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Foreign Exchange Risk Management in Commercial Banks of Pakistan

A Thesis Presented

by Maroof Hussain Sabri

(Registration Number: MAF 05091057) to

The Committee on Academic Degrees in partial fulfillment of the requirements

for a degree with honors of

MS Accounting & Finance

Business School, The University of Lahore

April, 2011

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The Thesis committee for Mr. Maroof Hussain Sabri certifies that this is the approved version of the following thesis: Foreign Exchange Risk Management in Commercial Banks of Pakistan

APPROVED BY SUPERVISING COMMITTEE:

Supervisor: ________________________________________ Ramiz Rehman

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Acknowledgements

I extend my sincere gratitude to my supervisor Mr. Ramiz Rehman who has been very friendly and cooperative throughout the course of this study. Without him, simply this piece of work would have not been possible. One simple cannot wish for any better or friendlier supervisor than him. I also thank to Mr. Akram for assisting in understanding certain techniques during this study.

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Abstract

The purpose of this study is to explore different aspects of foreign exchange risk management by the commercial banks of Pakistan. As there has been no previous significant work done on this particular topic, this study tries to explore different characteristics of Net foreign currency exposure, practices and tools used by commercial banks in this regard & income from foreign currencies of commercial banks. Different techniques and statistical procedures are used during this study including descriptive analysis, simple and multiple linear regression, binary logistic regression and independent sample t-tests. On the base of findings from the data of 110 banks listed on the Karachi stock exchange for the period 2005 to 2009 different conclusions are drawn. Commercial banks of Pakistan are exposed to foreign exchange risk and have a set of practices to manage this risk. Income from dealing in foreign currencies is also a substantial potion of total income of banks. Further conclusions are drawn regarding the dependence of Net foreign currency exposure, tools usage and income impact on other factors like ownership status, type of bank, size of bank, net assets of banks and exchange rate volatility.

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Table of Contents Abstract ................................................................................................................... 4

List of Symbols ....................................................................................................... 8

List of Tables .......................................................................................................... 9

List of Models ......................................................................................................... 9

Introduction ........................................................................................................... 10

Foreign Exchange ............................................................................................. 10

Foreign Exchange Market ................................................................................. 10

Exchange Rate .................................................................................................. 10

Foreign Exchange Regimes .............................................................................. 10

Foreign Exchange Risk ..................................................................................... 11

Foreign Exchange Risk in Commercial Banks ................................................. 11

Foreign Currency Exposure of a Commercial Bank ......................................... 12

Exchange Rate Volatility .................................................................................. 12

Foreign Exchange Risk Management ............................................................... 12

Hedging ............................................................................................................. 13

Central Bank’s Role in Foreign Exchange Risk Management ......................... 14

Foreign Exchange Risk & Its Association With Other Types of Risks ............ 14

Research Objectives .......................................................................................... 15

Literature Review.................................................................................................. 16

Methodology & Variables Construction ............................................................... 20

Time Horizon .................................................................................................... 20

Sample............................................................................................................... 20

Limitation of Scope of Research....................................................................... 21

Data ................................................................................................................... 21

Foreign Currency Exposure of Commercial Banks in Pakistan ....................... 21

1. Net Foreign Currency Exposure ......................................................... 21

2. Factors that Affect Foreign Currency Exposure ................................. 22

3. Comparison of Net Foreign Currency Exposure of Public Sector & Private Commercial Banks ........................................................................... 25

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4. Comparison of Net Foreign Currency Exposure of Islamic & Conventional Commercial Banks ................................................................. 26

Different Tools & Instruments Used by Commercial Banks in Pakistan to Manage Foreign Exchange Risk ....................................................................... 28

5. Tools & Instruments Used by Commercial Banks in Pakistan .......... 28

6. Factors Influencing Usage of Foreign Exchange Risk Management Tools 28

Foreign Exchange Risk Management & its Impact on Income ........................ 32

Study the descriptive of Income from dealing in foreign currencies in both Islamic & Conventional Banks & Comparison Between them .................... 32

Study the descriptive of Income from dealing in foreign currencies in both Public Sector Commercial Banks and Local Private Banks & Compare them....................................................................................................................... 32

Income from dealing in foreign currencies and size of bank ........................ 33

Effects of tools used on Income from Dealing in Foreign Currencies ......... 33

Income from dealing in foreign currencies and Exchange Rate Volatility ... 35

Findings & Analysis ............................................................................................. 38

Findings on Net Foreign Currency Exposure of Commercial Banks in Pakistan........................................................................................................................... 39

1. Net Foreign Currency Exposure ......................................................... 39

2. Findings on “Factors Affecting Foreign Currency Exposure ............. 39

3. Relationship Between NFX & Net Assets .......................................... 42

4. Comparison of Net FX Exposure of Commercial banks in Private Sector & Public Sector .................................................................................. 44

5. Comparison of Net Foreign Currency Exposure of Islamic Vs Conventional Banks ...................................................................................... 45

Findings on Usage of Different Tools for Foreign Exchange Risk Management........................................................................................................................... 47

5. Foreign Exchange Risk Management: Tools & Practices .................. 47

6. Currency Derivatives Usage ............................................................... 48

Findings on Factors that affect Currency Derivative Usage ......................... 50

Findings on Income from Dealing in Foreign Currencies and its Relationship with other Factors ............................................................................................. 53

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Findings on Income from dealing in foreign currencies and Type of commercial banks (Conventional & Islamic) ............................................... 53

Comparison of IFX of Conventional & Islamic Banks ................................ 53

Findings on Income from dealing in foreign currencies and Ownership Status of commercial bank (PSCB and LPB) .......................................................... 54

Comparison of IFX between Public Sector Commercial Bank & Local Private Banks ................................................................................................ 54

Findings on Income from dealing in foreign currencies and Size of Bank as measured by Net Assets ................................................................................ 54

Findings on Income from dealing in foreign currencies and Currency Derivatives used by commercial banks ......................................................... 57

Findings on Effect of Exchange Rate Volatility on Income from Dealing in Foreign Currencies ........................................................................................ 58

Conclusion ............................................................................................................ 61

References ............................................................................................................. 63

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List of Symbols

Below is the list of symbols used:

Symbols Explanation

NFXNA Net Foreign Currency Exposure Relative to Net Assets NFX Net Foreign Currency Exposure OS Ownership Status or Nature of Ownership ERV Exchange rate volatility NEER Nominal Effective Exchange Rate NA Net Assets PSCR Public Sector Commercial Banks LPB Local Private Banks IFX Income from foreign currencies as a percentage of total income

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List of Tables TABLE I: NO. OF COMMERCIAL BANKS, OPERATING IN PAKISTAN & LISTED ON KSE, 2005-2009 ...................... 20

TABLE II: NFXNA, DESCRIPTIVE STATISTICS ............................................................................................... 39

TABLE III: CORRELATION BETWEEN OS, SIZE & ERV .................................................................................... 40

TABLE IV: MULTIPLE LINEAR REGRESSION OUTPUT OF RELATIONSHIP BETWEEN "NFXNA & "SIZE, OS & ERV" .... 41

TABLE V: OUTPUT FOR REGRESSION: NFX & NA ....................................................................................... 43

TABLE VI: RESULTS OF INDEPENDENT SAMPLE T-TEST TO COMPARE NFX OF PSCR & LPB .................................. 45

TABLE VII: RESULTS OF INDEPENDENT SAMPLE T-TEST TO COMPARE NFXNA FOR ISLAMIC & CONVENTIONAL BANKS

............................................................................................................................................... 46

TABLE VIII: RESULTS OF INDEPENDENT SAMPLE T-TEST TO COMPARE NFXNA FOR ISLAMIC & CONVENTIONAL BANKS

............................................................................................................................................... 46

TABLE IX: DESCRIPTIVES: CURRENCY DERIVATIVES USAGE ............................................................................ 48

TABLE X: CURRENCY DERIVATIVES USAGE BY OWNERSHIP STATUS ................................................................. 49

TABLE XI: CURRENCY DERIVATIVE USAGE BY TYPE OF BANK .......................................................................... 49

TABLE XII: FACTORS THAT AFFECT CURRENCY DERIVATIVE USAGE: RESULT OF BINARY LOGISTIC REGRESSION ......... 51

TABLE XIII: LOGITS & ODDS RATIO RESULTS OF BINARY LOGISTIC REGRESSION ................................................. 52

TABLE XIV: DESCRIPTIVES FOR IFX AND IFXRS BY TYPE OF BANK ................................................................... 53

TABLE XV: DESXRIPTIVE STATISTICS FOR IFX AND IFXRS BY OWNERSHIP STATUS .............................................. 54

TABLE XVI: DESCRIPTIVE STATISTICS FOR IFX AND IFXRS BY OWNERSHIP ........................................................ 54

TABLE XVII: DESCRIPTIVE STATISTICS OF IFX AND IFXRS BY OWNERSHIP STATUS OF BANK ................................. 54

TABLE XVIII: OUTPUT OF REGRESSION: IFXRS ON NA ................................................................................. 55

TABLE XIX: OUTPUT OF REGRESSION: IFX ON NA ....................................................................................... 56

TABLE XX: REGRESSION OUTPUT IFX ON TOOLS .......................................................................................... 57

TABLE XXI: REGRESSION OUTPUT IFXRS ON ERV ....................................................................................... 58

TABLE XXII: REGRESSION OUTPUT OF IFX ON ERV ...................................................................................... 60

List of Models MODEL 1: NFX DEPENDS ON SIZE, OWNERSHIP & EXCHANGE RATE VOLATILITY .............................................. 22

MODEL 2: RELATIONSHIP BETWEEN NFX & NET ASSETS ............................................................................. 25

MODEL 3: BINARY LOGISTIC MODEL FOR CURRENCY DERIVATIVE USAGE ........................................................ 30

MODEL 4: CALCULATION OF P USING BINARY LOGISTIC REGRESSION FOR CURRENCY DERIVATIVE USAGE .............. 31

MODEL 5: MODEL 1 OF RELATIONSHIP BETWEEN NET ASSETS & IFXRS ......................................................... 33

MODEL 6: MODEL 2 OF RELATIONSHIP BETWEEN NET ASSETS & IFX ............................................................. 33

MODEL 7: RELATIONSHIP BETWEEN TOOLS USED AND IFX ........................................................................... 34

MODEL 8: MODEL 1 OF RELATIONSHIP BETWEEN ERV& IFXRS ................................................................... 35

MODEL 9: MODEL 2 OF RELATIONSHIP BETWEEN ERV & IFX ....................................................................... 35

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Introduction

Foreign Exchange

Foreign Exchange (FX) is the conversion of currency of one country to the currency of other country whereas foreign currency is any currency other than the country’s own currency. For example, Pakistani Rupee (PKR) is the currency of Pakistan and US Dollar (US$) is Foreign Currency in Pakistan whereas conversion of PKR into US$ is Foreign Exchange.

