Capital Adequacy and Credit Rating: Preliminary...

CAPITAL ADEQUACY AND CREDIT RATING: PRELIMINARY EVIDENCE FROM INDIAN PUBLIC SECTOR BANKS D.M.Nachane and Saibal Ghosh* (Department of Economics, University of Mumbai, Justice M.G.Ranade Bhavan, “Vidyanagari”, Vidyanagari Marg, Mumbai – 400 098). I. Introduction The Capital Adequacy Framework of the Basle Committee on Banking Supervision (BCBS) (1988) is widely regarded as the single most successful attempt in the move towards convergence of international standards in banking, enabling cross-country assessments and comparisons of internationally active banks. The results of a 1996 survey conducted by the BCBS indicated that 92 percent of the 140 participating countries had put in place a risk-weighted framework along the lines of the Basle approach (Musch, 1997). Despite being acknowledged as a valuable framework for comparing risks associated with assets, the approach has been the subject of some criticism. Under the framework, all corporate borrowers in the non-financial sector were risk weighted uniformly at 100 percent, despite wide divergences in the associated risks and all bank loans were risk-weighted at a uniform 20 percent, despite there being wide variation in the financial strengths of different banks. Again, the framework placed a more favourable risk weight on a weak bank than on a very strong non-banking company. This lack of risk differentiation has been cited as an important incentive for banks to enter into transactions, specifically with a view to arbitrage such anomalies and to shift to lower quality and higher risk assets in the same asset category. II. The New Capital Accord In order to address such anomalies, the BCBS proposed a revised Capital Adequacy Framework in 1999 (BIS, 1999) which uses a three pillar approach - (i) a standardised approach based on External Credit Assessments (ECA) and / or Internal Ratings Based (IRB) approach, which seeks to align more finely the risk weights with actual credit risks (ii) a supervisory review pillar to ensure that the bank's capital is aligned to its actual risk profile and (iii) a market discipline pillar to enhance the role of the other market participants in ensuring that appropriate capital is held by banks though higher disclosure requirements. The revised Accord is being widely discussed and debated and is expected to be operational by the year 2005. While the approach itself is certainly being viewed as an improvement over the simple standardised approach followed at present, it poses practical difficulties in implementation for both the banks and their supervisors in emerging economies, which may be deficient in the sophistication and the skills required for implementing such an approach as well as the background data, which is an essential prerequisite. There is no conclusive evidence as to whether the benefits of more efficiently allocating capital to risk will outweigh the costs of implementing the new Accord-either for the banks or for the supervisors in these economies. Further, the sophistication of the new Accord could well divert resources from supervision to capital regulation and monitoring, leading supervisors into a false sense of security that capital adequacy is an all-encompassing indicator of financial soundness, to the exclusion of other perhaps more relevant indicators. *

The views expressed in the paper are entirely personal and do not, in any way, reflect those of the institution to which the authors belong.

