BENCHMARKING OF ECONOMIC AND REGULATORY CAPITAL FOR INTERNATIONAL BANKING SYSTEMS: A THEORETICAL DISCUSSION John L Simpson *1 John Evans **2 Abstract There is no suggestion in the title of this paper that the Bank for International Settlements or any other international body starts formally regulating entire country-banking systems. However, the idea behind this paper is that by benchmarking economic and regulatory capital for international banking systems, regulators will be able to arrive at more realistic levels of capital adequacy for banks within these systems. The rationale behind the idea is that economic risk of countries is closely related to financial risk of their banking systems. This paper presents an alternative approach to dealing with these issues and constructs a model for analysing the performance of different country-banking systems. The paper proposes the use solely of stock market-generated data for the implementation of the model. JEL Classification: G29 Key Words: Country-banking system, systemic risk, Systematic Risk, interdependence, Regulatory Capital, Economic Capital, performance indicator.
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* University of Wollongong, Dubai Campus, P O Box 20183, Dubai, UAE
[email protected] ** University of Wollongong, Dubai Campus, P O Box 20183, Dubai, UAE
[email protected]
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John Simpson is the first author and is a member of the academic staff at Curtin University in Western Australia, currently seconded to the University of Wollongong, Dubai Campus. 2
John Evans is currently the Dean of The University of Wollongong, Dubai Campus and is a corresponding author.
Introduction Most financial economists, professional bankers and banking regulators agree or at least accept that banks, because of their uniqueness and economic importance, require a degree of regulation. However, if regulation is too stringent, banking business incentive is destroyed. If too lenient, financial system safety, both domestic and international, is threatened. The questions remain as to the degree of regulation; how should regulation be implemented; and how can the regulation be applied fairly and evenly among banks with different performance profiles. Banking risk management today, according to Bessis (2002), has involved the conceptualisation of banking risks, such as market risk (MR)3, liquidity risk, credit risk and operational risk. It is appropriate for commercial banks, for the benefit of their own management processes and operations, to closely evaluate, implement and monitor risk measures, and risk models such as Value at Risk (VaR) and Earnings at Risk. There are many issues involved in the measurement of MR (Dowd, 2002). Should historical simulation or parametric VaR be used? If the latter, do we estimate with normally distributed profits and losses, or with normally distributed arithmetic means. Do we estimate with lognormal VaR? How do we estimate expected tail loss? Bankers also need to consider asset-liability management, liquidity gaps, and the term structure of interest rates, interest rate gaps, internal hedging and financial derivatives usage. In addition they need to examine funds transfer pricing, credit derivatives, portfolio risk, capital management and loan portfolio models. Clearly these are technical issues that require technical knowledge, expertise and time if the analysis is to be at all worthwhile. A banker’s view may well be that the regulations and guidelines evolving from the initial Basel Accord in the late 1980’s are complex and cumbersome when it comes down to compliance, even though much of the work in compliance assists banks in their own risk management practices. Bankers may feel that imposed measures have been the result of massive over-analysis by regulators and financial economists. Bank practitioners may at times doubt whether the regulations are applied in a fair and even-handed manner bearing in mind their particular bank’s performance and risk/return profile. They would also be concerned about the interest opportunity costs of higher levels of regulatory capital (RC) that threaten their underlying objectives of shareholder wealth maximisation. Bankers may also argue that there has been few bank burials over the past two decades, so why increase regulation and its associated costs? Of course a balanced view is required in this most important of financial and economic issues. Banks do require a degree of regulation. Banks are unique and it can certainly be argued that the economic health of a country depends on the financial health of its banking system. Commercial banks are agents of central banks and, whether they want to or not, they are indirectly involved in the implementation of monetary and exchange rate policies by their very definition as lenders of funds and traders of foreign exchange. They 3 See systematic risk (SR) in the course of the paper. According to theory (Markowitz, 1952) market risk (MR) and systematic risk (SR) are synonymous.
