Bank Risk Factors and Changing Risk Exposures in the Pre- and Post-financial Crisis Periods: An Empirical Study for India

Abstract

In this study, we measure and analyse the time-varying nature of risk exposures for the Indian banking industry using weekly bank-level data from 23 October 2004 to 1 August 2014. We extend the literature by studying credit, equity, interest rate and exchange rate risks following a more comprehensive framework. The study finds evidence that the risk exposures are time varying in nature and differ across banks with different characteristics. Equity risk and credit risk increase post the global financial crisis (GFC) while interest rate and exchange rate risk gets reduced. The capital market has a favourable view of small-sized, well-capitalized, well-diversified private sector banks. Furthermore, the results also show that asset size and ownership structure offer relevant information for differentiating banks regarding their riskiness. Large banks have more equity risk exposure; public sector banks have higher credit risks while private sector banks have greater interest rates and exchange rate risk exposure. The study offers valuable insights for the regulators, supervisors, policymakers, banking industry, bank managers, investors and academia. The main contribution is a better understanding of sources of banks’ risks and needs to enhance the supervisory process in the Basel framework.

Keywords

Commercial banks market risk interest rate risk exchange rate risk credit risk

Introduction

Banks directly or indirectly affect economic development due to their various activities (Angadi, 2003; Goldsmith, 1969; Schumpeter, 1961). There is growing evidence which suggests that countries with developed banking systems and markets achieve faster economic growth (Levine, 1997, 2005; Sehgal, Ahmad, & Deisting, 2012). Carey (2001) shows that risk management is more critical in the financial sector than in other parts of the economy.

The global financial crisis (GFC) of 2008–2009 exposed various issues emanating from risk practices followed or overlooked by the banking industry. The crisis illustrated that the cost of delaying or avoiding proper risk management could be extreme—the failure of a bank and the possible collapse of the banking system, with its repercussions, felt global. The recent GFC has raised questions regarding the effectiveness of the existing bank regulations. Continuous transformation of the international financial system, changes in the sources and transmission channels of systemic shocks, increasingly competitive and interconnected financial markets, rising complexity of products, heightened regulatory scrutiny and rapid technological changes have intensified the need for ‘best-practice’ risk management in the financial service industry. Benefits of risk management can be understood regarding losses it helps to prevent. Efficient risk management practices in the financial sector improve the strength and efficiency of the economy and help banks to achieve the broader objective of maximizing the risk-adjusted rate of return on capital.

Understanding various dimensions of risk is an inherent part of banking and an area of the highest importance to bankers, regulators and policymakers. Basel Committee on Bank Supervision formed in 1974 recognized risk measurement and management as a key element of the financial sector. The Basel I Accord of 1998 linked the capital requirements of banks to asset risk categories and off-balance sheet obligations. Basel II norms, published in 2004, comprised three pillars. The First Pillar deals with the issue of minimum capital requirements, including market, credit and operational risk. The Second Pillar discusses the supervisory review process. The Third Pillar focuses on the use of disclosure to enhance market discipline. Before complete enforcement of Basel II across countries, GFC cropped up. Currently, the focus is to introduce Basel III¹ to address the concerns that emanated during the crisis.

The Reserve Bank of India (RBI),² 2002, defines credit risk as the risk of loss arising from outright default due to an inability or unwillingness of the customer or counterparty to meet commitments about lending, trading, hedging, settlement and other financial transactions. It also defines market risk as the possibility of loss of a bank due to changes in the market variables. Therefore, market risk encompasses interest rate risk, foreign exchange risk and equity price risk (RBI, 2002).

Indian banking industry provides a compelling case to be examined for its approach towards risk management. India has a long history of banks playing a pivotal role in the economic development. With the process of the financial sector reforms in 1991, the system has been liberalized gradually, and significant transformation has taken place. The Indian banking system comprises different banks, with differences in history and governance structures. At present, it constitutes of 26 public sector banks, 20 private sector banks, 43 foreign banks and 61 regional rural banks. Indian banking industry is valued at the US $ 1.31 trillion, with its total assets growing at a compound annual growth rate of 11.5 per cent during the financial years 2010–2013. According to the KPMG-CII report in 2013, it has the potential to be the fifth largest in the world by 2020. India’s largest lender, State Bank of India (SBI), is at the 38th position and India’s largest private sector bank ICICI is at the 99th position globally, when ranked by asset size (RBI, 2013).

Despite the significant influence of credit, equity, interest rate and foreign exchange risk in the banking sector, very little empirical evidence is available in the Indian context. The analysis of the effect of the GFC on the Indian bank’s risk exposures has not been studied extensively.

The study attempts to achieve its four-fold objectives: first, to measure the bank risk exposures and analyse why they vary in the pre- and post-GFC period; second, to examine the relationship between bank characteristics and risk exposures; third, to verify if our findings relating to risk exposures are robust for the market as well as accounting-based measures of risk; and, fourth, to develop a bank characteristic-based factor model which can explain risk exposures in the Indian context. Investigation across time periods helps us draw inferences on how the crisis has impacted risk exposures of banks and how the nature and magnitude of risk factors altered post the crisis period.

The remaining article is structured as follows. The second section encapsulates the related literature. Data and variable description are given in the third section. In the fourth section, we examine the risk exposures in the pre- and post-crisis period and their relationship with the various bank characteristics. In fifth section, a bank characteristic-based factor model is developed to help us explain the risk exposures for the Indian banking sector. The last section summarizes and provides concluding observations.

Review of Literature

In the literature, two main approaches have been adopted to quantify exposure of banks to various risks—the capital market approach and the accounting-based approach.

The capital market approach measures banks’ risk exposures by estimating the sensitivity of banks’ stock returns to movements in various risk factors. Theoretically, the capital market approach assumes that the bank’s equity returns reflect its valuations. Numerous studies have focused on the capital market approach to risk measurement for the developed markets including the US, Japan, Canada and Australia. Ryan and Worthington (2004) measure market, interest rate and foreign exchange risks using GARCH-M model, focusing on portfolio returns of Australian banks using daily data for the period 1996– 2001. The study finds that there exists significant market risk and short- and medium-term interest rate risk. Hooy, Tan and Nassir (2004) investigate the interest rate and exchange rate exposures of Malaysian banks, using a weekly dataset from 1 January 1995 to 26 July 2000, and find no significant differences across the Asian financial crisis for both the large and small banks. Elyasiani and Mansur (2005) apply the multi-factor GARCH model to assess the Japanese banking institutions’ market, interest rate and exchange rate risk exposure and draw a comparison with Elyasiani and Mansur (1998, 2004). The study also explores the relationship between the market-based measures of risk and accounting-based financial ratios, using data for 52 Japanese banks over the period 1986–1996. Their result indicates that market and exchange rate changes impact the equity returns in a positive direction, and accounting variables can describe these risks. Au Yong and Faff (2007) study bank risk exposures for both short term and long term in 10 markets of the Asia Pacific region for 2002–2003. The results suggest fewer restrictions on bank activities and ownership with increased private monitoring results in lower market risk exposure. The findings also indicate higher sensitivity to interest rate and exchange rate changes in case of newly industrialized economies, post the crisis period, especially for longer periods. Shamsuddin (2009) examines 10 publicly listed Australian banks’ exposure to the market, interest rate and foreign exchange rate risk using weekly data in the GARCH-in-mean model for the period 1994–2007. The findings indicate that large banks have higher interest rate risks with all banks exposed to market risk. Wong, Wong and Leung (2009) examine exchange rate exposure of 14 Chinese banks for the period 2005–2008 and found that almost half of Chinese listed banks had significant exchange rate risk exposure, with large banks more exposed. Kasman, Vardar and Tunç (2011) analyse 13 Turkish banks’ interest rate and exchange rate risk exposures for the period from 27 July 1999 to 9 April 2009, using OLS (Ordinary Least Squares) and GARCH estimation models. They report that market return changes have the strongest impact, with interest rate and exchange rate changes having a negative impact on banks stock returns. Bessler and Kurmann (2012) examine the magnitude and changes in risk exposures in the European Monetary Union and the US between 1990 and 2011 due to the introduction of Euro and the recent financial and sovereign debt crisis. The study includes credit, sovereign and real estate risk along with interest rate, foreign exchange rate and market risk in its model. Their findings indicate that stock prices reveal the time-varying nature of banks’ risk exposures. Sukcharoensin (2013) studies the market, interest rate and foreign exchange rate risk exposures using the GARCH approach with daily returns data from 4 January 2005 to 31 May 2012 for the 10 listed Thai banks, segregated by size. Their findings indicate that large banks have greater market risk and small banks higher exchange rate risk along with a decline in long-term interest rate sensitivity. Iorio, Faff and Sander (2013) find that interest rate sensitivity increases with increasing time horizon when examining financial sector stock return sensitivity to interest rate and exchange rate changes for pre- and post-Euro periods for the main euro and non-Eurozone countries.