In Pakistan, Foreign Exchange Act, (Section 2), 1947 defines Foreign Exchange as, “means includes any instrument drawn, accepted, made or issued under clause (8) of section 17 of the State Bank of Pakistan Act, 1956, all deposits, credits and balance payable in any foreign currency, and any drafts, traveler’s cheques, letters of credit and bills of exchange, expressed or drawn in Pakistan currency but payable in any foreign currency;”

Foreign Exchange Market

Foreign Exchange Market is a market where the currencies are traded and is the world’s biggest market across the globe. In this market, the price of one unit of a currency is determined in the units of other currency. Major participants of FX Market are commercial banks, central banks, governments, interbank brokerage houses, exchange companies, people travelling abroad or receiving remittances from other countries & money changers.

Exchange Rate

Exchange Rate refers to the price paid in one currency to acquire the one unit of foreign currency or the foreign currency received to sell one unit of currency.

Foreign Exchange Regimes

How a country manages is its own country and the FX market in its country is the exchange rate regime that is being followed by the said country. A country can follow any of the below mentioned exchange rate regimes:

• Fixed • Freely Floating

• Managed Float • Pegged

Pakistan has shifted its exchange rate system from Managed Float to Market Based Floating Exchange Rate System. Here, the commercial banks & authorized dealers are free to hold and conduct transactions in foreign currencies.

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Foreign Exchange Risk

Foreign Exchange risk arises when a bank holds assets or liabilities in foreign currencies and impacts the earnings and capital of bank due to the fluctuations in the exchange rates. No one can predict what the exchange rate will be in the next period, it can move in either upward or downward direction regardless of what the estimates and predictions were. This uncertain movement poses a threat to the earnings and capital of bank, if such a movement is in undesired and unanticipated direction.

Foreign Exchange Risk can be either Transactional or it can be Translational. When the exchange rate changes unfavorably it give rise to Transactional Risk, as the name implies because of transactions in Foreign Currencies, can be hedged using different techniques. Other one Translational Risk is an accounting risk arising because of the translation of the assets held in foreign currency or abroad.

Foreign Exchange Risk in Commercial Banks

Commercial banks, actively deal in foreign currencies holding assets and liabilities in foreign denominated currencies, are continuously exposed to Foreign Exchange Risk. Foreign Exchange Risk of a commercial bank comes from its very trade and non-trade services.

Foreign Exchange Trading Activities (Saunders & Cornett, 2003)include:

1. The purchase and sale of foreign currencies to allow customers to partake in and complete international commercial trade transactions.

2. The purchase and sale of foreign currencies to allow customers (or the financial institution itself) to take positions in foreign real and financial investments.

3. The Purchase and sale of foreign currencies for hedging purposes to offset customer (or FI itself) exposure in any given currency.

4. To purchase and sale of foreign currencies for speculative purposes base on forecasting or expecting future movements in Foreign Exchange rates.

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 upto 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.

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The abovementioned trade activities are the typical trade activities of a commercial bank and all of these activities do not involve risk exposure of the bank. The first 1 & 2 activities are done by the commercial bank on behalf of its customers and the foreign exchange risk is transferred to the customers as the bank takes Agency Role in this case. Third activity of bank involves hedging and there is no risk in this as well as the bank has hedged its risk by pre-determining the exchange rate with other financial institutions using different financial instruments. The fourth one involves the risk which may result in the gain or loss due to unexpected outcome. Ready, spot, forward & swap are the principal FX related contracts whereas banking products and services in foreign exchange give rise to non-traded foreign currency exposure.

Foreign Currency Exposure of a Commercial Bank

Any unhedged 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 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. Sum of all the Net Asset positions & Net Liability positions is known as Net Open Position or Net 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 unhedged position of bank in all the foreign currencies. A negative figure shows Net Short Position whereas positive figure shows Net Open Position.

Exchange Rate Volatility

There is a real time fluctuation in floating exchange rate. The Exchange rate volatility measures the degree to which the exchange rate fluctuates or varies over a period of time. Exchange rate is said to be more volatile if there are more frequent ups and downs or less volatile if there are lesser changes in it over a period of time.

Foreign Exchange Risk Management

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Whenever a commercial bank deals in foreign currency, it is exposed to risk of exchange rate. When these transactions are done on the behalf of customers, the risk is also transferred to them and the bank has no exposure. Bank’s assets & liabilities in foreign currencies or assets and liabilities in other countries give rise to Foreign exchange risk which has to be managed by the bank.

Hedging

Foreign exchange risk is mitigated by using different hedging techniques. Hedging is a way by using which a bank eliminates or minimizes its risk exposure. Hedging can be done using different ways:

1. Foreign Currency Assets & Liabilities Matches: A commercial bank matches its assets and liabilities in foreign currencies to ensure a profitable spread by dealing in FX. By using this technique the positive profit spread is ensured regardless of the movements in exchange rate at the respective maturities of these assets and liabilities, in the investment period. For example, if a bank has a liability in shape of a deposit for one year in US$ at rate of 3% p.a. and it has another liability of same type but in PKR @ 10% p.a., it can match its assets with these liabilities by advancing US$ at rate of 4.5% p.a. and PKR @ 15% p.a. Using this the bank has locked into the profit of spread. Bank will get US$ & PKR to repay the principal and exchange rate will not affect the cost of exchanging the currencies.

2. Hedging using Derivatives: A commercial bank uses foreign currency derivatives to hedge foreign exchange risk. Foreign currency derivatives are:

a. Foreign Currency Futures b. Foreign Currency Swap c. Foreign Currency Options d. Foreign Currency Forward Contracts

The most popular amongst all others as mentioned above are FX forward Contracts. Instead of matching FX asset-liability bank enters into a forward contract having the same maturity. For example in above examples bank does not need to advance loans in the same currency rather it uses forward contracts to insulate FX risk. An important feature of such contracts is that they do not appear on the balance sheet of the bank instead it appears under the head of Contingencies & Commitments and hence are off-balance sheet items.

3. Hedging through Diversification of Foreign Asset-Liability Portfolio: Commercial Banks try to mitigate the foreign currency risk on its individual currency by holding Multicurrency Asset-Liability Positions. Holding assets and liabilities in various foreign currencies does not reduce the risk of the portfolio of assets and liabilities of a bank alone but also significantly lower

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the cost of capital. The risk of holding any net open position in a currency is diversified by holding a position in foreign currency. The main reason for this is the differential inflation and interest rates in different countries. Almost all commercial banks hold such type of multicurrency asset-liability portfolios.

Central Bank’s Role in Foreign Exchange Risk Management

Central Banks across the globe continuously strive to achieve the financial stability in their respective economies. Nearly all the central banks issue guidelines for Risk Management in the commercial banks which they have to follow. State Bank of Pakistan has also issued a comprehensive set of guidelines for the management of different types of risk faced by commercial banks including foreign exchange risk. These guidelines provides the minimum requirement and procedures to manage risks faced by a commercial bank and focus on establishing Risk Management Committee & Asset Liability Management committee by banks, setting limits for the open positions, measurement & control of risk , independent audit of risk management process and role board of directors & management.

Foreign Exchange Risk & Its Association With Other Types of Risks

FX risk is not only the impact of adverse exchange rate movements on the earnings of the bank due to different open positions held; it impacts the earnings & capital of bank in different ways.

As per Risk Management Guidelines published by State Bank of Pakistan for Commercial banks & DFIs, Foreign exchange risk also exposes a bank to Interest Rate Risk due to the mismatches in the maturity pattern of foreign assets and liabilities. Even if the maturities of different assets and liabilities are properly matched, mismatches in the maturities of forward positions taken by bank also expose it to interest rate risk. Since the banks hold assets and liabilities in foreign currency, it also poses a serious risk of Counterparty (default) Risk, although in such case there is no principal is at stake due to the notional principal of the contracts but still the bank has to enter into different spot and forward positions to cover such failed transactions. In this case bank faces replacement cost depending upon the exchange rates at that time. The forex transactions with the parties situated outside the home country also lead to Time Zone Risk, risk arising because of difference of settlement time between the markets in two different time-zones, and Sovereign or Country Risk.

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Research Objectives

The study focuses on the below mentioned areas and try to get the answers to the following questions, regarding the foreign exchange risk management in commercial banks in Pakistan:

1. Do the Pakistani commercial banks face foreign exchange risk. Study the foreign exchange risk exposure of commercial banks in Pakistan. Whether the Foreign Currency exposure of Commercial Banks in Pakistan depends on Ownership Status (public sector commercial bank or local private bank), its type (conventional or Islamic), its size and exchange rate Volatility? Is there any difference between the foreign currency exposure of conventional banks and Islamic banks.

2. How do Pakistani commercial banks manage foreign exchange risk? What are the different tools & instruments used by the commercial banks in Pakistan to manage foreign currency risk faced by them? � Are there any tools used by the commercial banks in

Pakistan? � If yes, what are the tools used by them? � Do all the banks use same tools?

3. What are the currency derivatives which are being used by the commercial banks in Pakistan? Does the usage of these tools depend on its ownership status, its type, Size of Bank & Exchange Rate Volatility?

4. Study the income from dealing in foreign currencies by the commercial banks in Pakistan? Compare the income from dealing in foreign currencies of commercial banks in Pakistan between different ownership categories (public sector commercial banks and local private banks and types (conventional and Islamic). Does using different mix of currency derivatives have any effect on the income of the bank? Does size of bank or exchange rate volatility have any effect on income from dealing in foreign currencies?

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Literature Review

There has been not any significant work done on Foreign Exchange Risk Management in Commercial Banks in Pakistan before. There is also not any sufficient literature available on this specific topic. Different work has been done at different times regarding various topics included in the research objective of this study. An overview of the existing literature is given here.