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THE INDIAN ECONOMIC JOURNAL

III. Received Literature The literature on whether capital ratios impact credit ratings is still in a state of infancy. The early studies with respect to US banks notably by Peltzman (1970), Mingo (1975) and Dietrich & James (1983) all examine whether either book or market capital in the 1960s and 1970s reflects regulatory influence. Most of these studies are generally in agreement about regulators' inability to influence changes in capital1. Thereafter, Keeley (1988) concluded favourably on the hypothesis that the December 1981 regulatory regime shift (U.S.) increased regulatory control of capital ratios. It is of note that none of these studies used the concept of credit rating in their analysis. Recently, Swindle (1995) attempted to separate the relative roles of the market and regulators using the CAMEL (Capital Adequacy, Asset Quality, Management, Earnings and Liquidity) rating of supervisors. His analysis suggests that banks with lower regulatory capital ratings have higher expected increases in their primary capital ratios and to that extent, represents an important advance on earlier studies in attempting to explicitly incorporate (supervisory) ratings in understanding the response of capital-sufficient (deficient) banks to regulatory pressure. However, supervisory ratings are not information in the public domain, and therefore, it is difficult to evaluate, a priori, the extent to which regulatory pressure (proxied by CAMEL) was significant in influencing bank capital ratios. Most of the studies referred to above employ some proxy for measuring regulatory pressure. In a significant departure, Kamin and von Kleist (1999) employed a linear mapping of ratings to risk with Aaa (of Moody’s) and AAA (of Standard and Poor’s) being assigned a value of 1, and the lowest value being 16 for B3 (of Moody’s) and B- (of Standard and Poor’s). Their analysis reveals that in cases where ratings are assigned by both Moody’s and Standard and Poor’s, they were identical for 58 per cent of the issues and differed by one notch for 36 per cent of the issues. Subsequently, Monfort and Mulder (2000) employed a dynamic error-correction model to discern the relationship between (sovereign) ratings and several indicators of crisis in 20 emerging market economies. Their analysis suggests modest efficiency gains by using sovereign credit ratings for capital requirements in lending to emerging markets. IV. New Capital Accord and Ratings The revised Accord places an explicit emphasis on rating. Risk differentiation between counter parties, be they sovereigns, banks, corporates, public sector enterprises or securities firms, will be either on the basis of external or internal ratings. Risk dispersion is sought to be achieved by ranging the possible risk weights from 20 per cent to 150 per cent, depending upon the rating of the counter-party, instead of the flat-rate 20 per cent (for banks) or an uniform 100 per cent (for others), as at present. The rating is to be either by an external rating agency or by the bank's own internal rating process. However, the reliance on external ratings agencies presents a problem, given the low penetration of these agencies in most developing economies. Leaving aside the issue of penetration, the fact remains that banks in most emerging markets have already invested substantial resources in the credit management function, and are thus relatively better placed than external rating agencies to evaluate proposals. Abrogating this function to the rating agencies might not yield the desired results. In India, even the vast majority of corporate borrowers are un-rated. Since such borrowers are given the benefit of a risk weight of 100 per cent, which is lower than that proposed for the lowest rated borrowers, there is no real incentive to seek ratings for this vast majority. For the lending banks, this would mean a status quo in risk weight at 100 per cent. However, such an incentive exists for those borrowers who could get a premium rating, as this would make claims on them entitled to

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a preferential risk weight of 20 per cent and correspondingly, provide them the leverage to negotiate a finer rate from the banks. The lender banks similarly would be able to discharge capital held against such loans at levels comparable to the present. An additional capital requirement could however arise for Indian banks (under the revised system) from the high NPA levels, for the un-provided portion of these assets could entail a risk weight of 150 per cent associated with the lowest quality credits, raising the Basle minima by an estimated 4 per cent on the capital to be allocated (Table 1). As far as claims on other banks go, two options have been under consideration, of which the first links the bank's rating to that of the sovereign in which it is incorporated. The option is unlikely to find favor, since location cannot be a true indicator of financial strength, a case in point being the Japanese banks. The more acceptable proposal is the second option, which proposes to assign risk weights from 20 per cent to 150 per cent depending on the rating, with un-rated banks being given the benefit of a lower weight of 50 per cent. Even if these banks continue to be un-rated, the 50 per cent risk weight on claims on them (up from 20 per cent as at present) would more than double the capital allocation required by them on this account. And, if the banks do get themselves rated, then it is very likely that several will receive ratings that qualify them for even higher risk weights. While external ratings Table 1: Proposed Risk Weights based on External Risk represent an important Assessment cornerstone of the New Sovereigns Banks Corporates Option 1 Option 2 Accord, as Monfort and AAA to AA0 20 20 20 Mulder (2000) observe, such A+ to A20 50 50* 100 overt reliance on external BBB+ to BBB50 100 50* 100 rating suffers from serious BB+ to B100 100 100* 100 Below B+ 150 150 150 150 limitations. For one, the Un-rated 100 100 50* 100 relationship between * Claims on banks of short-term maturity, e.g., less than 6 months would sovereign ratings and receive a weighting that is one category more favourable than usual risk weight on the bank’s claim. repayment risks is not well Option 1: Based on risk weighting of sovereign where bank is tested. This issue has come incorporated to the fore, especially in the Option 2: Based on assessment of the individual bank. aftermath of the Asian crisis, wherein the credibility of external rating agencies has been seriously called into question. Secondly, sovereign ratings could be pro-cyclical, although the professed aim of rating agencies is to be cycle-neutral and avoid unforeseeable changes in ratings. More importantly, the focus of rating agencies on default risk could limit their usefulness in the New Accord. The focus of capital requirements should, in contrast, be on covering the unexpected loss with a high probability (Jackson and Perraudin, 1999), i.e., to secure bank soundness and limit the likelihood of insolvency (Greenspan, 1998). Therefore, there appears to be a gap between the target of the New Accord (to provision against unexpected loss), and the instrument (ratings), which measures ‘default risk’2. Internal ratings by contrast, have several important advantages. Firstly, internal ratings potentially incorporate proprietary information on bank clients that is unavailable to the public at large and to rating agencies, if the borrower is unrated. The informational advantage of internal systems could help generate more accurate credit risk assessments of the borrower. Accurate assessments, in turn, could help to minimize the difference between regulatory and economic capital. In addition, the use of internal ratings places the responsibility of risk management squarely where it belongs viz. within each bank, a trend the New Accord intends to encourage.