Alternatively, in the accounting-based approach, several studies have examined how the accounting indicators explain the risk exposures of the banks. They use the reported accounting ratios of the different risk factors as measures of risk sensitivity of banks. Previous studies (Aggarwal & Jacques, 1998; Agoraki et al., 2011; Agusman et al., 2008; Berger & DeYoung, 1997; Corsetti, Persenti, & Roubini, 1999; Das & Ghosh, 2007; Gonzalez, 2005; Klomp & Haan, 2012; Rose, 1995) have measured credit risk in terms of the level of net non-performing loans or advances. RBI also suggests the use of level of non-performing loans as an indicator of credit risk. To assess the interest rate risk impact in banking, the financial literature and practice have developed two different approaches: the current earnings approach and the economic value approach. Maracine (2002) points out that the income gap analysis focuses on the effect of interest rate changes on banks’ income. RBI and BIS recommend the use of the repricing gap. Wright and Houpt (1996) and Landier, Sraer and Thesmar (2013) discuss the use of repricing GAP to measure IRR (Interest rate risk). Studies by Balakrishnan (2008, 2010) and Makkar and Singh (2013) estimate the interest rate risk for Indian banks using the repricing gap analysis approach. Choi, Elyasiani and Kopecky (1992) suggest that the impact of exchange rate depends on the banks’ net position in foreign currencies. Furthermore, Chamberlain, Howe and Popper (1997) observe that the net foreign exchange position as the accounting measure of exchange rate risk could only partially capture the actual magnitude of risk. Atindéhou and Gueyie (2001) report that an appreciation of the Canadian dollar increased bank stock return, on account of the Canadian bank’s negative foreign currency position for the sample period. Hahm (2004) attributes the negative impact of exchange rate changes on bank stock returns to the net short foreign currency position of banks.

While the capital market and accounting-based approaches may be conceptually different, they may not be contradictory. Several authors (Agusman et al., 2008; Elyasiani & Mansur, 2005; Jahankhani & Lynge, 1980; Karels, Prakash, & Roussakis, 1989; Pettway, 1976; Pettway & Sinkey, 1980; Rosenberg & Perry, 1981) have found significant relationship between accounting and market risk measures for the banking systems of developed countries.

The studies in India mainly focus on the issue of credit risk, market risk, exchange rate risk and interest rate risk measurement and management but in isolation. Patnaik and Shah (2004) find evidence of substantial interest rate risk exposure for 42 Indian banks for the period 2001–2002, by studying the impact of 320 bps interest rate shock on the net present value of cash flows. It also suggests that interest rate risk and credit risk are related in Indian banking. Sy (2005) examines the interest rate risk management for Indian banks’ government securities portfolios by calculating weighted durations and convexities. The study reports that the aggregate level of investment fluctuation reserve (IFR) in the banking system as on the end of March 2004 is inadequate to cover for market losses when the yield curve shifts parallel by one percentage point. Das and Ghosh (2007) examine the causes of credit risk (ex-post problem loans to total loans) in Indian state-owned banks for 1994–2005 using generalized method of moments and found that credit risk is significantly influenced by GDP growth, credit growth, operating expenses and size with no impact of ownership. Bodla and Verma (2009) studies the credit risk management framework in the context of Basel Accords and RBI guidelines and the effect of size and ownership for a sample of 26 banks, using a structured questionnaire. K. Bandopadyay and S. Bandyopadhyay (2010) conduct a comparative analysis of total risk, as measured by net interest margin using the mixed model approach with quarterly data for the period 2000–2007 for 87 banks. Analysing primary data of 35 Indian commercial banks for the period 2007–2008, Arora (2012) suggests existence of a significant relationship between bank size and the credit risk management strategies, and Arora (2013) concludes that ownership does not influence banks’ credit risk management operations and systems. Prabhavathi (2013) discusses different types of interest rate risk in the context of Basel and the RBI guidelines and argues that the economic perspective provides a more comprehensive measurement of interest rate risk than earnings perspective. It also comments on the substantial heterogeneity across the Indian banks in their interest rate risk exposures.

The present study assumes importance given the fact that the existing literature on risk measurement and management in banks for India is limited. Previous Indian studies focus mainly on one dimension of risk, that is, either on the credit risk or market risk or for components of market risk. This study aims to fill the research gap by covering different bank risk exposures using both capital market and accounting-based approach to obtain a more integrated view on the subject. It also investigates the impact of GFC on risk exposures for the Indian banks. The study also attempts to develop a bank characteristic-based factor model to identify potentially vulnerable banks. In conclusion, the experience of the current crisis and extant research strongly warrants the need for making concerted efforts to develop a more integrated approach towards measurement and management of different forms of risks. The findings of the study offer insights about the association between bank’s accounting ratios and market measures of risk, which is significant in light of the market discipline (Pillar 3 of Basel II norms).

Data and Variable Description

Data is used for individual banks, instead of relying on aggregate numbers. The study period is about 10 years from 23 October 2004 to 1 August 2014. The starting point of the sample period was determined by the availability of reliable bank data post the Narasimham Committee report, which advised banks to adopt prudential norms by the end of March 2002 and tightened the definition of non-performing Assets (NPAs) thereon. The period till 2004 is usually considered as the entire period of regulatory reforms (Zhao, Casu, & Ferrari, 2008) where banks were adjusting to the new regulations and norms. Also, data on the default spreads (BBB and AAA corporate yield spreads), used to construct an indicator of credit risk, is available from 2004 onwards, which guides the starting point of the sample period studied.

40 banks are considered for the analysis as the remaining 6 banks are not listed on the stock exchange. The list of banks is presented in the appendix. Foreign banks are not included in the study since as a group they have different behaviour in respect of the minimum priority sector lending (PSL) and its composition.³ Moreover, the financial performance of parent banks also plays a significant role in the assessment of risk for foreign banks’ group.