The importance of foreign exchange risk management can not be neglected for any firm or banking organization. Banks face foreign exchange risk management due to dealing in foreign currencies result of the operations in foreign countries or dealing with foreign exchange for their own account or for customers account. Exchange Rate Risk is an integral part of every firm’s decision regarding foreign currency exposure (Allayannis, Ihrig, & Weston). Currency risk hedging strategies involve eradicating or reducing currency risk, and need understanding of both the ways that the exchange rate risk can impact the operations of economic agents and techniques to deal with the resulting risk implications (Barton, Shenkir, & Walker, 2002). Foreign Currency Risk is an important source of risk for the banking industry and different studies have been done in different parts of the world. (Papaioannou M. G., 2006) Foreign currency exposure and risk management is very important for the firm to avoid any vulnerability from exchange rates fluctuation which can affect the profits and assets values in a negative way. Different traditional types of foreign exchange risk i.e. translational, transactional and economic risks were reviewed. Also different ways and strategies for managing foreign currency risk were analyzed along with advantages and disadvantages of each strategy and technique. Additionally, best practices widely spread were outlined along with data on financial derivatives and hedging practices by US firms.

Sources of risks for banking sector have been investigated by many researchers in different economies. (Daugaard & Valentine, 1993) worked out different sources of risks and they found out that stock prices of banks have relationship with different variables like interest rates, exchange rates, banks profitability and market risk factor. According to them during the period from 1983 to 1991, share prices of banks responded with the appreciation of the Australian Dollar.

(Irio & Faff, 2000) Studied foreign exchange risk in industries in Australia including the banking sector. According to them, banking industry as a whole do effective foreign exchange risk management and therefore, this type of risk is insignificant in pricing banking companies stocks.

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A study conducted on 48 largest US commercial banks (Choi, Elyasiani, & Kopecky, 1992) for the period 1975-1987 showed that effects of exchange rate depend on the Net position of the bank in foreign currencies. According to them, when banks had positive net position, depreciation of foreign currencies negatively affected the stock prices of banks before year 1979 and after 1979 banks stock returns responded positively with the depreciation of foreign currencies as banks had changed from positive to negative net open positions. In a similar study on Canadian banks (Atindehou & Gueyie, 2001), it is found out for the Canadian Banks that stock prices responded positively with depreciation of foreign currencies.

Foreign Exchange Risk is also found out to be one of the major sources of risks in African Region. (Walter & Tewodros, 2004) investigated the foreign currency exchange rate exposure of the major commercial banks in South Africa with the help of augmented market model. According to this study, all the major four banks in South Africa exhibit the foreign exchange risk and the Net Asset position in foreign currencies is a weak predictor of foreign exchange risk.

(Shamsuddin, 2009) mentioned that adoption of flexible exchange rate regime in 1983 along with financial system globalization have exposed Australian Banks to new risks along with new opportunities. According to him small banks are immune to changes in interest and exchange rate.

Choosing the suitable hedging strategy is often a difficult task due to the difficulties involved in measuring precisely current risk exposure and deciding on the suitable degree of risk exposure that ought to be shielded. The need for foreign exchange risk management began to arise after the fall of the Bretton Woods system and at the end of the United States dollar peg to gold in 1973 (Papaioannou M. , 2001) The issue of foreign exchange risk management for firms in non-financial sector is independent from their principal business and is usually independently handled with by their corporate treasuries. In most of the firms there are independent committees who function to oversee the treasury’s strategy in managing the foreign exchange risk (and interest rate risk) (Lam, 2003). It clearly shows the importance of the fact that firms give a significant attention to risk management issues and techniques. Contrariwise, international investors usually use their underlying assets and liabilities to manage foreign exchange risk. Since the currency exposure of international investor is majorly related to translation risks on assets and liabilities held in foreign currencies, they tend to consider foreign currencies as a separate asset class, totally separate from other assets, requiring a currency overlay mandate (Allen, 2003).

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Banks use Derivatives to manage foreign currency risk. A review of literature on usage of derivatives and banks’ foreign exchange risk is given here.

There is much of literature which shows that foreign currency management tools significantly reduce foreign currency exposure. One of such study conducted on it (Allayannis, George, Ofek, & Eli, 2001), using S & P 500 non financial firms with the help of multivariate analysis suggested that with the use of foreign currency derivatives, foreign exchange risk is significantly reduced.

(Hue Hawa Au Yong, Faff, & Chalmers, 2006) Investigated derivative activities in banks in the Asia Pacific region and tried to discover that level of derivative usage is linked with the perceptions of market about interest rate and exchange rates. They did not find any significant relationship between derivative activities of banks and exposures.

Hedging allows the commercial banks to manage foreign exchange risk but hedging itself poses additional risk to bank. (Gandhi G. S., 2006) in the paper for “The Chartered Accountant” for Instt. Of Chartered Accountants of India is mentioned that currency derivatives like currency futures, currency forwards, currency swaps and currency options help in hedging foreign exchange risk of firms and other ways of hedging including off-setting positions against the underlying assets and money markets are themselves risky. Hedging and hedging right are two different things. If the hedging is not done properly in the right way, it itself can become a serious source of risk and have potential to pose a serious financial loss to the firm.

Fluctuations in the foreign exchange rate force the changes in the portfolio returns as uncertain future exchange rates translate the returns on investments denominated in foreign currencies into US dollar returns. 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 of the firms and institutes, however, due to information efficiency resultant of usage of currency derivatives makes the currency markets more efficient and exchange rates less forecast able (Liu, 2007)

“Foreign Currency options” are the derivative instruments that gives 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

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the option the flexibility of exercising settlement of that option or not. The article focused on the dynamics of hedging foreign exchange risk with the usage currency options applications. Indeed, the 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 G. S., 2006)

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Methodology & Variables Construction

This section explains a detailed view of Data, Time Period, Sampling, various statistical techniques and procedures used in the study.

Time Horizon

Time span for this study is five years period from 2005-2009. The main reason for taking this period into account is that State Bank of Pakistan reportedly allowed the use of Derivatives, critical for the management of risk management by commercial banks, by the December, 2004. By allowing such complex instruments by state bank of Pakistan has enabled commercial banks to have an equal access to the market. Therefore, this study takes into account the period from 2005 to 2009.

Sample

Sample data has been used in this study and sample consists of all the Commercial Banks Listed on Karachi Stock Exchange, Karachi. Only Public Sector Commercial Banks and Local Private Commercial Banks are listed on Karachi Stock Exchange. There exists a difference between the number of commercial banks operating in Pakistan and the number of commercial banks listed on the Karachi Stock Exchange.

Table i: No. of Commercial Banks, Operating in Pakistan & Listed on KSE, 2005-2009

Year No. of Commercial Banks in Pakistan (Excluding

Foreign Banks)

No. of Commercial Banks Listed on KSE

2005 24 20 2006 26 22 2007 30 26 2008 29 25 2009 29 25 Total 138 118 Missed Banks 8 Total Sample Size 110

The banks which are not incorporated in Pakistan and are working here, Foreign Banks, are not included because of certain limitations. Hence, this study does not study Foreign Exchange Risk Management by Foreign banks in Pakistan.

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Limitation of Scope of Research

i. Foreign banks are not included in this study; hence, any findings do not apply to Foreign Banks.

Data

Data used in this study is all of secondary nature. Annual reports of the commercial banks are the major source of data along with various Statistical Bulletins & publications by the State bank of Pakistan.

Foreign Currency Exposure of Commercial Banks in Pakistan

Foreign Currency Exposure of Commercial Banks gives an indication of the foreign exchange risk assumed by bank.

Holding Net Asset or Net Liability in a currency gives rise to foreign currency exposure into that currency. Banks hold multiple foreign currencies at a time and sum of all the net positions in all the currencies held by a commercial bank is Net Foreign Currency Exposure of the bank. This foreign currency exposure varies from bank to bank and an attempt is made to understand different factors which influence foreign currency exposure of a commercial bank.

To understand the foreign currency exposure of commercial banks in Pakistan, below mentioned questions are designed:

1. Whether Commercial Banks in Pakistan take any foreign currency exposure or not? If YES, does this exposure vary from bank to bank or there is some fixed rule set to this?

2. Does there exist any relationship between Foreign Currency exposure & other factors like Ownership Status (Type), Size of Bank and Exchange Rate Volatility? Which of these factors is most significant?

3. Does Foreign Currency Exposure of Public Sector Commercial Banks differ from than those of Local Private Banks?

4. Does Foreign Currency Exposure faced by Islamic Commercial banks vary from those of Conventional commercial banks?

Answers to the above mentioned research questions can be achieved by using different statistical and econometric techniques. The detailed methodology is given below:

1. Net Foreign Currency Exposure

The very first research question is to check whether there is any Net Foreign Currency Exposure assumed by the commercial banks in Pakistan. For this purpose, Annual Financial Statements of listed commercial banks are studied. As

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per the statutory requirements, all the banks operating in Pakistan including commercial banks have to mention in the NOTES to Financial statements “Net Foreign Currency Exposure” in Pakistani Rupees, the calculated net position by bank, under the heading of “Foreign Exchange Risk”. Whether this Net Foreign Currency Exposure varies from bank to bank or there is a set rule for all the banks? If a bank has zero Net Foreign Currency Exposure, it means it has all of his assets and liabilities hedged and offset against other currencies or in the same currency. It can be analyzed either relative to Total Assets or Net Assets of the bank; however, it is more appropriate to analyze it with its relativeness to Net Assets. Therefore, a new variable is constructed i.e. “Net Foreign Currency Exposure relative to Net Assets”, denoted by “NFXNA”. Descriptive are studied for this variable “NFXNA” i.e. Max, Min, Mean & Standard Deviation.

2. Factors that Affect Foreign Currency Exposure

Does this variable NFXNA depends on different factors like Ownership Status, Size of Bank & Exchange Rate Volatility and which of these factors is the most significant one?

