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Derivative use by banks in India.


Derivative use by banks operating in India is hypothesized to improve their intermediary function. The research outcome identifies the influence of derivative use on the growth of advances by banks. Bank participation in advances increases with increase in hedging activities through futures. It has also been found that the Indian private sector banks have a high exposure of risk and have externalized their risk management process. Foreign banks operating in India have a low risk exposure level, but still they have moderately externalized their risk management practices. Indian public sector banks have a large deposit bas and high risk exposure but are still internalizing their risk management through ALM. The policy implication of the study is that derivative usage by banks is likely to increase the intermediary role of banks, i.e., the increase in advances growth rate rather than investment portfolio growth rate. Banks with large deposit base could gain relatively by externalizing their risk management practices since the study reveals that interest rate risk exposure of derivative users is statistically lesser than non-users / partial users.


Banking sector faces numerous risks and the transit towards risk management practices has become imperative in the present scenario. Present day measured risk could be a potential loss to the bank. Risk measurement of revenue and cost potential of a bank is comparatively apparent while the interest rate risk is not as visible as these tangible revenues and costs. Modeling the interest rate risk management practices for banks has potential incentives to the sector as a whole in the form of improved profits, capital and integration with economic expectations.

Indian Banks have long used risk management activities such as duration and gap analysis. Risk management through derivative securities has been another avenue for banks to refine risk management practices. Similar to other international markets, price and interest rate volatility in Indian financial markets is high; hence the implications of not hedging the bank portfolio may prove to be disastrous.

Derivatives give banks an opportunity to manage their risk exposure and to generate revenue beyond that available from traditional bank operations. The research objectives framed to reiterate the importance of risk management practices through derivatives are to examine the derivative exposures in banks and to determine the influence of derivative exposure on bank's intermediation role.


Interest rate volatility and the globalization of capital markets have induced the usage of derivative futures by banks. The competitiveness in the market and the need to identify risk and hedge accordingly requires more coordination in the management of assets and liabilities of these banks. Research on introduction of derivatives, especially financial futures, into the balance sheet of banks and as an off-balance sheet hedging tool can be discussed through the works of Ederington (1979), Franckle (1980), Schweser, Cole & D'Antonio (1980), Arak & McCurdy (1980) and Morgan & Smith (1986). They have addressed the use of financial futures and have suggested hedging through interest rate derivatives as an ideal risk management solution. However, focus is more on hedging a cash position in a treasury bill or to hedge an anticipated issue of a Certificates of Deposit (CD). Further studies added multidimensional aspects to financial intermediary's hedging practices under conditions of uncertainty. Morgan, Shome & Smith (1988) considered uncertainties around deposit supply and loan demand as well as random rates of return on loans and CD's. They had also concentrated on the effect of deregulation on interest rate risk borne by financial institutions.

Risk management decisions of banks have been analyzed in detail and specifically with respect to hedging of bank risks. Allen & Santomero (2001), Kashyap (2002), Bauer & Ryser (2004) have identified risk management strategies for banks. Supportive works are Benninga & Oosterhof (2004), Cebenoyan & Strahan (2004), Szego (2002), Danielsson (2002), Nawalkha (2003), Angbazo (1997). Surveys by Bank for International Settlement (2002), Basle Committee and IOSCO (1996), are a few international market surveys on derivative practices in banks. Patnaik & Shah (2003) have measured interest rate risk of a sample of major banks in India using equity capital and market price.

A model of the intermediary role of banks and an explanation as to why derivatives use and lending are complementary activities are validated by Diamond (1984). Koppenhaver (1985) and Benninga (1985) use the optimization model for international hedging in commodity and currency forward markets. Kim & Koppenhaver (1992) used bank assets, net-interest margin, derivative dealing, capital-asset ratio and the concentration ratio to test the influence of derivative trading. Brewer, Minton & Moser (2000) examined the relationship between bank participation in derivatives and bank lending and found that the banks using interest-rate derivatives experienced greater growth in their commercial and industrial loan portfolios than banks that did not use these financial instruments.

Sharpe & Acharya (1992) and Bernanke & Lown (1992) have related loan growth with capital to asset ratio and quality of loan. A bank with too little capital relative to required amount would not be able to improve its capital position by improving the assets. Similarly the loan quality if good would induce the bank to increase its loan portfolio the next year. Bernanke & Lown (1991), Diamond (1984) and Brewer (2000) proposed that derivatives use could have an influence on the loan portfolio growth.


Asset liability management (ALM) process of bank management requires reduction of interest rate risk exposure of banks by increasing the volume of loans and decreasing the volume of deposits. Since loan demand and deposit supply is dependent on interest rates offered, banks can achieve this policy by changing loan and deposit rates to attract loans and dissuade deposits. This process internalizes the market incompleteness of a missing risk sharing market namely derivatives. But, if a derivative market is there for the banks to hedge the bank's exposure, then banks have an opportunity to enter into an unbiased derivative market to externalize their risk exposure. When banks enter a derivative market with an expected contract amount for the futures price quotation in the market, this to a certain extent may not require the bank to alter the interest rates beyond a desired level. Hence, through derivatives, the ALM process is expected to be more efficient. Banks stand to gain operationally as well as from the derivative exposure.