Data for the market measures of risk exposures is sourced from Bloomberg, and bank stock return data is sourced from the NSE website. Bank-wise data on the concerned variables for the assessment of risk exposures by the accounting-based approach and the bank-specific characteristics is obtained from RBI’s annual statutory publications. There is a report on Trend and Progress of Banking in India, giving bank-wise information on financial and prudential ratios, and Statistical Tables Relating to Banks in India, which furnish bank-wise balance sheet and income statement data. Annual reports of the commercial banks are also referred to (see Exhibit A).

Exhibit A.

Market based and Accounting–based risk measures

Risk Factor	Market-based		Accounting-based
Risk Factor	Measure	Source	Measure	Source
Equity risk factor	Weekly rate of return on the broad-based market index (S&P CNX 500)	Bloomberg	Exposure to capital market	RBI
Interest rate risk factor	Weekly rate of change in the 10-year long-term government bond yield	Bloomberg	Repricing GAP between the rate-sensitive assets and rate-sensitive liabilities	RBI
Exchange rate risk factor	Weekly rate of change of the Indian rupee against the US dollar	Bloomberg	Difference between foreign currency assets and foreign currency liabilities of a bank	RBI
Credit risk factor	Weekly change in the difference between yields of BBB- and AAA-rated corporate bonds.	Bloomberg	Level of non-performing assets of a bank	RBI

Source: Authors’ calculations.

For the description of various bank characteristics used to classify them into different categories, refer Exhibit B.

Exhibit B.

Description of Bank specific characteristics

Bank Characteristics	Measure	Source	Studies
Capitalization	Equity to assets ratio	Prowess	Brunnermeier and Pedersen (2009), Oura, González-Hermosillo, Chan-Lau, Gudmundsson and Valckx (2013)
Asset quality	Ratio of net NPA to net advances	RBI	Iannotta, Nocera and Sironi (2007), Klomp and Haan (2012), Agusman et al. (2008)
Management efficiency	Net interest margin	RBI	Ghosh and Sensarma (2004), Marinković and Radović (2014)
Earnings/profitability	Return on assets	RBI	Brewer and Lee (1986), Goodhart (2010)
Liquidity	Credit to deposit ratio	RBI	Le Leslé and Avramova (2012), De Haan and van den End (2013)
Ownership	As per RBI categories	RBI	Iannotta et al. (2007), Das and Ghosh (2007), Arora (2013)
Size	Natural log of total assets	RBI	Rajan and Dhal (2003), Das and Ghosh (2007), Hakenes and Schnabel (2011), Arora (2012)
Diversification	Ratio of non-interest income to total assets	RBI	DeYoung and Roland (2001), Baele, De Jonghe and Vander Vennet (2007), Chiorazzo, Milani and Salvini (2008)
Valuation	Price to book ratio	Prowess	Masera and Mazzoni (2014)

Source: Authors’ calculations.

Table 1.

Descriptive statistics for the risk variables used in the capital market and the accounting-based approaches risk measures for the pre- and post-crisis periods. It shows the mean, median, standard deviation, skewness, kurtosis, minimum and maximum values

	Capital Market				Accounting
	R_m	R_i	R_f	R_c	GAP	NOP	NPA
Panel A: Pre-crisis period (2004–2008)
Mean	0.005	0.002	0.000	−0.004	0.036	0.001	1.190
Median	0.010	0.001	0.000	−0.004	0.011	0.000	0.980
Maximum	0.090	0.057	0.030	0.491	0.405	0.061	5.230
Minimum	−0.212	−0.069	−0.022	−0.485	−0.153	−0.081	0.090
Std. Dev.	0.036	0.016	0.007	0.101	0.101	0.014	0.896
Skewness	−1.597	−0.132	0.551	0.117	1.388	−0.337	1.690
Kurtosis	9.677	6.411	6.109	10.529	4.709	12.038	7.024
Panel B: Post-crisis period (2009–2014)
Mean	0.004	0.001	0.001	−0.002	−0.006	−0.002	1.396
Median	0.005	0.001	0.001	0.000	–0.004	0.000	1.190
Maximum	0.169	0.116	0.045	0.269	0.262	0.050	7.180
Minimum	−0.095	−0.083	−0.049	−0.292	–0.242	–0.051	0.010
Std. Dev.	0.028	0.019	0.012	0.062	0.074	0.014	1.070
Skewness	0.652	0.707	0.115	−0.926	0.305	–1.010	1.412
Kurtosis	7.205	11.504	5.087	9.557	5.727	6.098	7.154

Source: Authors’ calculations.

Notes: The table presents descriptive statistics for the raw variables. The study uses weekly observations of 40 listed commercial banks in India from the period 23 October 2004 to 1 August 2014. Capital market risk measures include R_m which is the weekly rate of return on the broad-based market index (S&P CNX 500); R_r is the weekly rate of change in the 10-year long-term government bond yield; R_f is the weekly rate of change of the Indian rupee against the US dollar; and R_c is the weekly change in the difference between yields of BBB- and AAA-rated corporate bonds. Furthermore, accounting risk measures include NPA, ratio of net non-performing assets to net advances measuring credit risk; GAP, difference between rate sensitive assets (RSA) and rate sensitive liabilities (RSL) scaled to total assets, measuring interest rate risk; and net open foreign currency position (NOP), difference between foreign currency assets and foreign currency liabilities scaled by the total assets, measuring exchange rate risk.

The summary statistics for the variables used in the market-based and accounting-based approaches are reported in Table 1, including distributional parameters like mean, standard deviation, skewness and kurtosis.

Bank Characteristics and Risk Exposures in the Pre- and Post-GFC Period

We assess the risk exposures of banks via two approaches: market based and accounting based. This exercise helps us to understand the association between risk measures obtained from the two alternative methods.

Market-based Approach

Methodology

A multivariate approach is used to assess the risk exposures of the banks, where bank stock returns are regressed on market index return, interest rate changes, foreign exchange rate changes, change in the credit risk factor and its conditional volatility. Conditional variance of bank stock returns is estimated by a GARCH (1, 1) process and a lagged interest volatility term, as proposed by Elyasiani and Mansur (1998) to incorporate incremental information on macroeconomic volatility.

The mean equation is as follows:

R_{j, t} = β_{0} + β_{m} R_{m, t} + β_{r} R_{r, t} + β_{f} R_{f, t} + β_{c} R_{c, t} + γ \sqrt{h_{j, t}} + ε_{j, t}

ε_{j, t} Ω ~ N (0, h_{j, t})

(1)

where R_j_,t is the weekly stock return of bank j at time t, R_m_,t is the weekly rate of return on the broad-based market index (S&P CNX 500) at time t, following Basel market risk regulation; R_r_,t is the weekly rate of change in the 10-year long-term government bond yield at time t; R_f_,t is the weekly rate of change of the Indian rupee against the US dollar at time t; R_c_,t is the weekly change in the difference between yields of BBB- and AAA-rated corporate bond at time t (Bessler & Kurmann, 2012; Demsetz & Strahan, 1997); h_j,t is the conditional variance of bank stock returns; and ε_j,t is a serially uncorrelated normally distributed random error term. Coefficients β_m, β_r, β_f and β_c represent market, interest rate, exchange rate and credit risk, respectively, and β₀ is a constant term.

The variance equation is:

h_{j, t} = α_{0} + α_{1} ε_{j, t - 1}^{2} + α_{2} h_{j, t - 1} + α_{3} (C I V_{t - 1})

(2)

Conditional variance, h_j_,t, is determined by the past behaviour of the lagged squared error terms obtained from the mean equation, $ε_{j, t - 1}^{2}$ , and the previous period conditional variance, h_j_,t–1, and preceding period’s conditional interest rate volatility, CIV_t–1. α₁ and α₂ are the ARCH and GARCH terms, respectively, and α₃ is the coefficient of lagged interest rate volatility, and α₀ is the constant.