Using Linear Regression to Explore Relationship between NFX and Size,

Ownership Status & Exchange Rate Volatility

The below mentioned Regression Model is used to find out the relationship between Foreign currency exposure and the factors that influence it:

����� = � + ������� + ����� + ����� � + �

Model 1: NFX depends on Size, Ownership & Exchange Rate Volatility

Where

NFXNA = NFX relative to Net Assets Size = Size of the Bank OS = Ownership Status of Bank ERV = Exchange rate volatility α = Population parameter, intercept β = Population parameter, slope, regression coefficient

Hypothesis Testing:

Using this model below mentioned hypothesis will be tested to check the significance of the model as a whole along with individual βs as well.

Hypothesis 1:

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H0: There is no relationship between NFXNA & “Size, OS & ERV” H1: There is a relationship between NFXNA & “Size, OS & ERV”

F-test using Analysis of variance is used to check the overall significance of regression.

SPSS is used to analyze Regression & Partial Regression coefficients and their significance is studied from its output. Significance of individual Partial Regression coefficients will be checked using t-tests & overall significance using F-test. The vales of statistics t & F should be significant at a level of significance of less than 0.05.

Regression analysis used in understanding this relationship is backward and the final model with one independent variable obtained after eliminating all the lesser significant independent variables out of a total of three tells, if there is any, the relationship between the most significant variable & the dependent variable.

Independent Variables:

Net Foreign Currency Exposure Relative to Net Assets:

Net Foreign Currency Exposure relative to Net Assets is calculated for the comparison purpose. Different commercial banks have different Net Assets that represent the size of bank and obviously different net positions. Net foreign currency exposure is divided by Net Assets so that a comparison can be done between different banks. Therefore, it shows Net foreign currency exposure of a bank relative to its size, otherwise using only NFX in the model will show its maximum dependence on Net assets of bank and the effect of other factors will be not taken into account.

Net Foreign Currency Exposure is calculated by using the following formula:

��� = � ��������� �� + �−�������������� ������� ��

±� !���"#�$%���&'()�*+���� ��

Equation 1:Calculation of NFX

Net Foreign Currency Exposure is calculated by adding Net Open Position in all

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currencies held by a bank. All the banks have mentioned this NFX in the Notes to Financial Statement and hence that is used.

����� = ���/��

Equation 2: NFXNA Calculation

Dependent Variables:

1. Size of Bank: Size of Bank can be measured either using number of branches or the Net Assets (Net Assets = Equity). Since there are certain banks in Pakistan having lower number of branches and their Net Assets (or Equity) are higher than the Banks having higher number of branches. Therefore, Size of Bank, for the purpose of understanding the relationship under study, is measured by Net Assets. A further categorization could have been done into small, medium and large (as done by the KPMG’s Banking Survey 2009) which has not been done to check the true impact of bank size on the net foreign currency exposure.

2. Ownership Status: Ownership Status or Nature of Ownership. Usually there are three categories of banks operating in Pakistan i.e. Public sector commercial banks, local private banks & foreign banks. As foreign banks have been excluded, therefore, only two categories are left herewith. As ownership status is a category variable, therefore, a dummy variable OS is introduced in this model. Coding for the dummy variable is “0” for Public Sector Commercial Banks & “1” for Local Private Banks.

3. Exchange Rate Volatility: Exchange Rate Volatility is used as a measure to understand the fluctuations in the exchange rate. Standard deviations & percent changes are amongst the several measures used for exchange rate volatility (Mbutor, 2010). Exchange rate is measured in units of Pak Rupees Vis-à-vis US Dollars. An increase in exchange rate shows depreciation or weakening of Pak rupee whereas a decrease shows appreciation or strengthening of Pak Rupee against US dollar. Exchange rate volatility in this model is calculated by taking standard deviation of changes in daily price fluctuations. While constructing this variable, it is assumed for this particular objective that using the historical price changes can be used for the next period and not for the current period. Therefore, volatility of previous year is related to the current foreign currency exposure of current year i.e. if and only if the volatility has had any impact on the Banks’s Foreign currency exposure which is unhedged position in foreign currencies, bank will adjust its exposure in the next period. For example, if a bank has a policy to adjust its exposure by

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deciding whether to hedge or take a speculative position depends upon volatility of exchange rate; it will take Exchange rate volatility of previous year into account as historical data is used for the future forecasting.

Multicollinearity Problem

Multicollinearity can distort the results in the model; hence it has to be checked before running the multiple regression on the model. Multicollinearity is the existence of strong correlation between independent variables. In this model, independent variables i.e. Size of bank, Ownership Status & Exchange Rate Volatility may exhibit correlation with each other. A correlation matrix can be drawn and the one of the independent variables exhibiting a correlation of 0.5 or higher has to be dropped.

Relationship between Net Foreign Currency Exposure & Net Assets

Relationship between Net Foreign Currency Exposure & Net Assets can be studied using Simple Linear Regression as below:

��� = � + �����

Model 2: Relationship between NFX & Net Assets

Where

NFX = Net Foreign Currency Exposure NA = Net Assets α & β = Parameters of Model, intercept and slope respectively

In this model, NFX is dependent variable & NA is independent variable.

Hypothesis 2:

H0: There is no relationship between Net Foreign Currency Exposure and Net Assets of Commercial Banks in Pakistan H1: There is a relationship between Net Foreign Currency Exposure and Net Assets of Commercial Banks in Pakistan

F-Test using ANOVA is used to check the significance of this regression. If the value of F statistic is significant at a level of significant less than 0.05, it shows that there is a significant relationship between both of these two variables or NFX depends on Net Assets of bank.

3. Comparison of Net Foreign Currency Exposure of Public Sector &

Private Commercial Banks

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Public Sector Commercial Banks & Local private Commercial banks are two different ownership statuses of banks in Pakistan. It has to be checked whether there is any difference between these two types as far as their NFXNA is considered.

For this purpose same variable as constructed previously, Net Foreign Currency Exposure Relative to Net Assets (NFXNA), is used.

Hypothesis 3:

H0: The mean of NFXNA of Public Sector Commercial Banks is not significantly different than that of Local Private Banks. H1: The mean of NFXNA of Public Sector Commercial Banks is significantly different than that of Local Private Banks

The above written hypothesis is tested using Independent Sample t-Test. For this purpose, first equality of variances is tested using Levene’s Test. If the value of F-Test is significant at a significance level less than 0.05, it shows that variances of both groups are significantly different and if it is not significant, it shows that variances are not significantly different. Finally t-test is used to check independence of both groups and in this case if the value of t with the respective degree of freedom is significant at a significance level of less than 0.05, it shows that there is a significant difference between Net Foreign Currency Exposure relative to Net assets of both types of ownerships of banks.

4. Comparison of Net Foreign Currency Exposure of Islamic &

Conventional Commercial Banks

With a continuous growth in the Islamic Banking in Pakistan, it is important to check whether there is any difference between the Net foreign currency exposures taken by them is different from than those of conventional commercial banks.

For this purpose same variable as constructed previously, Net Foreign Currency Exposure Relative to Net Assets (NFXNA), is used.

Hypothesis 4:

H0: The mean of NFXNA of Islamic Banks is not significantly different than that of Conventional banks. H1: The mean of NFXNA of Islamic Banks is significantly different than that of Conventional banks

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The above written hypothesis is tested using Independent Sample t-Test. For this purpose, first equality of variances is tested using Levene’s Test. If the value of F-Test is significant at a significance level less than 0.05, it shows that variances of both groups are significantly different and if it is not significant, it shows that variances are not significantly different. Finally t-test is used to check independence of both groups and in this case if the value of t with the respective degree of freedom is significant at a significance level of less than 0.05, it shows that there is a significant difference between Net Foreign Currency Exposure of both types of banks.

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Different Tools & Instruments Used by Commercial Banks in

Pakistan to Manage Foreign Exchange Risk

All the commercial banks face Foreign Exchange Risk and they have to use different tools and instruments to manage this type of risk. Now this study has to explore in a broader spectrum, by taking into account all the Listed Commercial Banks, different tools and instruments used by the Commercial Banks in Pakistan for the management of foreign exchange risk.

To accomplish this objective, answers to the below mentioned questions had to be found out:

5. Whether commercial banks in Pakistan use any tool or instrument to manage foreign currency risk? If “Yes”, what are the different tools used by these commercial banks to manage foreign currency risk?

6. Whether all the commercial banks use the same tools to manage risk or there exists any difference regarding the usage of these tools? And If usage of these tools varies from bank to bank, does it depend on Ownership Nature (Private or Public Sector), Type of Bank (Islamic or Conventional) Size of Bank, Bank’s Foreign Currency Exposure and Exchange Rate Volatility?

5. Tools & Instruments Used by Commercial Banks in Pakistan

Financial Statements of commercial banks are studied to find out whether the commercial banks use any tool or instrument to measure this foreign exchange risk or not. Such instruments and tools are disclosed in the notes to the financial statements of all the banks. To address this answer a detailed and thorough study of financial statements of commercial banks is conducted and a list of the tools and instruments used by the commercial bank is prepared. A detailed report on such tools and instruments, with reference to their usage by the commercial banks in Pakistan, is given in the Findings & Analysis Section. A descriptive analysis is conducted for this purpose.

6. Factors Influencing Usage of Foreign Exchange Risk Management

Tools

Financial statements and their notes provide the answer to the question that whether all the commercial banks use the same tools or not. What are the tools that are commonly used by all the banks and what are the tools which are particularly used by used by certain banks.

Usage of these tools, particularly currency derivatives, certainly depends on different factors like size of bank, nature of ownership (Private or Public Sector), Type of Bank (Conventional or Islamic) Exchange Rate Volatility and its Foreign Currency Exposure.

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A. Currency Derivatives & Ownership Status of Banks B. Currency Derivatives & Type of Bank (Conventional or Islamic) C. Currency Derivatives & Size of Bank D. Currency Derivatives & Foreign Currency Exposure E. Currency Derivatives & Exchange Rate Volatility

These different bank specific (A, B, C & D) & macroeconomic (E) factors influence the usage of currency derivatives by the bank. These different factors can be studied using different ways.

For A & B: Usage of currency derivatives by banks, as far as ownership status & type of bank is concerned (A & B), is studied through descriptive analysis and further their relationship, currency derivatives as dependent variable, is studied using binary logistic regression.

For C, D & E: Relationship between Size of Bank, as measured by Net Assets (or Equity, since equity and net assets are equal so net assets are used), and Selection of currency derivatives is studied using binomial regression. Similarly, relationship between currency derivatives and foreign currency exposure & exchange rate volatility is also studied using binomial regression.