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essentially borrow from the public and business unsecured at comparatively lower costs of funds than other financial intermediaries and corporate entities. They have, by definition, high debt to equity ratios. They do borrow short to lend long, treating core demand deposits as a long-term source of funds (Greenbaum & Thakor, 1995). This is fine if the bank is perceived as being safe. This implies the existence of a social contract. The perception of bank safety emanates in part from the level of RC required. This safety valve is needed in the event of a bank run; or the withdrawal of interbank lines of credit; or the losses that may arise due to poor credit risk management and assessment resulting in unsustainable levels of nonperforming loans. Taxpayer funded bailouts of troubled banks are not digested well by governments or the public. Moreover, the moral hazard dilemma has proven to be a problem for banking systems where risk-taking banks write riskier business to improve expected returns. Shareholders need comfort in the face of higher regulatory costs. Banks of course are well aware a government bailout is available if they fail due to high bad and doubtful debt levels. Bank regulators rightly focus on market risk (BIS, 2001), but have ignored the performance of banks in achieving optimal risk/return profiles. Regulators have also largely ignored the systemic risk (note below that systemic risk and systematic risk differ)4 arising out of the interdependence of banks within and outside of systems. Herein lies an obvious threat of financial contagion. The evidence is can be found in the cointegration of interbank offered rates on interbank lines of credit between the major systems centred in New York, London and Tokyo (Simpson, Evans & De Mello, 2002). Such significant cointegration essentially amounts to strong interrelationship and interdependency in funding loan assets, which in turn amounts to higher systemic risk. A recent view of the regulators is that VaR models used to calculate RC, either formulated by the central bank or by the banks themselves with central bank approval, need to incorporate operational risk. This risk largely arises out of bad luck or bad management. The question remains as to how accidents or “acts of god” or management incompetence can be modelled. It is put that this could create more complexity in an already complicated framework. This paper takes the view that operational risk should be accepted as a normal business risk for any corporation and should not require the holding of additional Tier One or Tier Two Capital. In addition, a less complex and fairer regulatory system would provide a disincentive for the practice of regulatory capital arbitrage by banks within systems. If the answer to all of these problems is the implementation of management and market discipline (BIS, 2001), exactly how will these disciplines be implemented and monitored by the regulators and at what cost and benefits to the banks and the taxpayer?
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For reasons fully explained during the course of the paper systemic risk is also the risk of contagion in financial markets and arises out of interdependence or strong interrelationships between banking systems and banks within financial systems. It differs from systematic risk for that reason. Sell (2001) discussed the common factors in relation to financial crises in Brazil and Russia. These seemingly unrelated economies had in common large USD debt from Western Banks. Oort (1990) also discussed the interdependence of Western banking in interbank lines of credit.
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The notion of an appropriate theoretical level of a regulatory capital (that is, a benchmark) for a banking system is merely a starting point for improving the formal regulation of banks within that system. The basis for this argument lies in the very strong relationship between the economic riskiness of countries and the financial riskiness of banks within those countries. Risk ratings agencies such as Standard and Poor’s and Moody’s provide country risk and international banking risk scores based on economic and financial data. There is a significantly high positive correlation between the risk scores of countries and the international banking risk scores of banks within those countries (Simpson, 2002) 5. This paper is motivated by a desire to simplify a complex framework that has been lacking in its historical “one size fits all” approach to regulation. The central issues addressed in this paper are: How can economic capital (EC) for a system be reasonably and easily calculated on a daily, weekly or monthly basis? How can regulators clearly gauge the performance characteristics of their banking systems? How can a system’s EC be adjusted according to the risk/return profile and degree of interdependence of its banking and financial system with other systems? Will that adjustment and resultant systemic benchmark be useful to regulators at an international level? Can such a systemic model then be adapted to ascertain fair levels of RC for banks within a financial system? Can examining stock market generated data in various country-bank systems solve all or many of these problems? To expand these issues it is necessary to appreciate that it is a system, rather than a bank within a system, that is the starting point for analysis. Systemic capital adequacy would mean that a banking system or rather all banks within that system are holding a percentage of their loan assets in prime liquid assets such as deposits with the central bank or government securities with a short-time to maturity. Thus the underlying assumption must be that all banks within a system and internationally between systems are accurately valuing and thus risk weighting their loan and other assets in a conservative and uniform way. A Systemic Earnings at Risk Model In order to start to model our benchmark for systemic regulatory capital (RC) we need to capture in our analysis the dispersion (volatility) of banking stock returns in a system over time. We can therefore use volatility as the unit for measuring systemic economic capital (EC). We can investigate the effect on the banking system of market risk (MR). Any investigation could, for example, focus on the key Eurobanking markets, which are centred in London, New York and Tokyo. We could therefore examine the banking stock returns indexes in each of those systems as well as the market return indexes connected to the FTSE, the Dow Jones and the Nikkei Dow stock market indices. The distribution of
5 Simpson (2002) provided a model of risk score replication of the major risk ratings agencies for country risk as well as international banking risk and in doing so found that economic forces were the principal drivers of international banking risk scores by the major risk ratings organisations.