Above equations have been estimated on a fixed 52 estimation window, rolling 52 weeks every time. This process results in annual estimates of beta coefficients, which are used as a measure of risk exposures. As a robustness check, a 13-week rolling window is also used, which results in quarterly beta estimates.

The entire dataset is subdivided into three sub-periods. The estimations are done for the pre-crisis (from October 2004 to September 2008) and the post-crisis period (from April 2009 to August 2014). The period from October 2008 to March 2009 is considered as the crisis period, in line with Morales and Andreosso-O’Callaghan (2010). In the literature, there is strong consensus that by October 2008, after Lehman Brothers collapsed, the crisis became truly global, both in spread and in impact (Filardo et al., 2010; International Monetary Fund [IMF], 2010; IEG, 2012). Arora, Rathinam and Khan (2010) observe noticeable signs of recovery from the second quarter of the 2009–2010 for the Indian economy.

Previous literature (Flannery & James, 1984; Fraser, Madura, & Weigand, 2002; Au Yong, Faff, & Chalmers, 2009) suggests segregation of banks according to their balance sheet characteristics to identify potentially weak banks. Bongini, Laeven and Majnoni (2002) find that CAMEL ratings⁴ are strongly related to distress for a sample of East Asian banks. In the present study, the differences in the risk exposures of banks are analysed on different bank characteristics. Following Brunnermeier and Pedersen (2009), capitalization ratio (equity to total assets) is considered as an indicator to assess and identify bank vulnerabilities. Higher capital base reduces the perceived risk exposure of the bank. Asset quality, as gauged by the level of NPAs, affects the risk exposure as a higher NPA would mean poor asset quality, higher provisioning and write-offs, reducing profitability and earnings and increasing risk. Management efficiency is reflected by the level of net interest margins (NIM) a bank can earn. Higher the NIM (excess interest income over interest expense scaled by total assets), higher is the efficiency and thus lower is the risk exposure. Earning levels of the bank is represented by the amount of profit earned by the bank, indicated by return on assets (ROA). Ex-ante the relationship between profitability and risk is not clear. A more profitable bank would possess more resources to mitigate risk. On the other hand, it is also possible that higher profitability is the result of increased risk undertaken. Renewed focus on the liquidity of banks and the effect it has on the level of bank’s risk post the crisis motivate us to include credit deposit ratio as a measure of liquidity in the study. Too high a credit deposit ratio would mean less liquidity available with the bank. Too small a ratio would mean the bank is paying a very high premium for maintaining the level of liquidity. Additional factors like bank size, ownership, diversification and price-to-book ratio, apart from the CAMEL ratings, are also included in the analysis. Bank size is measured using natural logarithm of total assets, and the sample is divided into small and large banks using median breakpoint. Large banks are believed to be less risky as they contribute more to the issue of systemic risk and hence enjoy greater government protection under the argument of ‘too big to fail’ (Adrian & Brunnermeier, 2011). Furthermore, they have more opportunities for diversification, both activity and geographical (DeYoung & Roland, 2001). On the contrary, the complex large entities that are difficult to monitor can be exposed to higher risk, thus making bank size an important factor affecting risk. The ownership structure of the bank is also shown to impact the level of banks’ risk exposure (Arora, 2013; Das & Ghosh, 2007; Iannotta et al., 2007).

The literature indicates that public banks are set up with the motive of increased social well-being and inclusive economic development (Stiglitz, 1993). They assist projects which are socially advantageous and do not have access to other sources of financing, thus making public sector banks riskier. The impact diversification has on the level of risk has been extensively deliberated by prior studies. DeLong (2001) concluded for a sample of US banks that diversification fails to create value. DeYoung and Roland (2001) associate fee-based activities with increased revenue volatility and thus risk. In accordance with Baele et al. (2007), the ratio of non-interest income to total operating income is used to study the diversification effects on risk. Masera and Mazzoni (2014) suggest price-to-book ratio as more useful in forecasting banks’ future distress than the accounting ratios. For analysing this argument in the Indian context, it is included as one of the classifying variables.

Empirical Results

The results of estimations with 52 weeks rolling window for the sub-periods are presented in Table 2. When considering the equity risk, the beta value increases from pre- to post-crisis implying increased banks’ exposure to market movements post the crisis for the entire banking industry. It is positive for the whole banking sector as well as the bank groups in both pre- and post- crisis periods. There is a presence of weak negative credit risk exposure for all the banks and banks segregated into different groups with no visible change from the pre- to the post-crisis period (Aretz et al., 2011).

The interest rate risk beta coefficient has a negative sign for the entire banking industry along with all bank groups except small-sized, well-capitalized and well diversified private sector banks. A negative sign indicates that banks net worth erodes when the interest rate rises. Banks which possess higher capital base and which are more diversified can hedge their interest rate risk effectively. Private banks are considered to be better risk managers. In the post-crisis period, the interest rate risk beta coefficient changes from negative to positive, suggesting a reduction in the interest rate risk exposure for the Indian banking industry. Moreover, when analysing bank groups, only small-sized banks with small credit to deposit ratio and high-interest margins are exposed to interest rate risk owing to higher earnings at risk and also banks with a higher proportion of investment lose more in the case of rising interest rates. Post the crisis, small banks are perceived to be more vulnerable to interest rate risk, owing to fewer resources at their disposal.

Table 2.

Banks’ equity price, interest rate and exchange rate and credit risk exposures for the entire banking industry and by bank groups—with 52 weeks rolling window estimation. Panel A and B are depicting results for the pre- and post-crisis periods, respectively

I	Panel A: Pre-crisis period (2004–2008)					Panel B: Post-crisis period (2009–2014)
I	β₀	β_m	β_r	β_f	β_c	β₀	β_m	β_r	β_f	β_c
All Banks	−0.0032	0.9673	−0.0215	−0.2291	−0.0276	−0.0003	1.5104	0.0779	0.2070	−0.0266
Capitalization
High EA	−0.0034	1.0632	0.0469	0.1458	−0.0373	0.0022	1.4074	0.0493	0.1379	−0.0494
Low EA	−0.0030	0.8714	−0.0898	−0.4841	−0.0179	−0.0029	1.6134	0.1065	0.2752	−0.0039
Asset Quality
Low NPA	−0.0019	0.8860	−0.0243	−0.3348	−0.0246	−0.0033	1.5923	0.0353	0.2306	0.0068
High NPA	−0.0045	1.0486	−0.0186	−0.0035	−0.0305	0.0026	1.4285	0.1205	0.1825	−0.0601
Management Efficiency
High NIM	−0.0039	0.8921	−0.0332	0.0667	−0.0202	0.0020	1.4121	−0.0020	0.1366	−0.0481
Low NIM	−0.0026	1.0425	−0.0097	−0.4050	−0.0350	−0.0026	1.6087	0.1578	0.2765	−0.0052
Earnings
High ROA	−0.0045	1.0566	−0.0149	0.0174	−0.0278	0.0011	1.4488	0.0146	0.1183	−0.0507
Low ROA	−0.0020	0.8780	−0.0280	−0.3557	−0.0273	−0.0018	1.5720	0.1412	0.2948	−0.0026
Liquidity
High CD	−0.0016	0.9698	−0.0215	−0.0088	−0.0203	0.0061	1.3786	0.1645	−0.0016	−0.0221
Low CD	−0.0048	0.9648	−0.0215	−0.3295	−0.0348	−0.0068	1.6422	−0.0088	0.4147	−0.0312
Ownership
Public	−0.0043	0.9221	−0.1277	−0.5255	−0.0198	−0.0025	1.5824	0.0032	0.1763	−0.0092
Private	−0.0017	1.0350	0.1380	0.3654	−0.0393	0.0029	1.4024	0.1899	0.2519	−0.0527
Size
Large TA	−0.0037	1.0721	−0.1736	−0.5284	−0.0248	0.0001	1.6224	0.1558	−0.0247	−0.0365
Small TA	−0.0027	0.8625	0.1306	0.1901	−0.0303	−0.0008	1.3984	0.0000	0.4378	−0.0168
Diversification
High Non	−0.0035	0.9947	0.0873	0.2707	−0.0287	0.0019	1.4297	0.0567	0.1900	−0.0519
Low Non	−0.0030	0.9399	−0.1302	−0.6090	−0.0265	−0.0026	1.5912	0.0991	0.2231	−0.0014
Price to book ratio
High PB	−0.0033	1.1155	−0.0059	−0.0689	−0.0221	0.0037	1.4197	0.0536	0.0613	−0.0481
Low PB	−0.0032	0.8191	−0.0370	−0.2694	−0.0331	−0.0044	1.6011	0.1021	0.3518	−0.0052