Risk exposure, Bank size and certain financial parameters are expected to differentiate the derivative users from non-users. Variables that are expected to influence the intermediation role of banks (Growth in advances(AG)) such as Asset Size (LA), Intermediation cost (IC), Credit risk (LQ), Capital adequacy (CA), Investment deposit ratio (ID), Percentage of assets other than advances (OA), Earnings risk (ER), Interest Margin risk (IMR) and derivative growth (DG) are considered for the empirical model.

[AG.sub.t] = a + [b.sub.1] * [LA.sub.t-1] + [b.sub.2] * [IC.sub.t-1] + [b.sub.3] * [LQ.sub.t-1] + [b.sub.4] * [CA.sub.t-1] + [b.sub.5] * [sub.Idt] + [b.sub.6] * [OA.sub.t] + [b.sub.7] * [ER.sub.t-1] + [b.sub.8] * [IMR.sub.t-1] + [b.sub.9] * [DG.sub.t] + [e.sub.t] Formula (1)


During 2005-06, 92 banks were operating in India. Nine of these did not record any operational activities since they had commenced their operations very recently, hence were deleted for research purposes. The remaining 83 banks operating in India constituted the sample for the study. Financial reports published by banks as made available by the Reserve Bank of India constitute the data for the study. Interest rate sensitivity depends to a large extent on the deposit size of the bank. Besides the public sector banks, both foreign and private Indian banks were also included in the sample. The sample had adequate representation on the basis of size and sector (Table 1).

Derivative users were identified as those banks that reported financial futures exposure consistently in their books as an off-balance sheet item during the past five years. Banks that did not show any exposure in all the five years were considered as non-users. Additionally when banks had financial futures exposure in only one of the prior five years and no exposure in the current year were considered as partial users and were grouped with non-users. Derivative users were dominant in the sample (87.8%).


Derivative users showed significant difference from non-users / partial users only on the parameter of interest margin risk, which was lower for the derivative users (Table 2). On all other parameters the derivative users and non-users / partial users did not show any statistical significance. Derivative usage has curtailed the interest rate risk exposure of banks operating in India.

The application of the proposed model to banks using derivatives had a statistical good fit. The adjusted R square of the model had an explanatory power of the combined variables as 47.3%. Tolerance test and VIF do not indicate any multicollinearity among the variables. Adequacy of solvency, credit risk, derivative growth and asset size are significant at 1% level. Derivative growth as an explanatory variable for advances growth has an explanatory power of 8% while adequacy of solvency is the prominent influencer with 19.1% explanatory power. Credit risk has a significant explanatory power of 9.7% (Table 3).

Standardized beta coefficients are useful when the independent variables representing the model are of different units as in this case. Assuming all other variables of the model are held constant, the beta for derivative growth indicates that for every one-unit change in advances growth, banks enter into a derivative position to the extent of 0.4931 units.

Banks with low capital to asset ratio adjust their lending to meet some predetermined target capital to asset ratio, hence a positive relationship could be expected between capital to asset and advances growth. This has been affirmed in the case of banks operating in India. Brewer (2000) reported a positive coefficient between capital to total asset and loan growth. Loan quality if good enables a bank to increase its loan portfolio for the next year. The larger the non-performing assets (NPA), the lower the loan quality and hence the expectation is a negative relationship between loan quality to the advances growth next year. However, the positive credit risk coefficient in the model indicates that banks have a higher credit risk exposure and have not improved the loan assessment and recovery process.

Lagged total asset is expected to influence advances growth positively. This iterates the experience of bank in lending activities. The beta coefficient in the current model is significant and positive. Investment deposit ratio has a negative beta coefficient as is theoretically expected. Traditionally banks have viewed loans and investment securities as substitutable assets. Consequently, when loan growth strengthens, hypothetical assumption is that banks hold less investment securities. Conversely, larger investment leading to increase in assets results in a negative loan growth.

Interest margin risk and earnings risk have the expected negative sign in the equation but are not significant variables. The specific influence of variables could vary due to size differences among banks and also in terms of types of banks operating in India due their unique operational policies and governance.


The model was applied separately for banks with larger deposit size, medium deposit size and smaller deposit size. All three models established statically good fit, but as was expected significant variables were different for each model. Credit risk is the only variable common in all three models (Table 4). Derivate growth was a significant influencer only for the small deposit size banks. The coefficients in all the models had the similar sign as in the overall model.