Currency Derivative Usage by Type of Banks

Type of banks here refers to conventional or Islamic banks. With a continuous growth of Islamic banking across the globe and in Pakistan and the Islamic banks dealing in multiple currencies at the same time, it is important to check whether the Islamic banks use currency derivatives or not. For this purpose descriptive study is done.

Currency Derivative Usage by Ownership Status

To explore the usage of currency derivatives by the commercial banks in Pakistan with respect to its ownership status, a descriptive study is done.

Relationship between currency derivatives and other factors

Relationship between currency derivatives usage and other factors like Size of Bank, Ownership status of bank, type of bank, net foreign currency exposure relative to net assets & exchange rate volatility is studied using Binary Logistic Regression.

Predictors:

Predictors in Binary Logistic Regression Model are:

I. Ownership Status of Bank: This is a categorical variable, having two categories Public Sector commercial bank and local private bank. Since, it

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has only two categories; it is entered as a single dummy variable with values 0 and 1, 0 for Public sector commercial bank and 1 for Local private bank.

II. Type of Bank: Type of bank is also entered as a dummy variable with two categories Islamic and conventional, 0 for Conventional and 1 for Islamic. This variable is entered in to the model to check whether changing the type of bank results in the change in the usage of currency derivatives.

III. Size of Bank: Size of Bank is measured by the size of equity. Since, equity is equal to Net Assets; hence, Net assets are used for this purpose. However, Net Assets as entered in model are measured in billions rather than in thousands to magnify its effect.

IV. Net Foreign Currency Exposure Relative to Net Assets: This variable, as used previously, is entered as a continuous variable in this Binary Logistic Regression. This variable is entered in the model as a percentage.

V. Exchange Rate Volatility: Current Year Exchange rate volatility is entered as a continuous variable. However, in this model, it is entered after multiplying by 100.

Dependent Variable

Dependent variable in this case is “Type of Derivatives used”. There are three types of currency derivatives that are being used by Banks in Pakistan i.e. forward exchange contracts, currency swaps & foreign currency options. As Forwards are used by all the banks and other types are not common, hence, a dichotomous variable is constructed to include in the model. This variable has only two categories, one is the banks which use forward exchange contracts only and other one is the banks who use swaps and options also other than forwards. Former is coded with 0 and later with 1.

Binary Logistic Regression Model

As the dependent variable is a discrete categorical variable with two categories, a dichotomous variable, therefore the most appropriate statistical procedure for studying it as Binary Logistic Regression. Whether commercial banks in Pakistan use only forward exchange contracts or use swaps & options along with these forwards.

The Logistic Model is as below:

Log[p/1-p]=a+b1X1+b2X2+b3X3+b4X4+b5X5

Model 3: Binary Logistic Model for Currency Derivative Usage

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Where:

Log[p/1-p] = Logit [p] =ln[p/1-p]

a = Constant

b = slope values (for independent variables 1 to 5)

X = Independent variables ( 1 to 5)

P can be calculated using the below formula, which is simply another rearrangement of the above equation:

� = ��� a+b1

X1

+b2

X2+b

3X

3+b

4X

4+b

5X

5 / 1+��� a+b1X

1+b

2X

2+b

3X

3+b

4X

4+b

5X

5

Model 4: Calculation of P using Binary Logistic Regression for Currency Derivative Usage

Where:

p = odds ratio

The Logits (Log Odds) are the b coefficients (Slope values) of the regression equation. b coefficients (slope values) can be interpreted as the change in the Log Odds due to a unit change in the independent variable. b for the Net Assets (in billions rupees) can be interpreted as the change in Log Odds due to a one billion change in net assets of a commercial bank. Similarly, the b’s for all other independent variables can be interpreted in the same way.

p here refers to as the Odds ratio, which is very important to interpret in this model here. Odds ratio estimate the change in odds of the membership in the target group (which is usage of tools other than forwards in this case) for a unit increase in the independent variable. For example, changes in odds due to a unit change in Net Assets, i.e. one billion rupees of net assets. It can be calculated as the exponential of the regression coefficient, b, of the relative independent variable.

Using this model, it has to be found out that what role does above mentioned independent variables play for the selection of tools. Selection of tools means that whether the banks use only forward exchange contracts or use swaps and options along with forward exchange contracts. Using SPSS, Binary Logistic Regression is run using “Backward Stepwise based on Likelihood Ratio Test”. All the variables are entered in the model and then using backward stepwise method based on Likelihood ratio tests, insignificant independent variables are removed and finally only the significant variables are retained in the model.

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Foreign Exchange Risk Management & its Impact on Income

Foreign Exchange Risk Management practices vary from bank to bank and so does the income of banks. An important objective of this study is to study income from dealing in foreign currencies and do different factors affect income from dealing in foreign currencies.

Study the descriptive of Income from dealing in foreign currencies in

both Islamic & Conventional Banks & Comparison Between them

Income from dealing in foreign currencies of commercial banks with respect to its type i.e. conventional or Islamic is studied using descriptive analysis. For this purpose, the variable used is Income from Dealing in foreign currencies as a percentage of total income. Further income from dealing in foreign currencies in absolute terms is also descriptively analyzed. The actual purpose of using the both variables in this descriptive analysis is to check descriptive in absolute terms as well as in relative terms to its overall income.

To compare both these groups independent sample t-test is used and the below mentioned hypothesis are formed:

H0: There is no significant difference between IFX of conventional banks and Islamic Banks

H1: There is a significant difference between IFX of conventional banks and Islamic Banks

Study the descriptive of Income from dealing in foreign currencies in

both Public Sector Commercial Banks and Local Private Banks &

Compare them

Income from dealing in foreign currencies of commercial banks with respect to its ownership status i.e. whether it is a public sector commercial bank or local private bank, is studied using descriptive analysis. Both the variables Income from dealing in foreign currencies in absolute terms and in relative terms to overall income.

To compare both these groups independent sample t-test is used and the below mentioned hypothesis are formed:

H0: There is no significant difference between IFX of public sector commercial banks and local private banks

H1: There is a significant difference between IFX of public sector commercial banks and Islamic Banks

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Income from dealing in foreign currencies and size of bank

It is very important to check whether size of bank have any effect on income from dealing in foreign currencies. Simple Linear Regression model is used to check this relationship. Two separate models are constructed to check the effect of size of bank on income from dealing in foreign currencies. First model check the effect of size on Income from dealing in foreign currencies in absolute terms (in rupees) and second one check the effect of size on the Income from dealing in foreign currencies relative to total income.

Independent Variable: Size of bank, as measured throughout this study using Net Assets.

Dependent Variable (Model 1): IFXRS, Income from dealing in foreign currencies (in ‘000 rupees)

Dependent Variable (Model 2): IFX, Income from dealing in foreign currencies as a percentage of total income of bank.

Simple linear regression models used to investigate above relationships can be given as:

���� = + �� + ɛ

Model 5: Model 1 of Relationship between Net Assets & IFXRS

-�� = � + ��� + ɛ

Model 6: Model 2 of Relationship between Net Assets & IFX

Hypothesis:

Following are the list of hypothesis constructed

H0: There is no linear relationship between Net Assets & Income from dealing in foreign currencies in rupees.

H1: There is a linear relationship between Net Assets & Income from dealing in foreign currencies in rupees.

H0: There is no linear relationship between Net Assets & Income from dealing in foreign currencies relative to total income.

H1: There is a linear relationship between Net Assets & Income from dealing in foreign currencies relative to total income

Effects of tools used on Income from Dealing in Foreign Currencies

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Another objective of this study is to check whether the use of currency derivatives have any impact on the income from dealing in foreign currencies of commercial banks. This can be investigated using Simple Linear Regression.

Independent Variable: In this case independent variable is tools used by commercial banks. This variable is entered into the simple linear regression model as a dummy variable. As forwards contracts are used by all the commercial banks of Pakistan therefore, again here two categories are formed, 0 & 1. 0 for the banks who use only forward exchange contracts and 1 for the banks who use currency swaps or foreign currency options or both along with these forwards exchange contracts.

Dependent Variable: Dependent Variable in this case is Income from dealing in foreign currencies as a percentage of total income of bank, as obtained from Income Statement of respective commercial bank. Income from foreign currencies is mentioned under non markup income head in income statement. This cannot be directly used in this model due to the differences between the banks in size and overall income, therefore, income from dealing in foreign currencies is taken as a percentage of total income of the bank. Total Income here includes total Net Markup Income and Markup income before deduction of any expenses. Net markup income means total markup income earned less markup expenses as paid on deposits. Therefore, using this variable as Income from foreign currencies as a percentage of total income, denoted by IFX, adjusts for the interbank differences of characteristics.

Model:

The simple linear regression model as used in this analysis is as below:

��� = + ������+ �

Model 7: Relationship between Tools used and IFX

Where

IFX = Income from dealing in foreign currencies as a percentage of Total income

� = Intercept of the model

β = Slope of the model, regression coefficient

Tools = Independent variable: tools used entered as a dummy variable

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� = Error term

Using the above mentioned model, regression coefficient b, an unbiased estimate of β, is calculated and its significance is checked. To check the significance, ANOVA is used and below mentioned hypothesis is constructed;

H0: There is no linear relationship between IFX & Tools

H1: There is a linear relationship between IFX & Tools

ANOVA is used to check the overall significance of this regression model. R square explains the extent of relationship as explained by the regression model.

Income from dealing in foreign currencies and Exchange Rate Volatility

Exchange Rate Volatility is a very important phenomenon in the forex market. As commercial banks deal in foreign currencies, it is very important to check whether commercial banks are affected by the exchange rate volatility. Simple Linear Regression model is used to check this relationship. Two separate models are constructed to check the effect of exchange rate volatility on income from dealing in foreign currencies. First model check the effect of Exchange Rate Volatility on Income from dealing in foreign currencies in absolute terms (in rupees) and second one check the effect of Exchange Rate Volatility on the Income from dealing in foreign currencies relative to total income.

Independent Variable: Exchange Rate Volatility, for this purpose current year exchange rate volatility is taken into account.

Dependent Variable (Model 1): IFXRS, Income from dealing in foreign currencies (in ‘000 rupees)

Dependent Variable (Model 2): IFX, Income from dealing in foreign currencies as a percentage of total income of bank.