Source: Authors’ calculations.

Notes: The coefficient estimates reported in this table are obtained from Equation 1:

R_{j, t} = β_{0} + β_{m} R_{m, t} + β_{r} R_{r, t} + β_{f} R_{f, t} + β_{c} R_{c, t} + γ \sqrt{h_{j, t}} + ε_{j, t}

R_j_,t is the weekly stock return of bank j at time t, R_m_,t is the weekly rate of return on the broad-based market index (S&P CNX 500) at time t, following Basel market risk regulation; R_r_,t is the weekly rate of change in the 10-year long-term government bond yield at time t; R_f_,t is the weekly rate of change of the Indian rupee against the US dollar at time t; and R_c_,t is the weekly change in the difference between yields of BBB- and AAA-rated corporate bond at time t. 52 weeks rolling window is used for each variable in the Equation 1.

In the pre-crisis period, there exists a negative beta coefficient for exchange rate risk for the entire banking industry along with all other bank groups except for small-sized well-capitalized, well-diversified, more efficient and profitable private sector banks. A negative sign indicates that as the rupee depreciates, the bank stock return falls and thus banks have exchange rate exposure. One of the possible explanations for the above outcome is that the non-financial corporate sector in the emerging market carries significant amounts of forex debt, which indirectly contributes to the negative bank stock returns (Chamberlain et al., 1997; Hahm, 2004). Small banks have more localized operations; private banks are better risk managers, higher profits have access to more resources and higher margins indicate greater management efficiency. Furthermore, high level of diversification helps mitigate the exchange rate risk exposure. In the post-crisis period, the beta sign changes for exchange rate risk from negative to positive indicating a reduced exchange rate exposure, that is, as rupee depreciates the bank stock return increases. This result is true for all the bank groups except large-sized banks with less liquidity. Big banks have more overseas operations and hence higher ER (Exchange rate) risk exposures (Wong et al., 2009). Furthermore, less liquidity increases risk exposure and creates liquidity premium.

The results discussed here are based on 52 weeks rolling window estimations. The above results provide evidence that risk sensitivities are time varying in nature and also differ from banks with different characteristics. The 13-week rolling window estimate results are presented in Table 3. Broadly similar patterns are observed for all types of risks when considering the entire banking industry except the negative beta coefficient of interest rate risks in the post-crisis period. At the characteristic-sorted bank group level, the results of 13 weeks rolling window estimation are comparable but with few exceptions. For the equity risk and credit risk, the results are consistent. The probable reason for the difference in the interest rate risk assessment could be the reasonable control of banks over their asset mix in the short run, that is, on a quarterly basis, making it difficult for them to respond quickly to changing market conditions. In the case of exchange rate risk, the difference in the risk exposures primarily exists because it is difficult for the investors to know currency exposures on a day-to-day basis accurately (Kho & Stulz, 2000). Furthermore, Iorio et al. (2013) also point out the operational/transactional nature of the risk exposures in the short run, making it difficult to identify and hedge vis-à-vis long term.

The prior literature recommends the use of a longer horizon to accurately assess banks risk exposures as it reduces the noise in the data (Richardson & Smith, 1991; Valkanov, 2003). Hence, for further analysis in this article, we shall focus on 52 week rolling window-based risk exposures.

Accounting-based Approach

For checking the consistency of the results, different risk indicators are also measured by the balance sheet reported data, that is, accounting-based indicators on an annual basis.⁵

The variables used as measures of risks are as follows—NPA, ratio of net non-performing assets to net advances, for credit risk; interest rate risk is measured through the repricing gap analysis; GAP, the difference between the RSA and RSL scaled to total assets (Mishkin & Eakins, 2006), for interest rate risk; and NOP, the difference between foreign exchange assets and foreign exchange liabilities scaled by the total assets, for exchange rate risk. (Chamberlain et al., 1997). Equity risk has been measured by the banks’ exposure to capital market sector, which includes both the direct and the indirect exposure.

Table 3.

Banks’ equity price, interest rate and exchange rate and credit risk exposures for the entire banking industry and by bank groups with 13 weeks rolling window estimation. Panel A and B are depicting results for the pre- and post-crisis periods, respectively

I	Panel A: Pre-crisis period (2004–2008)					Panel B: Post-crisis period (2009–2014)
I	β₀	β_m	β_r	β_f	β_c	β₀	β_m	β_r	β_f	β_c
All Banks	−0.0053	0.8704	−0.1527	−0.5001	−0.0186	−0.0040	1.4265	−0.0593	0.1529	−0.0298
Capitalization
High EA	−0.0036	0.8334	−0.0958	−0.3744	−0.0183	−0.0031	1.4403	0.0462	0.2597	−0.0309
Low EA	−0.0034	0.9178	−0.0730	−0.1628	−0.0234	−0.0005	1.2863	0.0043	0.0876	−0.0255
Asset Quality
Low NPA	−0.0040	1.0087	−0.0314	−0.1358	−0.0320	0.0008	1.3327	0.0439	0.2032	−0.0521
High NPA	−0.0041	1.0571	−0.0283	−0.2136	−0.0262	0.0001	1.3209	−0.0232	0.1001	−0.0383
Management Efficiency
High NIM	−0.0035	0.9694	−0.0262	−0.3801	−0.0333	−0.0031	1.5149	0.0508	0.2134	−0.0244
Low NIM	−0.0039	0.9077	−0.0334	−0.2028	−0.0273	−0.0019	1.3867	0.0088	0.1825	−0.0348
Earnings
High ROA	−0.0033	0.8186	−0.0294	−0.2516	−0.0275	−0.0032	1.4220	0.0617	0.2578	−0.0226
Low ROA	−0.0051	0.9613	−0.0652	−0.4009	−0.0243	−0.0022	1.3220	−0.0232	0.1321	−0.0418
Liquidity
High CD	−0.0044	0.8976	0.0062	−0.2427	−0.0311	−0.0033	1.4872	0.0134	0.2774	−0.0442
Low CD	−0.0028	0.9219	0.0796	0.1179	−0.0293	−0.0015	1.3329	−0.0232	0.1457	−0.0383
Ownership
Public	−0.0018	0.9637	0.1456	0.2432	−0.0403	0.0013	1.3271	0.1109	0.2268	−0.0424
Private	−0.0039	1.0631	−0.2133	−0.5017	−0.0247	−0.0005	1.4862	−0.0361	0.0569	−0.0364
Size
Large TA	−0.0035	0.8285	0.1544	0.0903	−0.0333	−0.0016	1.2721	0.0413	0.3058	−0.0293
Small TA	−0.0045	0.9969	−0.0374	−0.1539	−0.0270	−0.0006	1.3515	−0.0441	0.1071	−0.0470
Diversification
High Non	−0.0050	0.8936	−0.1464	−0.5234	−0.0252	−0.0023	1.4406	0.0409	0.2193	−0.0314
Low Non	−0.0038	0.8067	−0.0353	−0.2698	−0.0225	−0.0046	1.4408	−0.0263	0.1618	−0.0176
Price to book ratio
High PB	−0.0038	0.7583	−0.0385	−0.1919	−0.0283	−0.0039	1.4526	0.0409	0.2649	−0.0314
Low PB	−0.0044	0.8460	−0.0406	−0.0254	−0.0213	−0.0007	1.2586	−0.0332	0.1516	−0.0453