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banking stock index returns can be taken as a proxy for market random earnings over a short time period. As noted earlier, the effect of credit risk of the banking system is not taken into account in this model because it is assumed that the distribution of earnings for the system will be highly asymmetrical as is the case with earnings affected by credit risk in individual banks within the system. In any case, credit risk could be assumed to be part of the normal business risks of banking as is operational risk and just as this is the case for other unregulated corporate entities. Should we not also acknowledge that banks have over the centuries evolved superior credit risk assessment and credit risk management skills in their prime function of lending money and that they do possess a comparative advantage in terms narrowing the borrower/lender informational asymmetric gap? Figure 1 indicates the area under the normal distribution of system returns beyond the boundary on the left and this represents the probability that the loss in systemic returns will exceed that boundary value. Figure 1 Normal Distribution of a System’s Banking Stock Index Returns
Source: Adapted from Bessis (2002).
Higher volatility of expected returns of banking systems means that the curve distribution around the mean will be wider and thus the chances that losses in systemic returns will exceed the given boundary value will be greater. The confidence intervals of the distribution are probabilities that the systemic losses will exceed the upper bound (that is negative systemic returns or earnings beyond the zero level). The confidence intervals are
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one tailed because only one-sided negative deviations materialise downside systemic earnings risk. Figure 2 further explains the approach by depicting confidence intervals for the distribution of returns. Figure 2 Confidence Intervals of the Distribution of a System’s Banking Stock Index Returns
Source: Adapted from Bessis (2002).
Confidence intervals, assuming a normal distribution, are simply multiples of the downside volatility of the systemic earnings distribution. The upper bounds of negative deviations corresponding to the confidence intervals correspond to deviations from the mean of the distribution. For example, confidence intervals of 10%, 5% and 1% correspond to deviations from the mean of 1.28, 1.65 and 2.33 times the standard deviation of the distribution. The characteristic of loss distributions is that they have “fat tails” which are the extreme sections of the distributions. The fat tails indicate that large systemic losses, although unlikely because their probabilities remain low, still have some likelihood of occurrence that is not negligible. The fatness of the tail refers to the non-zero probabilities over the long end of the distributions. The essence of a systemic earnings at risk model is that EC can be taken as the amount that losses of earnings in a system are unlikely to exceed. The problem here is that a uniform confidence level guideline for all systems needs to be set by regulatory authorities so that comparisons are meaningful.