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V. Empirical Estimation In the light of the aforesaid discussion, the purpose of the present study is to understand whether credit rating is expected to significantly impact the capital adequacy ratio of banks in India. Towards this end, we intend to examine whether credit rating behaviour affect bank’s capital decisions. Accordingly, we have selected banks that have been assigned short-term ratings by domestic rating agencies3,4. Since we cannot predict with certainty whether capital adequacy ratio would affect bank ratings, we estimate the probability that capital adequacy will impinge on ratings and hypothesize that this probability is a function of a vector of explanatory variables. The econometric approach used is the logit model, which is designed to identify the conditions under which one observes one or another set of (n+1) discrete outcomes (Greene, 1997). Such frameworks have been widely used in understanding the determinants of banking crises (Demirgic-Kunt and Detragiache, 1998). Formally, the model’s dependent variable is an indicator y that can take on values 0 and 1 that identifies two possible outcomes. The model can be given a random utility interpretation 'a la Nakosteen & Zimmer (1980). The explanatory variables X determine the ‘utility’ of each outcome according to the equation

U (alternative i ) = β 'i X + ψ i ; i = 1, 2

(1)

Here, X= X(k,t) is the vector of explanatory variables with k indexing banks (k=1…N) and t indexing time (t= 1…T) and ψ denotes the error term. These ‘utilities’ can be interpreted as the probabilities of observing the different outcomes, given the realization of the explanatory variables. Note that the model allows the parameters βi to differ across outcomes. For each observation, one obtains outcome i if it offers the maximum ‘utility’; in other words, U (alternativ e i) > U (alternativ e j) ∀ j ≠ i (2) One can interpret this approach as assuming that the realized outcome for each observation is that with the highest probability of occurrence under those conditions. As a normalisation, the parameters β0 for alternative i=0 are set to zero, and the logistic functional form is assumed, such that,

U (alternative i ) =

exp (β 'i X )

∑ j=0 exp (β 'j X) n

(3)

The model can then be estimated by a Maximum Likelihood procedure. Once the parameters are estimated, it is possible to calculate the probabilities of occurrence of each possible outcome, both within the sample and out-of-sample. For each observation, the ‘predicted’ outcome is the one with the highest conditional probability. Formally, let P(k, t) be the dummy variable that takes a value of one when the rating of bank k indicates highest safety, and zero, otherwise. β is a vector of n unknown coefficients and F(β β ’X(k,t)) is the cumulative probability distribution function evaluated at β ’X(k, t). Then the log likelihood function of the model is: (4) Ln L = {P ( k , t ) ln [ F (β ' X ( k , t )] + (1 − P ( k , t ) ln [1 − F ( β ' X ( k , t ))]}

∑ ∑

t =1 , 2 ,..., T k =1 .. N

When interpreting the regression results, it is important to note that the coefficients on the RHS reflect the effect of a change in an explanatory variable on ln[P(k, t)/(1-P(k, t)]. Therefore, the increase in the probability depends on the original probability and thus upon the initial values of the independent variables and their coefficients. While the sign of the coefficient does indicate the direction of change, the magnitude depends on the