Source: Authors’ calculations.

Notes: The coefficient estimates reported in this table are obtained from Equation 1:

R_{j, t} = β_{0} + β_{m} R_{m, t} + β_{r} R_{r, t} + β_{f} R_{f, t} + β_{c} R_{c, t} + γ \sqrt{h_{j, t}} + ε_{j, t}

R_j_,t is the weekly stock return of bank j at time t, R_m_,t is the weekly rate of return on the broad-based market index (S&P CNX 500) at time t, following Basel market risk regulation; R_r_,t is the weekly rate of change in the 10-year long-term government bond yield at time t; R_f_,t is the weekly rate of change of the Indian rupee against the US dollar at time t; and R_c_,t is the weekly change in the difference between yields of BBB- and AAA-rated corporate bonds at time t. 13 weeks rolling window is used for each variable in the Equation 1.

A priori, higher NPA implies an increase in credit risk exposure of the bank and vice versa (Mileris, 2012). Also, a higher absolute capital market exposure indicates a higher equity risk for the bank. For the other two risks, the sign and the magnitude of the coefficients are the primary focus of our analysis. Interest rate risk exposure increases with an increase in the absolute value of repricing GAP (Rahman, Majid, & Tamadonnejad, 2010). If interest rates are rising/falling, earnings of the bank with a negative/positive GAP (RSA</>RSL) are exposed to risk. The impact of exchange rate changes on the NOP will depend on whether the rupee is appreciating or depreciating. A net long/short foreign currency position is beneficial for the bank if the rupee is depreciating/appreciating.

Table 4 reports the results for the accounting-based approach for the pre- and post-crisis periods. Banks exhibit high credit risk exposure for both the sub-periods, with a significant increase from pre- to post-crisis periods. This result holds true for the entire banking industry and all the bank groups. In the case of equity risk, one can observe a significant increase from the pre- to the post-crisis period for the entire banking industry and all bank groups. In the case of interest rate risk, we see a reduction in the value of absolute repricing GAP, indicating a fall in the interest rate risk exposure from the pre- to the post-crisis period for the entire banking industry. Moreover, the nature of the repricing GAP changes from positive to negative from the pre- to post-crisis period. In the pre-crisis period, besides the whole banking industry, all the other bank groups also have positive repricing GAP, indicating that banks earnings increase with an increase in the interest rates. In the post-crisis period, there exists a negative repricing GAP implying an adverse impact of interest rate changes on the banks’ earnings for all the bank groups except large-sized public sector banks with small equity to asset ratio (indicating a high deposit base available with large public sector banks) and good asset quality. In the case of exchange rate risk, the Indian banking industry has a net long foreign currency position (positive NOP) in the pre-crisis period and a net short foreign currency position (negative NOP) in the post-crisis period, suggesting increased exchange rate risk exposure of banks post the crisis. When looking at banks segregated into groups by bank-specific characteristics, well-capitalized small-sized but overvalued, less liquid, less efficient, less profitable private sector banks are more vulnerable to exchange rate changes in the pre-crisis period. However, during the post-crisis period, all banks have substantial exchange rate exposure except highly liquid public sector banks with lower asset quality.⁶ This result is indicative of the favourable opinion towards public sector banks post the crisis when the investor behaviour is expected to be more risk averse.

Drawing a comparison between the findings from both approaches for the credit risk and equity risk, confirmatory results across all categories and the crisis period are observed. Interest rate risk assessment differs from both the approaches. It is probably because the accounting measure of interest rate risk captures the earnings exposures to the interest rate changes whereas the market assessment of interest rate risk incorporates the net worth exposure to interest rate changes⁷ (Bessis, 2011; Hull, 2012). In the pre-crisis period, small-sized, well-capitalized well-diversified private sector banks manage and hedge the IRR exposure from both the perspectives. For all the other bank groups, though the earnings are not exposed to interest rate changes their net worth erodes when interest rate rises. In the post-crisis period, both market- and accounting-based measures indicate substantial interest rate risk exposure for small-sized banks with low credit to deposit ratio and higher interest margins and limited interest rate risk exposure for highly leveraged large-sized public sector banks with good asset quality. For all the other bank groups, though their earnings are exposed to interest rate changes their net worth is insulated from the interest rate changes, probably because the market feels that banks have actively hedged their interest rate exposure by the use of off-balance sheet activities and derivatives. Moreover, deposit rates being stickier than the lending rates (Driscoll & Judson, 2013) mitigate the negative impact of rising interest rates on banks with a negative repricing GAP. Furthermore, post the crisis, market perception of risk has changed reflecting their favourable opinion about large-sized public sector banks on account of the implicit government guarantee they possess in case of distress or failure and the ‘too big to fail’ effect. In the pre-crisis period, market assessment of exchange rate risk differs from the accounting-based measure probably because of the indirect adverse effect on the non-financial corporate sector, who has huge amount of foreign currency loans on their books. In the post-crisis period, though banks have a negative NOP, the market has increased confidence in banks’ risk hedging and managing capabilities on account of the limited impact of the crisis on the Indian banking sector. When analysing the bank groups, both the approaches indicate reduced ER risk exposure for banks with higher profits, greater diversification and larger margins. Increased ER exposure is observed for overvalued banks with lower profitability, efficiency and liquidity. For small-sized, well-capitalized private sector banks, market-based measure suggests limited exchange rate risk exposure in spite of a net short foreign currency position. Possibly these banks are perceived to have more localized operations and thus limited exchange rate risk or higher risk-bearing capacity, or they might be undertaking active hedging strategies to manage currency risk. For all the other bank groups and as well as the total banking industry, market-based measure indicates substantial exchange rate risk exposure. It is on account of the significant indirect effect on the non-financial corporate sector borrowings and absence of active risk hedging practices by the banks, as observed in the low level of diversification and less securitization options which are still relatively undeveloped in the Indian context. In the post-crisis period, large banks with low levels of liquidity are considered riskier according to both the approaches, maybe due to increased overseas presence of these banks with less liquidity amplifying the risk exposure. Furthermore, results from both methods indicate that highly liquid public sector banks with lower asset quality have limited exchange rate exposure (public sector banks usually have a high level of NPAs). For all the other bank groups, though there is a net short foreign currency position, according to market-based measure, the adverse effect of rupee depreciation is probably moderated because market perceives banks to be able to hedge and manage exchange rate risk effectively to safeguard themselves from the increased volatility in the exchange rate market.