Simple linear regression models used to investigate above relationships can be given as:

���� = + ���� + ɛ

Model 8: Model 1 of Relationship between ERV& IFXRS

��� = + ���� + ɛ

Model 9: Model 2 of Relationship between ERV & IFX

Hypothesis:

Following are the list of hypothesis constructed

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H0: There is no linear relationship between Exchange Rate Volatility & Income from dealing in foreign currencies in rupees.

H1: There is a linear relationship between Exchange Rate Volatility & Income from dealing in foreign currencies in rupees.

H0: There is no linear relationship between Exchange Rate Volatility & Income from dealing in foreign currencies relative to total income.

H1: There is a linear relationship between Exchange Rate Volatility & Income from dealing in foreign currencies relative to total income

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Findings & Analysis

Data obtained from Annual reports of commercial banks, statistical bulletins and other publications by State Bank of Pakistan is analyzed using the above methodology mentioned in the previous section. Different research methods and a variety of statistical techniques are used for this purpose.

All the findings are mentioned in this section, along with analysis.

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Findings on Net Foreign Currency Exposure of Commercial Banks in

Pakistan

Foreign Currency Exposure of commercial banks in Pakistan is studied and findings relative to different research questions are below:

1. Net Foreign Currency Exposure

1. Annual reports of Commercial banks showed that majority of commercial banks assume foreign currency exposure. All the commercial banks mentioned their Net Foreign Currency Exposure in Notes to their financial statements.

2. A new variable “Net Foreign Currency Exposure Relative to Net Assets” (NFXNA) is constructed and analyzed.

a. Almost majority of the banks have positive figure which shows that they hold overall Net Asset Position.

b. Few banks have zero as they do not take any exposure and keep their positions offset and hedged all the time.

c. A few banks even have negative figure, which shows that they hold overall NET LIABILITY POSITION.

Below is the table showing descriptive of the variable NFXNA:

Descriptive Statistics: Net Foreign Currency Exposure Relative to Net Assets

N Range Minimum Maximum Mean Std. Deviation

NFXNA 108 2.18049 -1.03309 1.14740 .7988934 .39340539

Valid N (listwise) 108 Table ii: NFXNA, Descriptive Statistics

Descriptive statistics as mentioned in the above Table ii, clearly show that Net foreign currency exposure varies from bank to bank. A standard deviation of 0.3934, as obtained from the data of 108 banks for the period 2005-2009, shows the degree of dispersion in this variable and hence it can be said that Net foreign currency exposure of banks vary and all the banks do not have the same ratio of NFXNA.

2. Findings on “Factors Affecting Foreign Currency Exposure

To study whether different factors as mentioned in previous section affect the Net Foreign Currency Exposure of Commercial banks in Pakistan or not. Multiple Linear Regression is used to check the relationship between “Net Foreign Currency Exposure Relative to Net Assets” as a dependent variable and a set of

Page | 40

independent variables i.e. size of bank, ownership status of bank & exchange rate volatility. To serve the purpose, below mentioned model (Model # 1) is formed:

����� = � + ������� + ����� + ����� � + �

A correlation matrix to check the correlation between independent variables so that it can be found out if there is any Multicollinearity in the above mentioned model or not. The correlation matrix is below:

Correlations

Ownership

Status Size

Exchange

Rate Volatility NFXNA

Ownership Status Pearson Correlation 1 -.343** .073 -.056

Sig. (2-tailed) .000 .448 .565

N 110 110 110 108

Size Pearson Correlation -.343** 1 .125 .093

Sig. (2-tailed) .000 .193 .341

N 110 110 110 108

Exchange Rate

Volatility

Pearson Correlation .073 .125 1 .142

Sig. (2-tailed) .448 .193 .142

N 110 110 110 108

NFXNA Pearson Correlation -.056 .093 .142 1

Sig. (2-tailed) .565 .341 .142

N 108 108 108 108

**. Correlation is significant at the 0.01 level (2-tailed). Table iii: Correlation between OS, Size & ERV

It is obvious from the above correlation matrix that the correlation between any of two independent variables is not greater than 0.5 and is far less than this threshold. Therefore, it can be said on the basis of the above mentioned findings that there is no problem of Multicollinearity in this model.

After checking for the Multicollinearity and finding that there is no Multicollinearity, Multiple Linear Regression is run on the data using the above mentioned dependents and a set of independent variables, using SPSS Statistics Processer. The output from SPSS is below:

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Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .167a .028 .000 .39342807

a. Predictors: (Constant), Size, Exchange Rate Volatility, Ownership Status

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression .462 3 .154 .996 .398a

Residual 16.098 104 .155

Total 16.560 107

a. Predictors: (Constant), Size, Exchange Rate Volatility, Ownership Status

b. Dependent Variable: NFXNA

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) .784 .125 6.266 .000

Ownership Status -.054 .121 -.046 -.447 .656

Exchange Rate

Volatility

.336 .238 .139 1.414 .160

Size 9.561E-10 .000 .060 .580 .563

Table iv: Multiple Linear Regression Output of Relationship Between "NFXNA & "Size, OS & ERV"

Above mentioned tables show the output from the SPSS Statistics Processor. To check whether there is any significant relationship between the dependent variable and three independent variables is significant, F-Test is used and to check whether partial regression coefficients are significant or not t-test is used. From the output tables, the table showing the coefficients, it is evident that the three different values of t-statistic, for three partial regression coefficients are not significant at the desired level of significance. Further value of R Square is very small, which shows that very small variation is explained because of linear relationship. The value of F, calculated using ANOVA, is also not significant at the desired level of

Page | 42

significance. Since, the value of F-statistic is not significant, the multiple linear regression is not overall significant.

Since, the regression is not overall significant, there is no relationship between dependent variable & independent variables, the null hypothesis 1 is substantiated. Now, based on our results, it can be said that “Net Foreign Currency Exposure Relative to Net Assets” of commercial banks in Pakistan does not depend on Size of Bank, Ownership Status of Bank & Exchange Rate Volatility.

3. Relationship Between NFX & Net Assets

The below Model 2 is formed to check the relationship between Net Foreign Currency Exposure and Net Assets of commercial banks in Pakistan.

��� � � � ����

A scatter diagram of both variables is below:

Figure 1: Scatter Diagram of NFX & Net Assets (Model 2)

Simple linear regression is used in this model and the output from SPSS Statistics processor is below:

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

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1 .960a .922 .921 7.06015E6

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 6.264E16 1 6.264E16 1256.630 .000a

Residual 5.333E15 107 4.985E13

Total 6.797E16 108

a. Predictors: (Constant), Net Assets

b. Dependent Variable: Net Foreign Currency Exposure

Table v: Output for Regression: NFX & NA

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) -2420679.816 851025.994 -2.844 .005

Net Assets .973 .027 .960 35.449 .000

a. Dependent Variable: Net Foreign Currency Exposure

As per the above mentioned output of SPSS for model 2, it is evident that the value of regression coefficient B is 0.973 with a standard error of 0.027. The calculated value of t-statistic used to check its significance shows that it is significant at the desired level of significance rather highly significant. Value of R-square is very high which shows that there is a very strong relationship between dependent and independent variables. To check the overall significance of model, calculated value of F-Statistic is also significant.

In the light of the above results, null hypothesis 2 is rejected and the alternative hypothesis 2 is accepted which states that there is a relationship between Net Foreign Currency Exposure and Net Assets of Commercial Banks in Pakistan.

As there is a direct relationship between these two variables, if the Net assets of the bank are changed, Net FX Exposure will also be changed. Model 2 differs from the model 1 in that Model 1 takes into account NFXNA and studies its relationship with Size of Bank which is measured by Net Assets of bank whereas in model 2, Net Foreign Currency Exposure is related to Net Assets of bank.

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4. Comparison of Net FX Exposure of Commercial banks in Private

Sector & Public Sector

While studying the Net Foreign Currency Exposure of Commercial Banks in Pakistan, next objective is to compare the net foreign currency exposure of Public Sector Commercial Banks (PSCB) and Local Private Banks (LPB). Results (Output, using SPSS Statistics Processors) of the independent sample t-test are below:

Group Statistics

Ownersh

ip Status N Mean Std. Deviation Std. Error Mean

NFXNA PSCB 14 .8557330 .36257606 .09690253

LPB 94 .7904280 .39891236 .04114467

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

F Sig. t df

NFXNA Equal variances assumed .874 .352 .578 106

Equal variances not

assumed .620 18.028

Independent Samples Test

t-test for Equality of Means

Sig. (2-tailed) Mean Difference

Std. Error

Difference

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NFXNA Equal variances assumed .565 .06530500 .11305264

Equal variances not assumed .543 .06530500 .10527575

Table vi: Results of independent sample t-test to compare NFX of PSCR & LPB

Levene’s Test is used to check the Equality of variances, since, the value of F is not significant, therefore, variances of both groups is not significantly different and hence equal variances are assumed.

Assuming equal variances, the value of t=0.578 with degree of freedom 106 is not significant at the desired level of significance. There is no difference between means NFXNA of Public Sector Commercial Banks & NFXNA of Local Private Banks. Hence our Null Hypothesis 3 is substantiated.

5. Comparison of Net Foreign Currency Exposure of Islamic Vs

Conventional Banks

Independent sample t-test is used to check if there is a significant difference between Islamic & Conventional banks as far as there Net Foreign Currency Exposure is concerned. Below are the results (output from SPSS Statistics Processor):

Group Statistics

Type N Mean Std. Deviation Std. Error Mean

NFXNA Conventional 92 .7677321 .40961668 .04270549

Islamic 16 .9780714 .21426364 .05356591

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

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F Sig. t df

NFXNA Equal variances assumed 12.572 .001 -2.001 106

Equal variances not

assumed -3.070 37.623

Table vii: Results of independent sample t-test to compare NFXNA for Islamic & Conventional Banks

Independent Samples Test

t-test for Equality of Means

Sig. (2-tailed) Mean Difference

Std. Error

Difference

NFXNA Equal variances assumed .048 -.21033936 .10509514

Equal variances not assumed .004 -.21033936 .06850595

Table viii: Results of independent sample t-test to compare NFXNA for Islamic & Conventional Banks

Here Levene’s test gives the value of F which is significant at p<0.01, therefore equality (Homogeneity) of variances is not assumed. Using Independent Sample t-test, value of t is -3.070 with degree of freedom of 37.623. This value of t is significant as p-value is less than 0.01, therefore, it is established that there is a significant difference between means of these two groups. Hence, null hypothesis 4 is rejected and alternative hypothesis 5 is accepted stating that there is a significant difference NFXNA of Islamic Banks and Conventional Banks.

