Credit portfolio management

The initiative of Credit risk management techniques was taken by the banking industry’s desire to avoid early default experiences of the banks. The poorly controlled rush to build market share at the expense of asset quality and portfolio diversification, threatened the solvency of even well capitalised institutions and led to the heavy credit losses during this period. The need to better understand portfolio credit risks was reinforced by the publication of the Bank for International Settlements’ (BIS) capital adequacy guidelines in 1988. These guidelines specify minimum regulatory capital requirements, were inadequate to provide an accurate measure of the risk/reward characteristics of a credit portfolio. Banks therefore started to develop more sophisticated credit risk management techniques that recognised both the credit risk of individual exposures and the degree to which these risks were diversified. Banks leading the development of credit risk management techniques quickly discovered that credit pricing was highly inefficient. Typically pricing within a loan portfolio would be almost flat across the credit risk spectrum, generating huge skews in customer profitability. Initial efforts focused on mitigating these skews by calculating risk adjusted profitability (eg risk adjusted return on [risk-adjusted] capital) by sub-portfolio and then using these measures to create risk adjusted loan pricing tools. Leading banks thus started to rationalise pricing in both loan and bond portfolios, and moving under-performing assets off their balance sheets. Consequently banks that had not developed risk-adjusted performance measures started to suffer from negative selection, often accepting significantly underpriced assets from more sophisticated institutions. In parallel to developing aggregate risk-adjusted performance measures, leading banks were also starting to quantify credit risk at finer levels of detail. Credit portfolio models were developed which could differentiate credit risk along multiple dimensions (credit grade, industry, country/region etc) and, for large corporate exposures, on a name-by-name basis. These credit portfolio models have positioned leading institutions to take advantage of the increasing liquidity of the credit markets and to adopt a far more active approach to credit portfolio management than was previously possible. Historically, credit portfolio management had focused on the monitoring of exposure by broad portfolio segment and, if necessary, the imposition of exposure caps. The creation of a stand-alone credit portfolio management function, armed with sophisticated portfolio models and with a controlling mandate over assets held on the balance sheet, now enabled the credit portfolio to be optimised independent of origination activity. Active credit portfolio optimisation has enormous potential to enhance profitability. Using only very basic optimisation techniques a typical institution might expect to reduce the economic capital consumed by its credit portfolioby 25%–30%. Credit risk measurement framework. Credit risk is conventionally defined using the concepts of expected loss (EL) and unexpected loss (UL). Because expected losses can be anticipated, they should be regarded as a cost of doing business and not as a financial risk. Obviously credit losses are not constant across the economic cycle, there being substantial volatility (unexpected loss) about the level of expected loss. It is this volatility that credit portfolio models are designed to quantify. Volatility of portfolio losses is driven by two factors – concentration and correlation. Concentration describes the ‘lumpiness’ of the credit portfolio (eg why it is more risky to lend £10m to 10 companies than to lend £0.1m to 1,000 companies). Correlation describes the sensitivity of the portfolio to changes in underlying macro-economic factors (eg why it is more risky to lend to very cyclical industries such as property development). In all but the smallest credit portfolios, correlation effects will dominate. When quantifying credit risk, two alternative approaches can be used when valuing the portfolio: Loss-based method. Under this approach an exposure is assumed to be held to maturity. The exposure is therefore either repaid at par or defaults, and thus worth the recovery value of any collateral. Using this approach credit migration has no effect on the book value of the obligation. NPV-based method. Under this approach, the embedded value of an exposure is assumed to be realisable. If the obligation upgrades then it is assumed to be worth more than par, and if it downgrades it is assumed to be worth less than par. The value of the obligation can be calculated using either using market credit spreads (where applicable) or by marking-to-model using CAPM or similar method. In general, NPV-based methods are most applicable to bond portfolios and large corporate portfolios where meaningful markets exist for either the physical assets or credit derivatives. For the vast majority of commercial bank exposures, where such markets do not exist a more meaningful risk profile is obtained using a loss-based method. Loss-based calculations have the advantage of requiring less input data (margin and maturity information, for example, is not required) and being simpler to compute. However, many institutions are starting to run both methods in parallel, particularly for portfolios where securitisation is possible. The different credit risk profiles generated for the same portfolio using loss-based and NPV-based methods are shown later in this article. Measuring credit risk correlation: As discussed previously, to accurately model portfolio credit risk the correlation between exposures must first be measured. This seemingly simple statement conceals the complex string of calculations that are actually necessary. Complexity arises as it is extremely difficult to calculate credit risk correlations directly. Indeed, to measure default correlation (as required for loss-based measures) between two companies is impossible, as this would require repeated observations over a given time-period during which each company would either default