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slope of the cumulative distribution function at β ’X(k, t). In other words, a change in the explanatory variable will have different effects on the probability of rating, depending on the bank’s initial rating status. The choice of explanatory variables is broadly conditioned by the CRAMEL (Capital Adequacy, Resources, Asset Quality, Management Evaluation, Earnings and Liquidity) approach. Therefore, the following variables have been used in understanding the determinants of ratings: non-performing assets (GNPA), net interest income (NIIWA), fee income (FIRWA), bank deposits (BDRWA), off-balance sheet activity (OBSRWA), profits (PFRWA), provisions (PVRWA) and the hundred-per cent riskweighted assets (HRRWA), with all the variables being scaled by total risk-weighted assets. While GNPA can be taken to proxy asset quality, profits and provisions act as a proxy for earnings. Bank deposits reflect a vulnerability to run on deposits and can be considered as a proxy for resources. The off-balance sheet item indicates the degree of financial sophistication, while the 100-per cent risk weighted assets variable reflects the riskiness of bank operations5. Our independent variable derives from the consideration of the short-term rating assigned to the public sector banks by a domestic credit rating agency (either CRISIL or ICRA). In such a case, we assign a dummy variable defined as: RATE_SHORT = 1, if the rating reflects highest safety within the category = 0, otherwise It needs to be mentioned here that we have only selected banks for which ratings are available for all the quarters under consideration i.e. from 1997:Q1 to 1999: Q4. This provides us with data on 18 banks that have been provided short/medium-term rating. The ratings data are obtained from CRISIL and ICRA6. VI. Analysis of the Results At the outset, it needs to be mentioned that it has not been the purpose of this exercise to Dependent Variable: assess the impact of the new Accord, especially RATE_SHORT since it is still in its early days. However, what is 7.64 Constant intended is to raise some issues based on (1.07) 0.66 impending capital regulation, which could be a Capital (t-1) (2.60)* pointer to future work in the area. The results -3.14 NIIRWA would therefore need to be interpreted with (-3.25)* 10.71 caution. FIRWA (3.78)* The results of the panel data model for the -0.14 BDRWA (-1.16) short-term ratings case is presented in Table 2. -0.03 As evident from the analysis, high GNPA is OBSRWA (-1.25) associated with a low rating, confirming the 0.79 PFRWA (1.64)$ widely held belief that non-performing asset is a 1.74 critical factor in determining a bank’s rating. And PVRWA (2.62)* importantly, higher the GNPA, the higher is the -0.06 HRRWA probability that a bank will receive a lower (-0.92) -0.34 rating. The coefficient on the GNPA is negative GNPA (-3.50)* 2 in the short-term case, and is statistically R =0.62 significant. Also, a rise in the 100-per cent riskNo. of observations 216 Fraction of Correct weighted assets appears to worsen bank rating, 0.92 Predictions although it is statistically insignificant. Log-likelihood -36.62 Unsurprisingly, profitability appears to play an Figures in brackets indicate t-ratios. *, ** and *** indicate significant at 1, 5 and 10 important role in determining short-term rating per cent, respectively. and is statistically significant. The provisions variable too, has the expected positive sign in the short-term (and is statistically Table 2: Determinants of Bank Ratings1997:Q1 to 1999:Q4 Short-term Variables

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significant). Intuitively, higher the provisions in the short-run, the better is a bank equipped to deal with adverse effects on their balance sheets. Higher net interest income does not necessarily imply a higher rating, possibly reflecting the perception that the bank is unable to diversify into non-fund activities. The primary focus of this exercise is to understand whether capital has a significant impact on rating. Towards this end, the analysis reveals that the short-run impact of capital on ratings might be significant and greater amount of capital increases the probability of obtaining a better rating. Clearly, our results are only a pointer, and a much more detailed analysis is called for before one can predict with a reasonable degree of certainty what bank-specific and economy-wide factors play an important role in determining bank ratings. VII. Concluding Observations With the Accord still in its early days, it might be too early to gauge the full impact of the New Accord on the Indian banking system. Some simple conclusions however suggest themselves. Claims on banks would overall attract higher risk weight, irrespective of whether they continue to remain unrated or obtain ratings, internal or external since the present ceiling of 20 per cent would now become a floor. With most corporates being unrated, there would be no major change in the overall risk weights on good quality assets, and there would even be lower risk weights for premium borrowers. However, net NPAs would attract the 150 per cent risk weight from the 100 per cent at present and hence require more capital to support them. And, if the second pillar of the Accord is implemented, then an add-on can be expected for some banks, though some of this could be met by the existing system-wise add-on of 1 per cent prescribed from the year 200001. Yet, overall the conclusion is inescapable that the new Accord would require net additional capital for the Indian banking system as a whole, though quantitative estimates of this requirement seem to be unavailable at this juncture. NOTES 1.

