Managing Market Risk in Banks

Reserve Bank of Australia Bulletin December 1996 Managing Market Risk in Banks Analysis of banks’ risk exposures is In this way they provide a summary measure important both for management within banks of the risk exposure generated by a given and for bank supervisors. Two major sources portfolio. Draft guidelines1 released by the of risk for banks are credit risk (the risk that Reserve Bank in August 1996 give banks the loans will not be repaid) and market risk (the option (subject to supervisory approval) of risk of losses arising from adverse movements using VaR models to measure market risk on in market prices). This article focuses on the traded instruments in determining analysis and management of market risk, an appropriate regulatory capital charges. area that has received increasing attention VaR models can be developed to varying from managers and supervisors in recent years degrees of complexity. The simplest approach as banks’ financial trading activities have takes as its starting point estimates of the grown. The article is based on a series of sensitivity of each of the components of a seminars held in the first half of 1996 by the portfolio to small price changes (for example, Bank Supervision Department with a one basis point change in interest rates or a participants from the banking and finance one per cent change in exchange rates), then industry. assumes that market price movements follow a particular statistical distribution (usually the normal or log-normal distribution). This Measuring Risk in Trading simplifies the analysis by enabling a risk Portfolios: Value at Risk manager to use statistical theory to draw inferences about potential losses with a given degree of statistical confidence. For example Much of the debate in recent years on a given portfolio, it might be possible to concerning the management of market risk show that there is a 99 per cent probability within banks has focused on the that a loss over any one-week period will not appropriateness of so-called Value-at-Risk exceed, say, $1 million. (VaR) models. These models are designed to Elaborations to basic VaR models can allow estimate, for a given trading portfolio, the for correlations between different components maximum amount that a bank could lose over of a portfolio by modelling the extent to which a specific time period with a given probability. prices in different markets tend to move 1. The draft guidelines are closely modelled on the market risk proposals issued by the Basle Committee on Banking Supervision in January this year. They are due to be implemented in December 1997. 1 Managing Market Risk in Banks December 1996 together; in this way the method takes into In this regard the key assumption is probably account possible effects of portfolio that market prices are generated from a diversification. Still further elaborations normal distribution. In fact, there is strong permit the measurement of more difficult evidence that large changes in market prices aspects of risk such as the liquidity of the tend to occur more frequently than predicted instruments making up the portfolio. Here the by a normal distribution.2 For example, issue is the ease with which an institution can statistical theory does not tend to predict price liquidate or close risk positions. For some movements of the size seen in the 1987 share instruments (such as US or Australian market collapse or in the bond markets in government securities) large parcels can 1994. This violation of the statistical readily be sold at prevailing market prices. This assumptions is a potential source of might not be the case, however, for that part inaccuracy in parametric VaRs. In contrast, of the portfolio comprising relatively poorly the simulation approach is considered to be traded securities. Standard VaR methods take more accurate but is much more no direct account of this, although it can be computationally demanding. It requires indirectly taken into account by the choice of extensive daily calculation of simulated the portfolio holding period: the more illiquid portfolio values using daily market price the portfolio, the longer the holding period changes recorded over periods of a number that should be applied and, hence, the more of years. susceptible it will be to price changes. Leading Proponents of VaR approaches point to the international banks have begun to model these benefits of being able to summarise, in a single liquidity effects in more detail and incorporate figure, an estimated level of risk faced by an them directly into their VaR models, although institution from its trading activities. There is this work is still at a relatively early stage. no doubt that this characteristic makes VaR Closely related to the approaches described models a powerful management tool. The above (known as parametric VaRs) are those obvious qualification is that by its nature, such based on simulation of portfolios using a summary estimate does little more than historical price data. The main difference is provide a bank’s higher management with a that instead of using summary sensitivity guide to the size of potential losses and their measures, or relying on statistical theory to expected frequency in normal circumstances.3 enable inferences to be drawn about possible A comprehensive risk management approach price movements (as described above), the requires that these methods be supplemented simulation method takes a more direct by an effective stress-testing program, to approach. It takes a given portfolio, revalues examine methodically the potential impact of it directly at current and previous market extreme market events or scenarios. prices measured over a given time period, and Ultimately, it is these abnormally large price then takes the more extreme observations – movements that pose the greatest risks to the large simulated losses – as indicative of financial institutions, not those calculated by what theoretically could be lost on the the typical VaR model. portfolio. VaR methods have a number of Conceptually, the same or very similar shortcomings in dealing with large price results should be delivered by the two movements. It is recognised that the models approaches, as long as the underlying do not address all types of risk well (for assumptions of the parametric VaR are valid. example, risk associated with options where 2. In the risk literature, this is referred to as the problem of fat-tailed distributions. That is, there tends to be a larger number of extreme observations at the tails of the distributions than is implied by the statistical theory of normal distributions. 3. On average, for example, risk estimates based on a 95 per cent confidence interval will be exceeded once every 20 trading days. Using a 99 per cent confidence interval reduces the uncertainty but still suggests that estimates of risk will be exceeded on average 2 or 3 times a year (assuming a normal distribution). 2 Reserve Bank of Australia Bulletin December 1996 the relationship between an underlying asset price and the associated option price is not Non-Traded Interest linear). Most users of VaR models also Rate Risk recognise that reliance on estimated correlations across products and markets, while producing theoretically more accurate A second and often larger source of market measures of risk, requires that those risk for banks is non-traded interest rate risk. relationships between prices and markets This source of risk is a direct consequence of remain stable, even at times of market banks’ role as intermediaries. Banks carry a disruption. Historical evidence suggests that wide mix of both fixed-rate and floating-rate this might not always be the case. There is a assets and liabilities on their books, many of strong view that, for stress-testing purposes which are subject to repricing when interest at least, it may be desirable to assume that all rates change. For example, a balance-sheet correlations break down in order to calculate structure with predominantly short-term risk estimates under worst-case assumptions. liabilities and long-term fixed-rate assets These problems lead many institutions to would be subject to losses when interest rates rely on scenario-based approaches, where rise; a balance sheet with the reverse portfolios are routinely subjected to a wide configuration would incur losses when rates range of hypothetical price and volatility fall. movements. Advocates of this approach tend The asset and liability management process to downplay the benefits of a single VaR which takes place within banks is, in part, estimate, arguing that it obscures the potential about the determination of the interest rate impact that different configurations of prices sensitivity of the balance sheet and the might have on a portfolio. implementation of risk management practices Finally, it is recognised that any risk to hedge the potential effects of interest-rate management system must be understood by, changes. This is a quite separate matter from and consistent with, the activities of the risk the analysis of any credit risk on the balance takers themselves – those on the dealing desks. sheet (the risk that counterparties may Effective risk management systems are not default). The increasing complexity of bank solely about restricting risks taken by trading products, and especially the degree of staff (though that is obviously important). optionality being introduced into retail and They need also to be behaviour-altering in the wholesale products has heightened the

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