- Omotunde Johnson
- Published Date:
- April 2002
Regulators and bankers do not always have the same focus when discussing certain topics. Until recent years, risk was one of these topics. In general banks, with most of their portfolios invested in assets issued in the more stable economies, were more interested in day-today risk management while regulators were more interested in scenarios of systemic risk. Banks also tended to focus less on the impact of macroeconomic variables on potential losses. The market upheavals of 1998 have changed banks’ views on these issues, as reflected in this paper, and have brought a new emphasis on systemic risk and on the impact of the correlated nature of market and credit risks on the amounts exposed to counterparties.
Exposure estimation, while straightforward for traditional credit products, becomes more complex when the exposure is contingent on market factors, as is the case for derivative products, and more complex still when there is a dependency between counterparty credit quality and the relevant market factors. For example, for a U.S. bank, a swap by which the bank receives dollars and pays a domestic currency, will be well in the money if there is a devaluation of the domestic currency. But the devaluation will likely lead to deterioration of the credit quality of the domestic counterparty and to a possible default. The potential losses that the bank may suffer under those circumstances are hard to calculate since the procedure requires an estimate of the probability of default of the counterparty under extreme market factors conditions.
Regulators have explicitly recognized the uncertain future credit exposure on swaps, forwards and other derivative contracts. The Basel Capital Accord requires regulatory capital for current exposure (roughly, the amount which would be lost should the counterparty default today) plus additional “add-on” capital to account for the potential future exposure due to moves in the underlying market factors (the cost of replacing a contract some time in the future).
Practitioners have dealt with these issues using different methodologies. In order to examine these procedures, 12 large commercial and investment banks formed the Counterparty Risk Management Policy Group (CRMPG) in January 1999, and produced a report in June 1999. One of the report’s main recommendations was that financial intermediaries “should upgrade their ability to monitor and, as appropriate, set limits for various exposure measures.” The CRMPG report also highlights the fact that the credit quality of the counterparty depends on underlying market factors and that this dependency should be taken into account when measuring exposure.
To address the correlation between market and credit risk the CRMPG report proposes stress tests that simultaneously shock the market factors underlying the exposure amounts and the credit factors influencing default. J.P. Morgan’s proposal, as reflected in the paper by Uhl and Monet, is to use a new methodology called “wrong-way exposure,” where the name itself is intended to capture the negative correlation between the counterparty’s exposure size and credit quality (i.e., exposure increases at the “wrong” time). If applied to the currency swap previously mentioned, an exposure measure will take the following form:
exposure = (value of the long leg denominated in USD-Value of the short leg denominated in the domestic currency at the post-devaluation exchange rate) × Probability of default of the counterparty given devaluation.
Given that default can take place over a range of possible exchange rates, the exposure can be calculated as an average or as a distribution of possible outcomes. Uhl and Monet’s paper deals with the procedure to estimate the average exposure. It also extends some of the analytical framework to other market factors that can give rise to a wrong-way exposure such as interest rates.
Wrong-Way Exposure: Definition and Methodology
Transactions are subject to wrong-way exposure when scenarios in which the transactions are in the money are likely to coincide with the scenarios in which the counterparty may have difficulties in fulfilling its obligations due to systemic risk, such as sovereign default, exchange rate devaluation, or any severe deterioration of market conditions.
The important point underlying these situations is the dependence of the credit quality of the counterparty on the market factor underlying the contract or, in short, the probability of default is larger when macroeconomic conditions deteriorate. If that dependence is not taken into account, the potential losses will be underestimated. Wrong-way credit exposure is a concept that has been mainly applied to derivatives contracts (and not to loans) since the value of the transaction, and consequently the credit exposure, changes with market factors.
Wrong-way exposure methodologies are in their early stages. Levy and Levin (1999) and the Monet and Uhl paper discuss wrong-way foreign exchange risk. They estimate the expected wrong-way exposure by using the expected value of the currency, conditional on the default of a sovereign (readily observable), or conditional on the default of a corporate borrower given that the sovereign did not default. For the latter case, they use a Merton-based model that takes into account the correlation between changes in asset values, proxied by equity prices, and changes in the exchange rate. Uhl and Monet also discuss preliminary approaches to wrong-way interest rate and equity exposures. Finger (2000) derives a complete distribution of risk factors, conditional on default (not only the expected value), since in general defaults can take place over a range of extreme scenarios. He does so by first conditioning default on the risk factors and then using Bayes formula to work backwards.