2.

3. 4.

5. 6.

Mingo (1975) is an exception. Yet, Dietrich and James (1983) show that Mingo’s findings of significant regulatory influence is a proxy for binding deposit rate ceilings, which led banks to increase capital to lure depositors. Apparently, rating agencies concentrate on default risk for sovereigns because they have difficulty capturing expected loss for sovereigns. This may reflect the general problem that defaults of sovereigns are infrequently observed and depend on the willingness to pay and not only on ability to pay. Short-term ratings as those assigned to Commercial Paper/CDs. In the Indian situation, given the lack of dispersion across ratings across PSBs, it does not seem very meaningful to use external ratings for determining their implications for capital adequacy standards of banks. The details are contained in Nachane et.al.(2000). The ratings data obtained from the two major domestic rating agencies, Credit Rating and Investment Services of India Ltd. (CRISIL) and Credit Rating Agency of India Ltd. (ICRA) are conformable in terms of their short/medium-term ratings assigned.

REFERENCES 1. 2. 3. 4. 5.

Bank for International Settlements (1999) A New Capital Adequacy Framework, June, Basle, Switzerland. Demirgic-Kunt, A and E.Detragiache (1998) Financial Liberalisation and Financial Fragility, Paper Presented at the Annual World Bank Conference on Development Economics, Washington D.C. Dietrich, J.K and C.James (1983) Regulation and the Determination of Bank Capital Charges, Journal of Finance, 38, 1651-1658. Government of India (1998) Report of the Committee on Banking Sector Reforms (Chairman: Shri M.Narasimham), Government of India, New Delhi. Greene, W.H. (1990) Econometric Analysis, Prentice Hall, New Jersey: USA

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8. 9. 10. 11. 12.

13. 14. 15.

16. 17. 18. 19.

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Greenspan, A (1998) The Role of Capital in Optimal Banking Supervision and Regulation, Federal Reserve Bank of New York Economic Policy Review, October, 163-168. Jackson, P. and W.Perraudin (1999) The Nature of Credit Risk: The Effect of Maturity, Type of Obligator, and Country of Domicile, Financial Stability Review, November, 122-150, Bank of England: London. Kamin,S.B and K.von Kleist (1999) The Evolution and Determinants of Emerging Market Credit Spreads in the 1990s, BIS Working Paper No. 68, Bank for International Settlements: Switzerland. Keeley, M (1988) Bank Capital Regulation in the 1980s: Effective or Ineffective, Federal Reserve Bank of San Fransisco Economic Review, Winter, 3-20. Kimball, D.J and C.James (1983) Regulation and the Determination of Bank Capital Changes, Journal of Finance, 38, 1651-58. Mingo, J (1975) Regulatory Influence on Bank Capital Investment, Journal of Finance, 30, 1111-21. Monfort, B and C.Mulder (2000) Using Credit Ratings for Capital Requirements on Lending to Emerging Market Economies: Possible Impact of a New Basle Accord, IMF Working Paper No. 69, IMF: Washington. Musch, F.C (1997) Applying Basle Standards in Developing and Transition Economies, in C.Enoch and J.H.Green (eds.) Banking Soundness and Monetary Policy, IMF, Washington, D.C. Nachane, D.M., (1999) Capital Adequacy Ratios: An Agnostic Viewpoint, Economic and Political Weekly, January Special No. on Money, Banking and Finance, 155-160. Nachane, D.M., A.Narain. S.Ghosh and S.Sahoo (2000) Capital Adequacy Requirements and the Behaviour of Commercial Banks in India: An Analytical and Empirical Study, DRG Study No. 22, Reserve Bank of India: Mumbai. Nakosteen, R. and M.Zimmer (1980) Migration and Income: The Question of Self-Selection, Southern Economic Journal, 46, 840-851. Peltzman, S (1970) Capital Investment in Commercial Banking and its Relationship to Portfolio Regulation, Journal of Political Economy, 78, 1-26. Reserve Bank of India (2000) Comments of the RBI on A New Capital Adequacy Framework, Reserve Bank of India: Mumbai. Swindle, C.S (1995) Using CAMEL Rating to Evaluate Regulator Effectiveness at Commercial Banks, Journal of Financial Services Research, 8, 123-141.