Table 4.

Banks’ interest rate and exchange rate and credit risk exposures for the entire banking industry and by bank groups with an accounting-based approach. Panel A and B are depicting results for the pre- and post-crisis periods, respectively

i	Panel A: Pre-crisis period (2004–2008)			Panel B: Post-crisis period (2009–2014)
i	GAP	NOP	NPA	GAP	NOP	NPA
All Banks	0.0356	0.0006	1.1899	−0.0064	−0.0025	1.3959
Capitalization
High EA	0.0373	−0.0005	1.1418	–0.0222	–0.0043	1.1779
Low EA	0.0335	0.0020	1.2484	0.0098	–0.0006	1.6196
Asset Quality
Low NPA	0.0085	0.0008	0.6341	0.0007	–0.0061	0.6627
High NPA	0.0635	0.0005	1.7679	–0.0136	0.0011	2.1292
Management Efficiency
High NIM	0.0321	0.0020	0.9174	−0.0052	−0.0034	0.9834
Low NIM	0.0388	−0.0006	1.4385	−0.0077	−0.0015	1.8191
Earnings
High ROA	0.0443	0.0017	0.8410	−0.0026	−0.0045	0.8268
Low ROA	0.0260	−0.0005	1.5722	−0.0104	−0.0004	1.9797
Liquidity
High CD	0.0217	−0.0007	1.2221	−0.0011	−0.0058	1.1673
Low CD	0.0499	0.0021	1.1572	−0.0119	0.0009	1.6305
Ownership
Public	0.0053	0.0034	1.0865	0.0005	0.0008	1.8389
Private	0.0739	−0.0029	1.3225	−0.0166	−0.0073	0.7453
Size
Large TA	0.0043	0.0017	0.9820	0.0041	−0.0008	1.5615
Small TA	0.0752	−0.0006	1.4567	−0.0170	−0.0042	1.2304
Diversification
High Non	0.0418	0.0005	1.1063	−0.0099	−0.0042	1.1478
Low Non	0.0291	0.0008	1.2768	−0.0028	−0.0007	1.6505
Price to book ratio
High PB	0.0381	−0.0002	1.1370	−0.0017	−0.0029	1.1514
Low PB	0.0327	0.0016	1.2510	−0.0112	−0.0021	1.6468

Source: Authors’ calculations.

Notes: NPA, ratio of net non-performing assets to net advances, measures credit risk; GAP, difference between RSA and RSL scaled to total assets, measures interest rate risk; and NOP, difference between foreign currency assets and foreign currency liabilities scaled by the total assets, measures exchange rate risk.

From the empirical analysis, the capital market approach has certain advantages over the accounting-based approach for the study of the Indian market. First, since accounting data of banks is available at a lower frequency than banks’ equity market data, it’s hard to use the accounting approach for a banking system of an emerging economy like India which has a short financial history. Second, Martin and Mauer (2003) and Muller and Verschoor (2006) observe that the risk exposure estimated by the capital market approach is forward looking and suggest that from a policy analysis perspective, it may be more appropriate. Capital market approach gains prominence by Pillar 3 of Basel II of Market discipline (Herring, 2004). According to Greenspan (2001) ‘Market discipline—private counterparty supervision—is, and always has been, the first line of regulatory defence in protecting the safety and soundness of the banking system.’ Hence, for subsequent analysis, we use capital market-based measures of risk exposures.

Developing a Bank Characteristics-based Factor Model

The objective of this section is to verify the relevance of bank-specific characteristics for assessing their impact on bank’s risk exposure, as measured by the time-varying betas of respective risks. This exercise helps to identify weak banks with significant exposures. The analysis of the relationship between bank betas and observed bank characteristics helps us identify areas where policymakers could focus on enhanced supervision, in light of bank-specific characteristics.

We use a panel of annual data to develop a bank characteristic-based factor model and examine the relationship between the estimated market-based measures of risk and fundamental bank characteristics. Panel data methodology controls for individual heterogeneity, the problem of multicollinearity and estimation bias and considers both space and time dimensions of the data (Baltagi, 2001; Hsiao, 2014). We work on unbalanced panels as some of the banks were incorporated later in the study period.⁸ Hausman test suggests random effects model perform better vis-à-vis fixed effects model. We follow the previous literature for choosing our bank characteristics in the model, as discussed in the previous section.

We estimate the following model for the total sample period from 2004 to 2014.

\begin{matrix} β_{j, t} = α_{i} + β_{1} (O W N) + β_{2} (T A) + β_{3} (C D) + β_{4} (N I M ​) + β_{5} (N O N ​) \\ + β_{6} (R O A) + β_{7} (N P A) + β_{8} (P B) + β_{9} (E A) + β_{10} (C r i s i s) + μ_{i, t} \end{matrix}

(3)

where β_j,t represents the four time-varying betas of equity risk, credit risk, interest rate risk and exchange rate risk of banks as dependent variables, estimated in the above section.

The ownership factor is accounted for by introducing a dummy OWN, which takes a value of 1 for private banks and 0 for public banks. TA proxies for the size factor and is calculated by taking the natural log of total assets. CD approximates for the credit to deposit ratio, an indicator of liquidity and NIM, net interest margins indicates management efficiency. NON is the ratio of non-interest income to total operating income indicating the level of diversification for the bank and profitability is measured by ROA. NPA, net NPAs to net advances, shows the asset quality of the bank and PB is the price-to-book ratio, which captures the valuation factor. EA is the equity-to-asset ratio, a measure of capital base available with the bank. All the above variables take a value 1, when their value is higher than the median value and 0 otherwise. For instance, value 1 for TA signifies a large-sized bank, for NPA, value of 0 would indicate that there are less NPAs. To identify the role of the determinants of bank risk, we control for the crisis period effects by introducing a crisis dummy, which takes a value 0 for the period prior to the crisis and 1 for period post the crisis.

The results presented in Table 5 indicate that large banks have higher equity risk exposure and size is the only determining factor for equity risk. Several studies have concluded that larger banks have more incentives to indulge in riskier activities and large complex entities become difficult to monitor, resulting in higher risk exposures. Banks of different sizes have different risk attitudes. Big banks have the problem of moral hazard behaviour.

Table 5.

Estimated coefficients from panel data estimations, years 2004–2014

	β_m	β_r	β_f	β_c
Capitalization (EA)	−0.0031	0.1230	0.2955	0.0061
	(−0.04)	(0.85)	(1.35)	(0.21)
Asset quality (NPA)	0.1018	0.0820	0.3338	0.0117
	(1.36)	(0.55)	(1.48)	(0.39)
Management efficiency (NIM)	−0.1054	−0.1238	−0.0562	−0.0017
	(−1.32)	(−0.79)	(−0.24)	(−0.06)
Earnings (ROA)	−0.0390	0.0192	0.2313	−0.0222
	(−0.46)	(0.11)	(0.9)	(−0.66)
Liquidity (CD)	−0.0406	0.1385	−0.2294	−0.0016
	(−0.58)	(1.01)	(−1.1)	(−0.06)
Ownership (OWN)	0.0354	0.4241	0.5021	−0.0745
	(0.35)	(2.2)	(1.72)	(−1.94)
Size (TA)	0.2025	0.2235	−0.2298	−0.0470
	(2.16)	(1.27)	(−0.86)	(−1.33)
Diversification (NON)	0.0295	−0.0280	−0.4890	0.0342
	(0.37)	(−0.18)	(−2.07)	(1.1)
Price-to—book ratio (PB)	−0.0511	−0.1347	−0.3920	0.0258
	(−0.7)	(−0.94)	(−1.8)	(0.9)

Source: Authors’ calculations.