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The Credibility of a Systemic Earnings at Risk Model As discussed above, when implementing techniques based on confidence levels there is a need for a benchmark for the banking systems involved. When there is a tight confidence interval the earnings at risk (or the upper bound of systemic losses of earnings) could be so high that banking transactions in a system if it was internationally regulated would soon become limited by authorisations or not feasible at all. If globally competing systems use different confidence intervals in the same model such internationally regulated systems would not operate on equal terms. Tighter confidence intervals will reduce the volume of transactions in that system relative to another system and reduce the volume of business. Other systems would have comparative advantage. Regulators in a system must be aware of this loss of international banking competitiveness as much as they are aware of the need for adequate RC for “safety valve” reasons for banks within systems. It should be noted that using RC as a surrogate for EC generates distortions because of the divergence between real risks and the forfeited risks of RC. The forfeited risks are less risk sensitive than economic measures. Diversification effects of systems that have a high component of ownership and representation in other international banking systems are ignored. Measuring EC means that the same measure of risk is used for widely globally diversified systems as for highly domestically concentrated systems. The most important benefit of systemic EC is to correct these distortions. The drawbacks of earnings at risk models relate to the fact that trends of time series can themselves increase volatility if the series is time dependent. The volatility will come from the trend itself rather than from instability in the systemic earnings. Bessis (2002) feels that earnings at risk models capture risks as an outcome of all risks (eg MR, credit risk, liquidity risk, operations risk). It does not capture risks at their source. Without links to all of the sources of risk earnings at risk will serve to define aggregated EC but will not involve the tracing back of all risks to their source. In this paper the approach taken makes the assumption that there is a degree of informational efficiency that factors in normal business risks and the share prices reflect this information very quickly. It is put that the capturing of all risks is the problem of over-regulation and complexity with current and proposed regulation. It has been stated earlier that some risks such as operational and credit risks need to accepted as being part of banking business and should not require coverage in terms of capital adequacy. Moreover, the major risks faced by a country-banking system as well as banks within that system are MRs and systemic risks due to interdependence of banking systems and banks within systems. These are the risks that require capital adequacy coverage. Earnings at risk models make it a relatively easy task to track the major variations in earnings and to interpret them. There is simplicity in the historical data bases used and in the information technology used to store and analyse the data. In addition MR and the effect of the volatilities of historical market prices for loans, currencies and commodities on earnings can be factored in to an expanded model.
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Because normal distributions are assumed either actual or Monte Carlo simulated data can quite easily be generated to investigate worst-case scenarios. The Model, Methodology and Proposed Data After providing the machinery to estimate a system’s benchmark EC, the proposed model in this paper takes into account the performance of and the correlation between the performances of different systems. As mentioned above, in finance theory, normal business risks from the market are assumed to be immediately reflected in the current banking share price index (BSI) and the overall stock market index (SMI). This is in accordance with the efficient market hypothesis originally put forward by Fama (1976). Again it needs to be noted, that there is a difference between systematic risk (SR) and systemic risk. The SR is also market risk (MR) referred to in portfolio theory by Markowitz (1952). This component of total risk is unavoidable and thus undiversifiable and is measured by a Beta, which originally described the strength of the relationship between the returns of a security and the returns of the overall market. It must also be remembered that a lower value of Beta (say less than one) for a banking system does not always mean low total riskiness of that system. The Beta captures the market risk of the banking system. The other component of total risk in portfolio theory is unsystematic risk or, in the case of our example, banking system specific or banking system idiosyncratic risk. Systemic risk is the risk referred to by Oort (1990) that arises out of the interrelationship of banking systems in the their interbank borrowing and lending. Systemic risk is the risk of a “domino effect” collapse of banks following the failure of one major interdependent international bank. It could lead to widespread domestic and international financial instability. Although systems in the United Kingdom, United States and Japan are developed and sophisticated and account for the majority of international banking business they are also highly interdependent through their interbank lines of credit. An illustrative example can quite easily be undertaken using simulated stock market data. Daily, weekly or monthly data on banking stock indexes (BSI) and overall SMI in a system can be used when the model is applied in the future. Using entirely market generated data in the returns of the banking index and the risk free rate applicable in that system as well as the Beta for that system a banking system performance indicator (adapted from the performance index for securities by Treynor, 1965) 6 is formulated. It describes a system’s level of SR, return performance and also takes into account the market capitalisation weighted correlation of returns and risks with other related systems. Models for a single banking system, two interacting banking systems and more than two interacting systems can be developed. The simulated data can be embodied in a final 6 Treynor’s performance index was applied to securities and used a risky asset’s Beta as the risk index rather than it’s standard deviation as used by Sharpe (1966) who used its standard deviation. The security’s Beta is the covariance of a security return and the market portfolio return divided by the variance of the market portfolio. The numerator was the difference between the security’s return and the risk free rate.