I have four main comments: (1) wrong-way exposure is not a new topic as the authors would suggest; (2) wrong-way exposure can cover more situations than normally imagined; (3) there are some methodological issues worth underscoring; and (4) the examples in the paper do not apply the methodology that is discussed in the paper.
The Topic Is Not New
Wrong-way exposure is not a new topic but one that often recurs whenever market forces begin to affect credit risk (correlated nature of market and credit risks). In recent years, banks have rediscovered this phenomenon after suffering sizable losses. What is definitely new is the emphasis on the dependence of credit risk on macroeconomic variables, the focus on systemic scenarios, and the methodological approach to both.
Wrong-Way Exposure Has Wide Applications
Wrong-way exposure can cover more situations than usually assumed. I would like to mention one clear application of the concept that is not covered in any paper on this subject. This is the case of a mortgage loan and what could be called “wrong-way real estate price exposure.”
A loan collateralized with property is very similar to a swap. A typical swap that could cause wrong-way exposure to a U.S. bank is in fact a loan in foreign currency collateralized with a loan in domestic currency with the exposure given by the difference between the values of the two loans. When there is a depreciation of the domestic currency, the value of the collateral falls at a time when it is most needed—that is, when the credit quality of the counterparty deteriorates and this is what magnifies the exposure. The situation is not different from a floating-rate loan collateralized with real estate. The exposure of a mortgage loan is given by the difference between the loan and the value of the collateral, net of transaction costs.
Scenarios of systemic risk may involve not only a large depreciation of the domestic currency but also large decreases in real estate prices, which may coincide with a deterioration of the borrowers’ credit quality. While during so-called normal times a positive correlation has been observed between changes in the price of real estate and changes in interest rates, under scenarios of large and volatile interest rates, this correlation becomes negative. Large interest rates often appear together with sizable depreciation in property values. This situation is magnified in emerging economies, where interest rates are not only the intertemporal rate of substitution or an instrument of monetary policy. For instance, they may be an instrument to defend a band or a peg, since interest rates in almost all cases bear all the adjustments to market pressures. Under those circumstances mentioned in the previous example of real estate loans, the collateral does not cover the value of the loan and once foreclosed, it cannot be sold immediately unless the bank is willing to realize losses (i.e., the mortgage loan is in the money).
The Colombia savings and loan crisis in 1999 is a recent example in which banks were caught by surprise: they thought they were protected against both interest rate and credit risks due to floating interest rates and real estate collateral. However, they suffered large losses due to client default and very depressed real estate prices.
Three methodological comments seem particularly relevant. First, the extension of this methodology to other prices and to a multivariate setting would be useful. Given that this literature is part of a much broader topic, it is natural to ask whether it could accommodate other situations and be used as a general approach to integrate market and credit risk, under systemic risk scenarios. For example, one might wonder whether it would be possible to derive the distribution of real estate prices conditional on sovereign default or corporate default. The wrong-way exposure methodology is a univariate approach to modeling market and credit risk; that is, this approach captures one market factor at a time, making it quite complex to extend to a correlated environment. Ideally, it would be better to have a framework that captures the correlations among market factors.
Second, the client leverage—and not the value of the contract—should be the emphasis of this methodology. Wrong-way exposure emphasizes the value of the contract, but default is mainly determined by the leverage of the client (the ratio of the value of all the client’s positions to the equity value). Since the value of equity also changes during a crisis, the wrong-way approach should be extended to some measure of leverage.
Third, the connections between credit risk and market factors should be more clearly identified both empirically and theoretically. Uhl and Monet estimate the expected wrong-way exposure of a position in an emerging market currency under a systemic scenario (i.e., a large depreciation) as the product of the expected value of the currency, given default, times the probability of default conditional on market factors. However, the probability of default used in the examples is the probability of default embedded in the term structure of interest rates, which is not the probability of default conditional on market factors. In contrast, Finger (2000) derives a formula for the conditional density of risk factors whose main input is the probability of default conditional on risk factors. He proposes a function for that conditional probability of default and a number of assumptions are made to calibrate the parameters used in that function (e.g., size of the market price move that will induce default, speed of transition from nondefault states to a default state). However, those parameters that are in fact endogenous variables do not have an explicit link to macro-economic variables.
The emphasis of Uhl and Monet on systemic risk and the effort made to model the correlated nature of market and credit risk in the paper is most welcome. However, it would be important to be able to extend this methodology to all situations in which this correlation has proved to be critical. Advances in the wrong-way exposure methodology should include the possibility of modeling several market factors, the evaluation of the leverage of the client (as opposed to a single transaction), and the inclusion of a more explicit link between the probability of default and macroeconomic factors.