Notes: The coefficient estimates and t-statistics (in parentheses) reported in the table are obtained from Equation 3:

\begin{matrix} β_{j, t} = α_{i} + β_{1} (O W N) + β_{2} (T A) + β_{3} (C D) + β_{4} (N I M) + β_{5} (N O N) \\ + β_{6} (R O A) + β_{7} (N P A) + β_{8} (P B) + β_{9} (E A) + β_{10} (C r i s i s) + μ_{i, t} \end{matrix}

β_j,t represents the four time-varying betas of market risk, credit risk, interest rate risk and exchange rate risk of banks as dependent variables, estimated in the above section. OWN, dummy variable for ownership which takes a value 1 for private banks and 0 for public sector banks; TA, dummy variable for size; CD, dummy variable for liquidity; NIM, dummy variable for management efficiency; NON, dummy variable for diversification; ROA, dummy variable for earnings level; NPA, dummy variable for asset quality; EA, dummy variable for capitalization; and PB, dummy variable for price-to-book valuation ratio; All the dummy variables take a value 1, when value is higher than the median value and 0 otherwise.

The outcomes further point out that public and private sector banks exhibit differences in their risk exposures, where public sector banks are exposed to credit risk (similar to Iannotta et al., 2007) while private sector banks are more vulnerable to interest rate risk and exchange rate risk. Public sector banks are considered to be relatively less risky because of the implicit government guarantee they possess. But the prevailing situation of directed lending, PSL, political considerations including farm loan waivers and objective of social welfare adversely affects the credit risk exposure of the public sector banks in comparison to private sector banks. Private sector banks undertake riskier activities guided by the profit maximizing objective, thus having higher interest rate and exchange rate risk exposures. The lower level of diversification and lower PB ratios (as a measure of fundamental distress) are the weaknesses at the bank level which increase the exchange rate risk exposure of private banks.

Other bank level characteristics do not have any significant explanatory power for banks’ risk exposures. The above results don’t change significantly with another measure of bank size (market capitalization instead of total assets) and profitability ratios (return on equity, ROE, in place of ROA).

The findings of the present study have important managerial and policy implications, where individual bank characteristics can provide additional insights for identifying risky and potentially systemically important institutions and thus contribute to the discussion of the effectiveness of the regulatory process and the future direction of reforms and policies for the Indian banking sector. From the policymaker’s perspectives, the empirical findings are pertinent as they can use readily available information on bank characteristics to detect an early risk exposure impending in the banking system on a timely basis for preventing massive losses, which is paramount when a crisis emerges. Bank managers can also take business decisions in the light of the effect they may have on the different risk exposures. Understanding the sources of a bank’s riskiness may also help to enhance the bank supervisory process within the Pillar 3 of market discipline in the Basel regulatory framework.

Summary and Conclusion

The issue of risk management in banks has been studied extensively in the financial institution’s literature. India’s financial sector reforms, adoption of Basel norms and increased focus on supervision and regulation of banking industry post the GFC have changed the business environment in India. Multifaceted time-varying nature of risks makes it more challenging for the banking sector to assess and manage the overall bank’s risk exposures accurately. The main aim of the study is to measure the bank’s risk exposures by the capital market-based approach and check for its consistency with the accounting-based approach. We also examine the time-varying nature of risk exposures in light of the GFC. The study also examines the power of the individual bank’s specific characteristics like size, ownership, diversification and valuation along with CAMEL rating factors to explain the estimated beta risk exposures in a panel data analysis framework. The above analysis has been done using bank-level data for 2004–2014, covering the GFC period, both for the Indian banking industry as an aggregate and for the bank, groups segregated according to bank-specific characteristics.

The empirical analysis reveals several interesting findings. Equity risk and credit risk have increased in the post-crisis period, while there has been a reduction in the interest rate and exchange rate risk exposures for the Indian banking industry. For bank groups, the results show that capital market believes small-sized, well-capitalized and well-diversified private sector banks to be less exposed to risk. The results also point out that size is an important factor impacting equity risk whereas ownership helps in identifying banks with significant credit, interest rate and exchange rate risk exposures. For instance, large banks have more market risk exposure; public sector banks have higher credit risk exposure and private sector banks have greater interest rate and exchange rate risk exposure. Private sector banks undertake riskier activities guided by the profit maximizing objective, thus having higher interest rate and exchange rate risk exposure. Additionally, the level of diversification and PB ratio are significant factors to be considered when looking at the exchange rate exposure of the banks.

The results of the study are pertinent for all the stakeholders in the banking industry as it augments the understanding of banks’ risk exposures—For bank managers to measure and manage risk, for regulators to ensure the stability and strength of the banking system by supervising the risk at individual bank level and aggregate level and for investors to value bank stocks by risk. The study highlights that static risk assessment may not be correct because the risk exposures are time varying in nature and heterogeneous across banks with different characteristics. Understanding the dynamic nature of risk exposures is a prerequisite for developing an effective and efficient risk management system, particularly in response to a crisis, and it shall aid in developing adequate safeguards to protect the Indian banking industry. The outcome of the study indicates that continuous market assessment of risk can be used besides employing the regulatory measures of risk to gather information at a higher frequency in a timely and cost-efficient manner, which would also help to reduce the increasing opacity of the banking sector. Private monitoring and supervision can enhance and support the regulatory efforts in developing an early warning model to take preemptive action when required. Furthermore, the marked differences in risk exposures across bank categories suggest that the regulators need to account for these characteristics instead of implementing a ‘one-size-fits-all’-type of regulation. The study provides evidence that readily observable bank characteristics contain valuable information about the banks’ risk exposures, which can help in early detection of risk and enhance the effectiveness of the regulatory process. The study has a bearing on the direction of future reforms and current regulatory policy debate on the structure of the Indian banking sector and the role of capital markets thereof. The results support the Basel III initiative of identifying Systemically Important Banks and requiring them to maintain a higher level of capital. The results of the study also provide valuable insights on the issue of risk measurement and management in banks, especially for emerging markets like India.

For reducing risk, banks should diversify and move towards generating income from non-traditional sources like fee and fund-based services to reduce risk. Specifically, public sector banks should focus on activity diversification which can help them earn revenue from investment banking, insurance, housing finance, securities brokerage and other non-traditional financial services. Improving the capitalization levels of banks by infusing additional capital also helps in reducing the bank’s risk exposures and gives more strength to banks to face a crisis. The study highlights the role of size in increasing equity price risk, thus warranting a need for reviewing the policy of consolidation in the Indian banking industry by acknowledging its repercussions on the risk profile of the banks.

While the present study focuses on India, a similar exercise can be undertaken to perform inter-country comparison across the emerging economies and vis-à-vis developed economies. Further research shall also be done by adding the newly identified risks by the Basel III-like reputational risk, regulatory risk and liquidity risk, operational risk and so on in this framework. Additionally, it is imperative to explore how different risks interact among each other and examine the reasons for the interaction among risks of the banks, which would help in creating a more proactive, efficient and integrated risk management approach to strengthening banks. This study would assist in adding depth and dimensions to the literature of banking risks, especially in the Indian context. We hope further studies in the area shall address these research issues.

Footnotes

Notes

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