C., Minton, B. A. &Schrand, C. (1997).Why Firms Use Currency Derivatives.

Journal of Finance, 52 (4), 1323-1354. Giddy, I.H. (2007). Exchange Risk Whose view?, Financial Management, Summer (pp.

23-33).

Gitogo, J.K. (2012). The Relationship between Derivatives and Financial Performance of Commercial banks in Kenya.Unpublished MBA Dissertation, University of Nairobi, Nairobi.

Glaum, M. (1990).Strategic management of exchange rate risks.Long Range Planning, 23

(4), 65-72.

Grant, R. and Soenen, L. (1991). Conventional hedging: an inadequate response to long-term foreign exchange exposure. Managerial Finance, 17 (4) 1-4.

Hardwick, P. & Adams, M. (1999). The determinants of financial derivatives use in the United Kingdomlife insurance industry.ABACUS, 35(2), 163.

He, J. and Ng, L. K. (1998).The foreign exchange exposure of Japanese multinational.Corporations Journal of Finance, 53, 733-53.

Henschel, L. & Kothari, S.P. (2001). Are corporations reducing or taking risks with derivatives. Journal of Financial and Quantitative Analysis, 36, 93-118.

Jorion, P. (1990). The exchange-rate exposure of US multinationals.Journal of Business, 63 (3), 331-345.

Jorion, P. (1991). The pricing of exchange rate risk in the stock market.Journal of Financial and Quantitative Analysis, 26 (3), 363-376.

Jarrow, R. and Turnbull, S. (2000). Derivatives Securities, South-Western College Publishing.

Kenya Bankers’ Association, (2013). Khoo, A. (1994). Estimation of foreign exchange exposure: an application to mining

companies in Australia. Journal of International Money and Finance, 13 (3), 342-63.

43

Levich, R., Hayt, G., &Ripston, B. (1999).Survey of Derivatives and Risk Management

Practices by US Institutional Investors, New York University Stern Graduate School of Business, CIBC World Markets and KPMG Consulting Group, 1999.

Loudon, G. (1993). The foreign exchange operating exposure of Australian

stocks.Accountingand Finance, 33(1), 19-32. Mackay, P. and Moeller, S. (2007). The Value of Corporate Risk Management.Journal of

Finance 62:3, 1379-1419. Madura, J. (2003). International Financial Management, 7th ed., South-Western College

Publishing, Boston, MA. Mbithi, E.M. (2011). The Impact of Derivatives in the Nairobi Stock Exchange: is

Corporate Kenya ready?Unpublished MSc Dissertation, University of Nairobi. Minton, B. A., Stulz, R., & Williamson, R. (2009). How Much Do Banks Use Credit

Derivatives to Hedge Loans?Journal of Financial Services Research 35, 1-31. Mugenda, O.M., &Mugenda, A.G. (2003).Reseach methods. Quantitative and Qualitative

Approaches (pp. 46 - 48). Nairobi, Kenya: ACTS Press. Mumoki, A.N. (2009). Foreign Exchange Risk Management: Strategies and Techniques

used by Banks in Kenya to Manage Foreign Exchange Risk Exposure.Unpublished MBA Dissertation, University of Nairobi, Nairobi.

Nguyen, H. and Faff, R. (2002). On the determinants of derivative usage by Australian

companies. Australian Journal of Management, 27(1), 1-24. Nystedt, J. (2004). Derivative market competition: OTC Markets versus Organised Derivative Exchanges.IMF working paper WP/04/61. Peek, J. and Rosengren, E. (1997). Derivatives activity at troubled banks.Journal of

Financial Services Research 12, 287-302. Salifu, Z., Osei, K. A. and Adjasi, C. K. D. (2007). Foreign exchange risk exposure of

listed companies in Ghana. The Journal of Risk Finance, 8 (4), 380-393. Santomero, A. M. (1995). Financial Risk Management: The Whys and Hows; Financial

Markets, Institutions and Instruments, 4 (5), 1-14. Shapiro, A.C. (1984). A practical Method of Assessing Foreign Exchange Risk, Midland

Corporate Finance Journal (pp. 6–17).

44

Shen, X. and Hartarska, V. (2013).Derivatives as risk management and performance of Agricultural banks.Agricultural Finance Review, 73 (2), 345-367.

Ubindi, B.S. (2006). A Survey of Foreign Exchange Risk Management Practices by

Forex Bureaus in Kenya.Unpublished MBA Dissertation, University of Nairobi, Nairobi.

Whaley, R. (2008).Understanding The Derivatives Market.Vanderbilt University, Owen

Graduate School of Management, memo.

45

APPENDICES Appendix 1: List of listed commercial banks

BANKING 1 Barclays Bank Ltd. 2 CFC Stanbic Holdings Ltd. 3 Diamond Trust Bank Ltd 4 I & M Holdings Ltd 5 Kenya Commercial Bank Ltd. 6 National Bank of Kenya Ltd. 7 NIC Bank Ltd. 8 Standard Chartered Bank Ltd. 9 Equity Bank Ltd.