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Findings on Usage of Different Tools for Foreign Exchange Risk

Management

Foreign Exchange Risk is managed by all the commercial banks in Pakistan and the findings related to research objectives as listed in the previous sections are given below in the same order as they appear previously:

5. Foreign Exchange Risk Management: Tools & Practices

Foreign exchange risk management practices as adopted by commercial banks in Pakistan can be broadly listed as below:

• Foreign Currency Portfolio Diversification • Foreign Currency Assets & Liabilities Matches

• Use of Currency Derivatives

Foreign Currency Portfolio Diversification

Almost all commercial banks hold a portfolio of different foreign currencies i.e. they deal in multiple currencies. Major currencies, in which they deal in and hold open positions at the same time, are US Dollar (US$), Great British Pound (GBP), Japanese Yen, Euro, UAE Dirham & Others.

Foreign Currency Assets & Liabilities Matches

Foreign currency assets and liabilities matching is a very common practice by the commercial banks in Pakistan to hedge against foreign exchange risk. However, such matches are strictly done within the limits. These limits are set internally by the banks themselves, mostly by the Asset Liability Committee, as advised by the State Bank of Pakistan. These limits control foreign currency exposure through dealer limits, open foreign currency position limits & counterparty exposure limits. Foreign currency assets and liabilities are also managed within the strict limits as prescribed by the State bank of Pakistan.

Use of Currency Derivatives

The most important practice of managing foreign exchange risk involves currency derivatives. Currency Derivatives are tools employed by every commercial bank, however, selection of derivatives from the available ones vary from bank to bank.

In Pakistan, a descriptive analysis of such tools (currency derivatives), as used by the commercial banks included in our sample, is given below:

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Category Frequency Percent Cumulative Percent

Only Forward Exchange Contracts 63 57.3 57.3

Forward Exchange Contracts & Swaps 33 30.0 87.3

Forward Exchange Contracts, Swaps & Options 14 12.7 100.0

Total 110 100.0 Table ix: Descriptives: Currency Derivatives Usage

In the above table, 110 commercial banks included in sample, use different mix of currency derivatives.

• Majority of banks use forward exchange contracts only.

• Some use a mix of forward exchange contracts and currency swaps.

• Few banks use a mix of forward exchange contracts, currency swaps & foreign exchange options.

• None of the bank uses Futures for the Foreign exchange risk management

The main reason behind the no usage of Futures is lack of futures exchange in Pakistan. Same is the reason behind the lesser usage of foreign currency options. FX options are both exchange traded and over the counter. As there is no exchange for trading of FX options therefore the FX options being traded here by the commercial banks are usually over-the-counter. Forward exchange contracts & currency swaps are also over the counter. Forward exchange contracts is the most common and most important tool used by the commercial banks in Pakistan as every bank use it whereas the currency swaps are the second popular tool used. Swaps are usually long term foreign exchange risk management tool. Few banks also use options, only 12.7% of our sample i.e. listed commercial banks of Pakistan. As there is no specific exchange for the exchange traded options in Pakistan, therefore, the option used by few banks are over the counter ones.

6. Currency Derivatives Usage

Currency derivatives are crucial for the management of foreign exchange risk. In Pakistan, all the commercial banks use currency derivatives. Even if their policy is not to use Derivatives, they still use forward exchange contracts.

Results show that all the banks whether public sector commercial banks or local private banks use forward exchange contracts. 110 banks out of sample of 110 banks use forward exchange contracts. However, 33 out of 110 banks use swaps in addition to forwards and 14 out of a total of 110 banks use foreign currency options along with forwards and swaps as well. This is evident from the results

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mentioned in the above table that public sector commercial banks do not use foreign currency options at all.

Currency Derivatives Usage by Ownership Status

Currency derivatives are used by both Public Sector Commercial Banks and Local Private Banks. To what extent these banks use currency derivatives, below are the results:

Ownership Frequency Percent Cumulative Percent

Public Sector Forwards 9 64.3 64.3

Forwards & Swaps 5 35.7 100.0

Total 14 100.0

Local Private Forwards 54 56.3 56.3

Forwards & Swaps 28 29.2 85.4

Forwards, Swaps & Options 14 14.6 100.0

Total 96 100.0

Table x: Currency Derivatives Usage by Ownership Status

Descriptive study, to check whether Islamic banks or conventional banks use currency derivatives or not, is conducted. Findings in this context are mentioned in a table below:

Currency Derivative Usage by Type of Bank

Type Frequency Percent

Cumulative

Percent

Conventional Forwards 52 55.3 55.3

Forwards & Swaps 28 29.8 85.1

Forwards, Swaps &

Options

14 14.9 100.0

Total 94 100.0

Islamic Valid Forwards 11 68.8 68.8

Forwards, Swaps 5 31.3 100.0

Total 16 100.0 Table xi: Currency Derivative Usage by Type of Bank

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Sample contains 110 banks in total, including 16 Islamic and 94 conventional banks. All the banks, both Islamic and conventional banks, use forward exchange contracts. Only 5 out of 16 Islamic banks use swaps along with forwards and none of Islamic bank use foreign exchange options. However commercial banks use all the three types of currency derivatives i.e. forwards, swaps and options. It is evident from the above findings that only 14 out of 94 conventional banks use foreign currency options. Therefore, the most popular tool is forward exchange contracts and the least popular and used tool is foreign currency options.

Findings on Factors that affect Currency Derivative Usage

Using Binary Logistic Regression it is tried to establish what are the different variables that affect the selection of tools for a commercial bank. A set of five independent variables (Size of bank, Ownership Status of Bank, Type of Bank, Net Foreign Currency Exposure and Exchange rate Volatility) are included in model to check their relationship with tools selection as a dependent variable. The objective is to find out the most significant variables which influence dependent variable. For this purpose, Stepwise Backward method based on the Likelihood ratio test is used with the help of SPSS.

Backward Stepwise Binary Logistic Regression took place in the following steps, in order to reach the significant independent variables:

1. In the beginning the model is fitted with only constant and no independent variable. After this model is again fitted using all of the five independent variables. Therefore, step 1 involves the model with all the independent variables in it.

2. In step 2, model is refitted removing independent variable “Type of Bank” which proved to be insignificant on the basis of Likelihood Ratio test.

3. In step 3, another independent variable “Current year exchange rate volatility” is removed from the model and the number of independent variables in the model is reduced to three. These independent variables include Ownership Status of Bank, Size of Bank (as measured by Net Assets) and Net foreign currency exposure relative to net assets.

Both Type & Current ERV are removed from model based on the Likelihood ratio test. The summary of results of likelihood ratio test is given below:

Model if Term Removed

Variable

Model Log

Likelihood

Change in -2 Log

Likelihood df

Sig. of the

Change

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Step 1 OS -56.634 7.040 1 .008

TYPE -53.120 .012 1 .911

Size (NA) -73.188 40.147 1 .000

NFXNA -54.109 1.990 1 .158

Current ERV -53.430 .632 1 .427

Step 2 OS -56.706 7.170 1 .007

Size (NA) -73.618 40.996 1 .000

NFXNA -54.139 2.038 1 .153

Current ERV -53.440 .639 1 .424

Step 3 OS -56.801 6.724 1 .010

Size (NA) -73.650 40.420 1 .000

NFXNA -54.964 3.049 1 .081

Table xii: Factors that affect Currency Derivative Usage: Result of Binary Logistic regression

Below is the summary of Logits and Odd Ratios which show that how the binary logistic regression model is changed from five independent variables to three independent variables, Step 1 & 2 do not need to be discussed in detail, however, step 3 needs to be explained in detail as it corresponds to our objective of this study.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a OS(1) -3.257 1.679 3.763 1 .052 .039

TYPE(1) -.072 .642 .012 1 .911 .931

Size (NA) .086 .021 16.757 1 .000 1.090

NFXNA -.008 .006 1.962 1 .161 .992

Current ERV -.012 .015 .627 1 .428 .989

Constant -.484 .798 .368 1 .544 .616

Step 2a OS(1) -3.262 1.676 3.787 1 .052 .038

Size (NA) .086 .021 16.818 1 .000 1.090

NFXNA -.008 .006 2.013 1 .156 .992

Current ERV -.012 .015 .634 1 .426 .988

Constant -.552 .525 1.104 1 .293 .576

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Step 3a OS(1) -3.053 1.580 3.736 1 .053 .047

Size (NA) .084 .020 16.779 1 .000 1.087

NFXNA -.010 .006 2.997 1 .083 .990

Constant -.647 .512 1.595 1 .207 .523

a. Variable(s) entered on step 1: OS, TYPE, NA, NFXNA, Current ERV. Table xiii: Logits & Odds ratio Results of Binary Logistic Regression

The second column, with heading B, shows the values of regression coefficient

during each step. Logits (Log Odds) are the regression coefficients of the model

which show the impact of a unit increase in the independent variable on the

dependent variable i.e. how does a unit increase in independent variables affects

the decision of the commercial bank to select the tools for foreign exchange risk

management. In the last column, Exp (B) i.e. Odds Ratios are mentioned which

shows that how do a unit increase in independent variables changes the odds of

usage of swaps options along with forwards by a commercial bank.

It is important to interpret the odds ratio in step 3 here. If the ownership status of

bank is changed from public sector commercial bank to local private bank, the

odds of using swaps & options are 0.047 times greater than public sector commercial bank. This shows a weak influence of ownership status on decision of usage of swaps and options. If the Net Assets of bank are increased by one billion, the odds of usage of swaps and options are 1.087 times greater. If NFXNA, when expressed as percentage, is increased by one percent the odds are 0.99 times greater for the usage of swaps and options.

Cox & Snell R square for the model as in step 3 is 0.321 and Nagelkerke R square is 0.430. Based on these results, it can be stated that using our model 43% of the changes in decision to use swaps and option are because of the independent variables in our model.