or survive. Credit risk correlation could then be calculated from the number of times both companies defaulted simultaneously. Clearly such analysis is impossible in practice. Similar difficulties exist when trying to estimate correlation between changes in credit rating or bond spreads. The simplest solution is to use aggregate time series to infer credit risk correlation. Unfortunately this approach is unsuitable except for the most basic of portfolio analysis for two main reasons. Firstly, aggregate time series are usually available only at a very high level, with insufficient data on underlying credit risk rating, industry and geographic distribution of the portfolio. Secondly, using aggregate time series produces unstable results over time. A more attractive solution to calculating credit risk correlation is to use a causative default model that takes more observable financial quantities as inputs, and then transforms them into a default probability. The most widely used model for commercial lending portfolios being the Merton default model. The Merton model assumes that a firm will default if, over a 12- month period, the market value of assets falls below the value of callable liabilities. This enables asset correlation to be transformed into credit risk correlation. The more correlated the movements in the two companies’ assets the greater the ‘twist’ in the joint asset value distribution. Hence the greater the probability that the credit quality of the two firms will rise, fall and ultimately default together. Asset correlations have the benefit of being more easily observable (from equity prices, balance sheet analysis etc) and their correlations have been shown to be stable over time. The Merton model has also been successfully adapted to describe credit risk correlations in financial institution portfolios that contain corporate exposures. The correlation of model inputs themselves are best measured using factor models in the same way that an equity ‘beta’ is estimated. Factor models usually produce better prospective correlation estimates than direct observation and have the additional benefit, if macro-economic factors are chosen, of enabling intuitive stress testing and scenario analysis of the credit portfolio. The ‘connection’ of credit risk to underlying macroeconomic risk factors has significant implications for credit risk management and the future development of credit markets. Not only could a credit portfolio manager potentially hedge credit risk via equity or ‘macro-economic’ derivatives, but professional market- makers should ensure that credit, equity and other derivative desks are positioned to take advantage of resulting arbitrage opportunities. These developments are likely to be a major driver of liquidity as these markets develop. A positive factor weight indicates that a positive change in that factor produces an increase in asset value, with a corresponding rise in credit quality and reduction in default rate. Conversely, a negative factor weight indicates that a positive change in that factor produces a decrease in asset value, with a corresponding fall in credit quality and increase in default rate. Simulation methods. Whilst the risk of small credit portfolios can be calculated analytically, the large number of calculations required mean that for most portfolios it is better to employ a numerical simulation technique. Monte Carlo simulation is the standard method, and can be thought of as a ‘state-of-the-world generator’ that generates all possible states of the economy and the resulting impact on the value of the credit portfolio. In this way a distribution of all possible portfolio values is built up, from which its credit risk profile can be calculated. Summary of credit portfolio models. There are a number of currently available credit portfolio models that are distinguished by their correlation structures and choice of risk measure. Portfolio model applications: Having discussed the inner workings of credit portfolio models we can now illustrate their uses by examining a number of management applications. Solvency analysis. The most obvious application of a credit portfolio model is to calculate economic capital. This is calculated from the tails of the credit risk distribution by determining the probability that a reduction in portfolio value exceeds a critical value. Credit risk concentrations and portfolio optimisation. Breaking down the aggregate credit risk distribution to show the credit risk of each portfolio element allows risk concentrations and hence diversification opportunities to be identified. For most credit portfolios, simple optimisation techniques will substantially reduce economic capital requirements – typically reductions of 30% are achievable equivalent to annual savings of £288m (assuming a capital charge of 18%) for a portfolio of £100bn. Sensitivity analysis and stress testing: Portfolio models can be used to calculate expected loss rates under different economic scenarios and thus drive dynamic provisioning estimates or loan loss reserving methodologies such as the SBC ACRA reserve. The sensitivity of portfolio credit losses to changes in iour under stress-test scenarios. Conclusion: This article has described the underlying theory of credit portfolio models and illustrated their value in making more effective management decisions. With the rapidly growing market in credit derivatives and portfolio securitisations, the possibility of active credit portfolio management will increase dramatically and result in a fundamental shift in the way banks both originate and hold credit assets. In order to benefit from these new opportunities, banks must ensure that they understand the economic value of their portfolios and how this value can be maximised through efficient credit portfolio management. The underlying macro-economic risk factors can also be examined to determine whether a hedging strategy might be possible.  An extension of this application is to use the models for ‘stress-testing’ to estimate possible changes in portfolio value conditional on extreme macro-economic scenarios.

You can reach CA. Aparna RamMohan (Chartered Accountant) at caaparnasridhar@gmail.com