10 The Cooperative Bank of Kenya Ltd. Source: Nairobi Securities Exchange - 2013

46

APPENDIX II: RESIDUAL OUTPUTS

Observation Predicted Returns Residuals Standard Residuals 1 -0.32065059 0.385003 0.303733347 2 -0.241605924 0.309151 0.243892581 3 0.012467859 0.051101 0.040314045 4 -0.383196453 0.415419 0.327728901 5 -0.27439111 -0.597949 -0.471728973 6 -0.152714365 -0.794425 -0.626731562 7 0.073957681 0.028367 0.022378776 8 -0.327457978 0.441957 0.348665111 9 -0.268956511 0.201617 0.159057657

10 -0.136381224 0.120942 0.09541234 11 0.100441255 -0.110464 -0.087146749 12 -0.260840366 0.355205 0.280225232 13 -0.274535026 0.151415 0.11945311 14 -0.122781663 -0.01015 -0.008007638 15 0.072796483 -0.276165 -0.217869503 16 -0.257798206 0.526354 0.415247081 17 -0.157307073 0.133842 0.105589599 18 -0.080091481 0.108537 0.085625759 19 0.077044729 0.126079 0.099465567 20 -0.288698491 0.228804 0.180506505 21 -0.036159675 0.05983 0.04720034 22 -0.003707438 0.098348 0.077587901 23 0.158254593 0.061241 0.048313623 24 -0.15937591 0.128657 0.101498999 25 -0.045279141 0.117624 0.092795081 26 0.047067154 0.026352 0.020789587 27 0.214075493 -0.396983 -0.313184365 28 -0.132768259 0.324666 0.256133191 29 -0.208618683 0.352179 0.277837941 30 -0.226006447 0.35356 0.278927578 31 -0.07295079 0.744425 0.587285832 32 -0.438196665 -0.221223 -0.174525109 33 0.011482564 -0.883823 -0.697258105 34 0.074120989 -1.021261 -0.805684664 35 0.27539845 -0.173074 -0.136540237 36 0.026397835 0.088101 0.069504062 37 -0.229311555 0.081459 0.064263919 38 0.58934115 -0.542999 -0.428378341 39 0.131441285 -0.312301 -0.24637826 40 0.300040613 -0.398814 -0.314629451 41 -0.199702969 0.12315 0.097154654

47

42 0.802831668 -0.590443 -0.465807686 43 0.185145357 -0.380416 -0.300114453 44 0.287967255 -0.289382 -0.228296642 45 -0.119468201 0.089363 0.070499695 46 0.757721375 -0.701763 -0.55362946 47 0.368096736 -0.507766 -0.400582606 48 0.601111408 -0.724821 -0.571819911 49 -0.19144459 -0.581362 -0.45864332 50 0.560397754 -0.648672 -0.51174497 51 0.147695297 6.958308 5.489491902 52 0.337867402 -0.357815 -0.282284687 53 -0.137040758 -0.204873 -0.16162645 54 0.680936851 -0.5249 -0.414100069 55 0.160528857 -0.24047 -0.189709879 56 0.414138638 -0.669189 -0.527931253 57 -0.057203829 0.632968 0.499355989 58 0.883756371 -0.072785 -0.057421157 59 0.211491804 -1.026246 -0.809617727 60 0.460280187 -0.553913 -0.436988542 61 -0.046268184 -0.032219 -0.025418239 62 0.899413002 -0.490402 -0.386884262 63 0.399919237 -0.598191 -0.471920089 64 0.381752692 -0.442816 -0.349342603 65 -0.203042249 0.053242 0.042003102 66 0.744714429 -0.583141 -0.460046997 67 0.054374366 -0.23595 -0.186143399 68 0.373770536 -0.479964 -0.378649099 69 -0.135907786 0.182895 0.144287867 70 0.718891543 -0.393124 -0.310139826 71 0.126863171 -0.149391 -0.117856309 72 0.293971217 -0.227251 -0.179281181 73 -0.332453685 0.139284 0.109883141 74 0.26497147 0.084136 0.066376183 75 0.144636306 -0.362813 -0.28622781 76 0.277213217 -0.236963 -0.186943247 77 -0.240553193 0.104475 0.082421607 78 0.644704118 -0.468987 -0.369989034 79 0.223389252 -0.314194 -0.247871255 80 0.323841699 -0.323691 -0.255363782 81 -0.40727517 -0.550866 -0.434584897 82 0.544060004 -0.375149 -0.295959307 83 0.163959394 -0.388362 -0.30638371 84 0.214007402 -0.066802 -0.052701232 85 -0.339975705 -0.908151 -0.716451288 86 0.555183696 -0.514843 -0.406165985