Three models were built to examine specific influencers of advances growth in terms of bank type. All these models showed statistically good fit. Adequacy of solvency was the only variable that influenced advances growth in all the three models. Public sector banks in addition emphasized intermediation cost for determining the growth of advances. Foreign banks in addition considered interest margin risk and percentage of other assets. Foreign banks' exposure in terms of advances and investments as bank assets is low compared to other assets. This could be the reason for the negative statistical significance of other assets in the model for foreign banks. Indian private sector model results are similar to the overall model (Table 5). Operationally, Indian private sector banks seem to dominate the banking sector advances growth. This could be due to the competitive environment and introduction of best practices in bank operations.


Sub-models built on the basis of deposit size and bank type were integrated together to identify the risk exposure and risk management practices through correspondence analysis. Private sector banks are small deposit size banks and have high-risk exposure and have externalized their risk management practices (Figure 1). Indian public sector banks though have a high-risk exposure with a large deposit base have not externalized their risk management practices and are more traditional in this respect. Foreign banks have low risk exposure and their risk exposure to a certain extent have been externalized.



The research highlights the use of derivatives in a bank portfolio as an influencer of advances growth. There is a positive relationship between derivative growth and advances growth. Derivative growth significantly influences the advances growth of small deposit size banks and Indian private banks. However in all the models derivate growth had a positive sign and this could be inferred as derivatives being used by even public sector and foreign banks as a tool to foster lending activities. Hence, restrictive policy regulations with respect to bank's derivative activities may lead to lower loan growth rate.

Indian banks report their overall commitment of futures position in their financial reports. However, the distinct usage of different types of derivative products needs to be known to understand the attributes of bank's hedging requirements. A policy on mere usage of derivative products may not be as convincing as a policy that is based on knowledge of usage as well as implications of the derivative product usage on the operations of banks. This research tests only the implications of the derivative use. This could be further corroborated through the type of derivatives used to hedge bank portfolio risk. Further the effect of derivatives use on earnings of banks can be established. This would help in determining whether derivative use has resulted in value addition for banks.


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Madhumathi Rajendran, Indian Institute of Technology, Madras
Table 1: Sample Characteristics

Deposit Size Classification Sector Classification

 Bank Size Total Sector Total Derivative
 Sample Sample Users

Large Banks 33 (39%) Public Sector 27 (33%) 25
Medium Banks 27 (33%) Private Sector 29 (34%) 25
Small Banks 23 (28%) Foreign Banks 27 (33%) 22
Total 83 (100%) Total 83 (100%) 72 (87.8%)

Table 2: Parameters Differentiating Derivative Users

 Derivative Interest Solvency Credit Earnings
 Usage Margin Risk Risk Risk Risk

Users 0.46 21.26 4.15 1.42
 Partial Users 0.73 32.33 2.97 1.13
F-Value 5.228 * 1.946 0.126 0.340

 Derivative Business Profit Per Bank
 Usage Per Employee Employee Asset Size

Users 563.94 4.82 3013755.5
 Partial Users 486.40 2.83 908288.36
F-Value 0.278 0.069 1.295

Note: *--Statistical significance at 5% level.

Table 3: Empirical Model Results--Full Sample

 Variable Beta t-value R square

Constant -2.23 **
Investment deposit ratio -0.2141 -2.13 ** 0.042
Intermediation cost 0.1145 1.29
Adequacy of solvency 0.5830 4.47 * 0.191
Credit risk 0.4284 4.09 * 0.097
Derivative growth 0.4931 4.80 * 0.080
Other asset growth -0.0420 -0.37
Bank asset size 0.3532 2.66 * 0.063
Interest margin risk -0.0203 -0.19
Earnings risk -0.0726 -0.73

 Variable Tolerance Variance

Investment deposit ratio 0.7130 1.4026
Intermediation cost 0.9126 1.0958
Adequacy of solvency 0.4245 2.3554
Credit risk 0.6573 1.5214
Derivative growth 0.6832 1.4638
Other asset growth 0.5607 1.7835
Bank asset size 0.4101 2.4382
Interest margin risk 0.6286 1.5908
Earnings risk 0.7358 1.3591

Note: Dependent variable: Advances growth;
*--Statistical significance at 1% level;
**--Statistical significance at 5% level.

Table 4: Model Results--Banks Classified on the basis of Deposit Size

Large size banks Medium size banks Small size banks

Credit risk Credit risk Credit risk
 Earnings risk Adequacy of solvency
 Intermediation cost Derivative growth
 Adequacy of solvency

Table 5: Model Results--Banks Classified on the basis of Type

Public Sector Banks Foreign Banks Private Sector Banks

Intermediation cost Intermediation cost Adequacy of solvency
Adequacy of solvency Interest margin risk Derivative growth
 Percentage of other Credit risk
 Adequacy of solvency Investment deposit
 Bank size
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Author:Rajendran, Madhumathi
Publication:Academy of Banking Studies Journal
Geographic Code:9INDI
Date:Jan 1, 2007
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