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number to be used as an indicator of the overall health of a banking system and comparison can be made with the financial health of other systems. In addition the level of EC for a banking system can be modified by the banking system performance indicator 7 (BSPI) to arrive at a systemic benchmark level of capital adequacy. The development of the model is described as follows: a. Systemic EC Reference is made to the above section on systemic earnings at risk. The first step is to calculate this level for each system by applying confidence intervals to a distribution of BSI returns. See the model in Figures 1 and 2 and simulated/hypothetical data in Table 1. b. MR for a Single System (i) We analyse market models for each of the banking systems that are being compared, to arrive at respective Betas (measures of SR for each system). Ri =
Pit − Pit −1 Pit −1
…………………………………………………………………1)
Where: Ri is the (monthly) return in the banking index for country-bank ( i) .
Pit , Pit −1 are the values of the bank index at (months) t and t − 1. Rmi =
Pimt − Pimt −1 Pimt −1
………………………………………………………………2)
Rmi is the (monthly) return on the SMI for country-bank (i ) Pimt , Pimt −1 are the values of the SMI in country-bank ( i ) at (months) t and t − 1. Ri = α i + β i ( Rm i ) + ei …………………………………………………………3)
α i is regression intercept for market model for system ( i) . β i is the regression coefficient or measure of MR for system ( i ) where β i 〉 0 . ei is the residual or error term for system ( i) .
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Hereafter to be called the Banking System Performance Indicator or the BSPI denoted BI in the related formulae.
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A positive Beta here is assumed.8 The banking system Beta will measure how closely the banking industry has followed the ups and downs of the stock market on average over long periods of time. It is calculated using past performance 9. c. The Banking System Performance Indicator (BSPI) in a Single System (i)
As adapted from Treynor (1965) the BSPI for a single system is as follows10: Ri − R f i BI i = βi Where:
………………………………………………………………..4)
BI i is the BSPI for a system ( i ). Ri is the return for banking system index for country ( i ). R f i is the (monthly) risk free rate for country ( i ).
β i is the MR for system ( i ) where β i 〉 0. d. The BSPI for a Two-System Model BI i j = BI i /( X ij ρBI i j ) …………………………………………………………5)
Where: BI i j is a BSPI for a two-system model for system (i) relative to system (j).
BI i is the single BSPI for system (i).
ρBI i j is the correlation between the returns and SR of systems ( i ) and ( j ). X ij is the ratio of the market capitalisation of banking system (i) to that of systems
(i) plus (j). 8
This is because the higher the market or SR the greater the expected returns. In our analysis the Beta provides a measure of how risky a banking system is in relation to the overall market. Recall we are comparing a banking stock price index in a country-banking system to an overall stock market index in the same country. 9 Examples of Betas follow: A banking system with a Beta of 1.0 has tended to closely follow the overall stock market in that system over long periods of time. If the Beta is greater than one it will mean, in this analysis, that the banking system is riskier than the system’s overall market. When the Beta is less than one the Banking system is less risky than the overall market for that system. 10
See footnote on Treynor’s index page 8.
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Note: a. Higher values of BI i j reflect higher returns, less SR and lower interdependence (systemic risk) for system (i) relative to system (j). b. X i is introduced for the purpose of weighting the correlation between the performances of the systems. The weighting is based on banking industry market capitalisations. e. A Generalised Model
This is equal to the BSPI for system (i) divided by the average of the weighted correlations between indicator (i) and the performance indices of “1-n” systems. BI i1− n =
BI i
[( X
i1
ρBI i1 + X i ρBI i 2 + X i ρBI i 3 )....... X i ρBI in ) / n] …………6) 2
3
n
Where: BI i1− n is the BSPI for system (i) interacting with “1-n” systems. X i1− n is a weighting. It is the ratio of market capitalisation of banking system (i) over the market capitalisation of (i) plus systems “1-n”.