48

87 0.265303106 -0.400274 -0.315780674 88 0.172021849 -0.144667 -0.114129605 89 -0.331433646 0.116791 0.092138019 90 0.517673363 -0.162749 -0.128394712 91 0.329320501 -0.503768 -0.397428499 92 0.207281901 -0.328888 -0.25946353 93 -0.268385555 0.106802 0.084257754 94 0.404294832 -0.092256 -0.072781649 95 0.482822852 -0.516004 -0.407081428 96 0.340169303 -0.35833 -0.282690982 97 -0.215520823 -0.131527 -0.103763186 98 0.740305373 -0.389848 -0.307555332 99 0.319716265 -0.43993 -0.347065699

100 0.365092651 -0.253516 -0.200002014 101 -0.356108005 0.008794 0.006937349 102 0.537572326 -0.246408 -0.194394377 103 0.14141014 -0.340282 -0.26845258 104 0.130074469 -0.131928 -0.104079787 105 -0.3851892 -0.111629 -0.088065339 106 0.498802914 0.128832 0.101637422 107 0.341843174 -0.522154 -0.411933378 108 0.234613028 -0.194151 -0.153167712 109 -0.073590001 0.182318 0.14383307 110 -0.822932961 1.031062 0.813417101 111 -0.51912744 0.630524 0.497427817 112 0.214665022 -0.326115 -0.257276048 113 0.079802054 -0.003209 -0.002531255 114 -0.74838872 0.592403 0.467353534 115 -0.388610001 0.463053 0.365308224 116 0.284764475 -0.193098 -0.152337152 117 -0.05592461 0.179135 0.141321419 118 -0.821495166 0.975775 0.769800843 119 -0.531993004 0.748389 0.590413052 120 0.211806727 -0.259601 -0.204802186 121 -0.103684785 0.20653 0.162934137 122 -0.831855647 1.286801 1.015172576 123 -0.53089141 0.633106 0.499465088 124 0.199763769 -0.199764 -0.157596007 125 -0.059518327 -0.110186 -0.086927345 126 -0.801451853 1.480537 1.168013061 127 -0.491465673 0.663146 0.523163934 128 0.180269753 -0.320318 -0.252702788 129 0.136047471 -0.010254 -0.00808936 130 -0.640083567 0.768509 0.606285741 131 -0.388558886 0.59446 0.468976157

49

132 0.271430909 -0.07723 -0.060927374 133 0.162322427 0.290789 0.22940685 134 -0.573271233 0.238299 0.187996617 135 -0.341895787 0.291425 0.229908766 136 0.313433976 -0.284881 -0.224745619 137 -0.112879438 0.167424 0.132082924 138 -0.825290581 1.014831 0.800612515 139 -0.525500456 0.679188 0.535819355 140 0.08340544 -0.070127 -0.055324063 141 -0.067870626 0.176097 0.138924671 142 -0.8713031 1.312328 1.035311289 143 -0.520326777 0.699396 0.55176168 144 0.133385738 -0.060634 -0.04783515 145 0.172343643 -0.210985 -0.166448829 146 2.293231982 -2.911366 -2.296811109 147 -0.254409939 -0.186753 -0.147331438 148 -0.08088449 0.368081 0.290383244 149 0.401547092 -0.90955 -0.71755506 150 2.256610312 -0.71132 -0.561168423 151 0.017913143 -0.372754 -0.294070351 152 0.198264301 -0.139931 -0.110392833 153 0.347627026 -1.212276 -0.956379294 154 2.577407453 11.47437 9.052269508 155 -0.05347144 -0.206166 -0.162647049 156 0.225458688 -0.3246 -0.256080863 157 0.089094667 -0.11726 -0.092507475 158 2.04358966 -2.02884 -1.600576106 159 -0.313024279 -0.0405 -0.031950743 160 -0.012121399 -0.04568 -0.036037198 161 0.150329881 0.651205 0.51374363 162 2.161645765 -2.887401 -2.277905255 163 -0.240690483 0.118276 0.093309477 164 0.037462125 -0.133992 -0.105708193 165 0.190420283 -0.908321 -0.716585298 166 2.388875394 2.199978 1.735588469 167 -0.15307889 -0.055507 -0.043790106 168 0.065118604 -0.127724 -0.10076337 169 0.410762718 -0.217254 -0.171394336 170 2.538205523 -2.800812 -2.209593771 171 -0.018114245 -0.352151 -0.277815918 172 0.198716578 -0.316127 -0.24939603 173 0.3248301 -1.139874 -0.899259788 174 2.563395355 5.735088 4.524478937 175 -0.081285162 -0.276246 -0.217934077 176 0.12058729 -0.331698 -0.261680803

50

177 0.07960314 -0.933025 -0.736074477 178 2.308778106 -1.681143 -1.326273479 179 -0.257087943 0.076777 0.060570485 180 -0.001658006 0.04212 0.03322931