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Findings on Income from Dealing in Foreign Currencies and its

Relationship with other Factors

Income from dealing in foreign currencies is mentioned separately in the income statement of commercial banks under the head of “Income from dealing in foreign currencies”. Findings related to this income as per the research objectives of this study are given below:

Findings on Income from dealing in foreign currencies and Type of

commercial banks (Conventional & Islamic)

Below are the findings regarding the variables income from dealing in foreign currencies in absolute terms and income from dealing in foreign currencies relative to its total income.

Descriptive Statistics for Convenntional Banks

N Minimum Maximum Mean Std. Deviation

IFX % of TI 94 -32.01639 33.53655 5.0842763 6.69593911

Income from FX 94 -79327.00 3969057.00 513044.2766 6.98267E5

Descriptive Statistics for Islamic Banks

N Minimum Maximum Mean Std. Deviation

IFX % of TI 16 .58781 21.15430 6.9507450 4.87415155

Income from FX 16 740.00 1019732.00 337289.6875 3.15040E5

Table xiv: Descriptives for IFX and IFXRS by Type of Bank

Comparison of IFX of Conventional & Islamic Banks

Independent sample t-test is used to test the hypothesis that there is no significant difference between means of conventional and Islamic banks.

The value of F using Levene’s test is insignificant and hence it is assumed that variances are not different. The value of t is -1.066 which is not significant at desired level of significance (as it is significant at 0.289) hence Null hypothesis is accepted that there is no significant difference between the means of IFX of commercial banks and Islamic banks.

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Findings on Income from dealing in foreign currencies and Ownership

Status of commercial bank (PSCB and LPB)

Results of the descriptive statistics for commercial banks with respect to ownership status are below:

Descriptive Statistics for Public Sector Commercial Banks

N Minimum Maximum Mean Std. Deviation

IFX % of TI 14 .24369 7.42242 3.2638994 1.96094421

Income from FX 14 3371.00 3969057.00 835056.3571 1.23401E6

Descriptive Statistics Local Private Banks

N Minimum Maximum Mean Std. Deviation

IFX % of TI 96 -32.01639 33.53655 5.6608261 6.84683360

Income from FX 96 -79327.00 2229809.00 436791.7500 5.18312E5

Table xv: Desxriptive Statistics for IFX and IFXRS by Ownership Status

Comparison of IFX between Public Sector Commercial Bank & Local

Private Banks

To compare the mean IFX of public sector commercial banks & local private banks, independent sample t-test is used.

The Levene’s test is significant and equality of variances is assumed. The calculated value of t is -1.297 which is significant at 0.1097 level of significance. Since level of significance is much higher than the desired one, null hypothesis is accepted which shows that both groups have not significantly different means.

Table xvi: Descriptive Statistics for IFX and IFXRS by Ownership

Table xvii: Descriptive Statistics of IFX and IFXRS by Ownership Status of Bank

Findings on Income from dealing in foreign currencies and Size of Bank

as measured by Net Assets

Using the simple linear regression, following outputs are produced:

Model 1:

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Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .764a .584 .581 4.26448E5

a. Predictors: (Constant), Net Assets in Billions

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.762E13 1 2.762E13 151.849 .000a

Residual 1.964E13 108 1.819E11

Total 4.726E13 109

a. Predictors: (Constant), Net Assets in Billions

b. Dependent Variable: Income from dealing in foreign currencies in ‘000 Rs.

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 106691.287 51070.033 2.089 .039

Net Assets in Billions 20390.617 1654.717 .764 12.323 .000

a. Dependent Variable: Income from dealing in foreign currencies in ‘000 Rs. Table xviii: Output of Regression: IFXRS on NA

The first model to study the impact of Size of bank, as measured by Net Assets,

on the Income from dealing in foreign currencies show that there is a significant

relationship. The model R square shows that 58.1% of variation is explained by

the relationship. The value of b i.e. 20390.617 is also significant. Since there is a

relationship between independent and dependent variable, therefore, null

hypothesis is rejected and alternative hypothesis is rejected.

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Model 2:

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .067a .004 -.005 6.49309152

a. Predictors: (Constant), Net Assets in Billions

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 20.397 1 20.397 .484 .488a

Residual 4553.306 108 42.160

Total 4573.703 109

a. Predictors: (Constant), Net Assets in Billions

b. Dependent Variable: Income from dealing in foreign currencies as a percentage of total income

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 5.683 .778 7.308 .000

Net Assets in Billions -.018 .025 -.067 -.696 .488

a. Dependent Variable: Income from dealing in foreign currencies as a percentage of total income Table xix: Output of Regression: IFX on NA

Results for the model constructed to investigate the relationship between net assets (size of bank) and income from dealing in foreign currencies as a percentage of total income indicates that there is no significant relationship between these two variables. Null hypothesis is accepted in this case. The difference between results shown by these two models can be interpreted as increasing the size of bank increases the income from dealing in foreign currencies in bank. However, if this income from dealing in foreign currencies is taken as a percentage of total income of bank, net assets does not affect this income. It can be said on the basis of findings that Size of bank does not help in earning extra income from dealing in foreign currencies by simply increasing the size of bank.

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Findings on Income from dealing in foreign currencies and Currency

Derivatives used by commercial banks

The simple linear regression, as described in the methodology section is used with the help of SPSS and below are the relevant outputs as produced by SPSS.

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .078a .006 -.003 6.48770060

a. Predictors: (Constant), Tools

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 4.920 .817 6.020 .000

Tools 1.019 1.250 .078 .815 .417

a. Dependent Variable: IFX % of TI

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 27.955 1 27.955 .664 .417a

Residual 4545.748 108 42.090

Total 4573.703 109

a. Predictors: (Constant), Tools

b. Dependent Variable: IFX Table xx: Regression output IFX on Tools

The above mentioned results show that b is not significant. Also the overall regression is not significant at the 0.10 level of significance. There is a very weak, almost no relationship between these two variables, as evident from the value of R square. Since there is no relationship, therefore, null hypothesis substantiates and it can be said that in Pakistan, income from dealing in foreign currencies is not affected by using different tools.

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Findings on Effect of Exchange Rate Volatility on Income from Dealing

in Foreign Currencies

To examine whether Exchange rate volatility have any effect on the income from dealing in foreign currencies, two separate models are constructed.

Result of Model with Income from dealing in foreign currencies in rupees

in ‘000

First model contains income from dealing in foreign currencies in Rs.‘000 as dependent variable. Output produced by SPSS is presented below:

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .277a .077 .068 6.35607E5

a. Predictors: (Constant), Current Year Exchange Rate Volatility

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 3.624E12 1 3.624E12 8.971 .003a

Residual 4.363E13 108 4.040E11

Total 4.726E13 109

a. Predictors: (Constant), Current Year Exchange Rate Volatility

b. Dependent Variable: Income from dealing in foreign currencies in Rs. ‘000

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 281336.798 91704.883 3.068 .003

Current Year

ERV

1036856.895 346183.103 .277 2.995 .003

a. Dependent Variable: Income from dealing in foreign currencies in Rs. ‘000

Table xxi: Regression Output IFXRS on ERV

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Using Income from dealing in foreign currencies in Rs. ‘000 in the simple linear

regression model showed a weak but significant relationship between the

independent variable and the dependent variable. R square in this model shows

that less than 7% variation is explained due to linear relationship between the

variables. Therefore, null hypothesis is rejected and alternate hypothesis is

accepted.

Result of Model with Income from dealing in foreign currencies relative to

total income

Results of the simple linear regression to check the impact of the Exchange rate volatility on the income of the bank using income from dealing in foreign currencies as a percentage of total income are below:

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .006a .000 -.009 6.50749413

a. Predictors: (Constant), Current Year Exchange Rate Volatility

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression .175 1 .175 .004 .949a

Residual 4573.528 108 42.347

Total 4573.703 109

a. Predictors: (Constant), Current Year Exchange Rate Volatility

b. Dependent Variable: IFX % of TI

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 5.310 .939 5.656 .000

Current ERV .228 3.544 .006 .064 .949

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Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 5.310 .939 5.656 .000

Current ERV .228 3.544 .006 .064 .949

a. Dependent Variable: IFX % of TI Table xxii: Regression Output of IFX on ERV

Results of the simple linear regression show that there is no significant relationship between the independent variable and the dependent variable used in this model. Therefore, Null hypothesis is substantiated and it can be said that exchange rate volatility has no impact on the income of the income of the commercial banks in Pakistan.

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Conclusion Based on the findings of this study, following conclusions can be drawn regarding the Foreign currency exposure, ways to manage foreign exchange risk, currency derivatives usage & income from dealing in foreign currencies of the commercial banks in Pakistan:

Foreign Currency Exposure of Commercial Banks in Pakistan

Majority of the bank have significant net position in foreign currencies and this position varies from bank to bank. If net foreign currency exposure of commercial banks is taken as a percentage of Net Assets, different factors which are Ownership Status, Exchange Rate Volatility and Size of Bank do not have any effect on it. Some bank have zero exposure, majority have net foreign currency exposure equivalent to or around Net Assets. Net foreign currency exposure is positively related to Net Assets which means that majority of banks have Net positions moved with the movement in their Net Assets. There is no difference between the Net positions as taken by Public Sector Commercial Banks and Local Private Banks whereas there is a significant difference between the net positions of conventional and Islamic banks.

Foreign Exchange Risk Management by Commercial Banks in Pakistan

Commercial banks in Pakistan use different tools to manage foreign exchange risk which include foreign currency portfolio diversification, foreign currency assets and liabilities matches and Use of currency derivatives.

Usage of Currency Derivatives by the Commercial Banks in Pakistan

In Pakistan, commercial banks use three types of currency derivatives i.e. forward exchange contracts, currency swaps & foreign currency options. All of these contracts are over the counter. Forward exchange contracts are used by all the banks whereas currency swaps are second popular tools used by commercial banks and foreign exchange options are used by only few banks. The usage of currency derivatives depends on the ownership status of bank, size of banks and net foreign currency exposure relative to net assets whereas it does not depend on exchange rate volatility and type of bank.

Income from Dealing in Foreign Currencies

Income from dealing in foreign currencies is not different if the type of bank or ownership of bank is considered. Similarly size of bank does not play any role in increasing income from dealing in foreign currencies of banks. Type of currency derivative used & exchange rate volatility during the year also do not have any effect on income from dealing in foreign currencies.

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