ρBI i1 , ρBI i 2 , ρBI i 3 ...ρBI in are correlations of BSPIs for system (i) against those for systems “1-n”. Discussion
Assume in a two country-banking system model that the end concern is with the calculation of regulatory capital for one of those systems. The starting point is the calculation, from market-generated data, the economic capital for each system. Then using a market model the betas and a BSPI for each system are calculated. The EC for any country-banking system is a worst-case scenario for that system. In the model presented, that benchmark will need to be adjusted down according to better systemic performance. In a regulated international environment the downward adjustment would assist as an indicator for preserving banking system incentive and competitiveness while at the same time it would provide an indication of systemic safety. When the model is expanded to contain more than two country-banking systems, average weighted correlation coefficients are considered and a system with lower average weighted correlation coefficients will receive an even higher downward adjustment of economic capital, because of lower interdependence of that system with the other systems. That is, there will be less systemic risk for the system being measured. It may be that that the system described must still maintain higher levels of regulatory capital because of the higher than normal worst-case scenario losses in earnings.
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Conclusion
It is repeated that the notion of formal international regulation of entire banking systems is not being suggested. Nevertheless, it is put that the establishment of guidelines or benchmarks for levels of theoretical regulatory capital of country-banking systems at an appropriate level is just as important as the regulation of banks within financial systems. Countries still need to concern themselves with the international competitiveness of their own systems in rapidly globalising and competitive financial markets. An overregulated system in terms of excessive costs of regulatory capital would make that system less competitive with others. This would do nothing for that country’s balance of payments position. Whilst the proposed models makes the somewhat restrictive assumption that all country markets are at least semi-strong form efficient, the position of this paper is that guidelines or benchmarks for systemic regulatory capital shed some light on what may be a fairer system for the regulation of banks within systems. Market risks only should be used to decide on capital adequacy levels, as operational and credit risks are normal business risks that are also faced by unregulated corporations. Systemic benchmarks create a useful starting point for analysis and a similar model to the systemic model can be applied at a later point using individual banking stock prices and returns. At a level where banks within a system are being considered, review of regulation may need to be combined with some form of imposed market discipline. Such discipline needs to be carefully defined and costed if it means the greater use of derivatives instruments to manage risks such as credit risk and operational risk. The preservation of banking business incentive as well as systemic safety is the ultimate goal.
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References
Bessis, J. 2002, “Risk Management in Banking”, Second Edition, Wiley. BIS, 2002, “The New Basel Capital Accord”, Basel Committee on Banking Supervision, Consultative Document, Bank for International Settlements, January. Dowd, K., 2002, Measuring MR, Wiley, pp. 37-46 Fama, E F., 1976, “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of Financial Economics, Volume 3, Number 4, pp.361-377. Greenbaum, S. I., and Thakor, A. V., 1995, Contemporary Financial Intermediation, The Dryden Press, Orlando, pp 97-131. Markowitz, H. M., 1952, “Portfolio Selection”, Journal of Finance, 6, March. Oort, C. J., 1990, “banks and the Stability of the International Financial System”, De Economist, 138, n 4. Sell, F., 2001, Contagion in Financial Markets, Edward Elgar. Sharpe, W. F. 1966, “Mutual Fund Performance”, Journal of Business, January. Simpson, J. L., Evans, J., Di Mello, L., 2002, “Systemic Risk in the Major Eurobanking Markets: Evidence from Inter-Bank Offered Rates”, Curtin University, School of Economics and Finance, Working Paper Series, 2002. Simpson, J. L., 2002, “An Empirical Economic Development Based Model of International Banking Risk and Risk Scoring” Review of Development Economics, Volume 6, Issue 1, February. Treynor, J., 1965, “How to Rate Management Investment Funds”, Harvard Business Review, Jan. to Feb.
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