Risk Management Lessons from the Credit Crisis - The Paul Merage ...

0 European Financial Management, 2009, forthcoming

Risk Management Lessons from the Credit Crisis

Philippe Jorion Paul Merage School of Business, University of California at Irvine and Pacific Alternative Asset Management Company (PAAMCO) E-mail: [email protected]

Abstract Risk management, even if flawlessly executed, does not guarantee that big losses will not occur. Big losses can occur because of business decisions and bad luck. Even so, the events of 2007 and 2008 have highlighted serious deficiencies in risk models. For some firms, risk models failed because of known unknowns. These include model risk, liquidity risk, and counterparty risk. In 2008, risk models largely failed due to unknown unknowns, which include regulatory and structural changes in capital markets. Risk management systems need to be improved and place a greater emphasis on stress tests and scenario analysis. In practice, this can only be based on position-based risk measures that are the basis for modern risk measurement architecture. Overall, this crisis has reinforced the importance of risk management. Keywords: risk management, financial crisis, risk models, stress test JEL classifications: D81 (decision-making under risk), G11 (investment decisions), G16 (government policy and regulation), G21 (banks), G32 (financial policy)

Keynote Address at the European Financial Management Association (EFMA) meetings, Nantes, France, April 2009

P. Jorion – Risk Management Lessons from the Credit Crisis

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“The best Wall Street minds and their best risk-management tools failed to see the crash coming,” New York Times, January 2, 2009

Many financial institutions that experienced large losses over the past few months apparently employed sophisticated risk management systems. That losses occurred does not necessarily imply that there were failures in risk management, however. As Stulz (2008) put it, “A large loss is not evidence of a risk management failure because a large loss can happen even if risk management if flawless.” The scale of losses in the credit crisis that started in 2007 has been unprecedented. The International Monetary Fund (IMF) has estimated that total losses on US assets now exceed $4,000 billion. The root causes of this crisis are many. Taylor (2008) argues that government actions and interventions “caused, prolonged, and worsened the financial crisis.” In addition, however, there were several layers of failures in the private sector. This goal of this presentation is narrowly focused on the role of risk management in this credit crisis. This presentation is structured as follows. The first Section reviews and describes the structure of modern risk measurement systems. The key feature is that it relies on position-level information, unlike the traditional returns-based risk measures. The second Section then discusses the various types of risks that an institution is exposed to. A useful classification is into known knowns, known unkowns, and unknown unknowns. As Donald Rumsfeld put it, it is the risks in “the latter category that tend to be the difficult ones.” Nevertheless, risk managers have several tools at their disposal to manage risks better. The third Section draws risk management lessons from the credit crisis. The last Section concludes.

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1. Risk Measurement Systems To start with, let us describe the main components of modern risk measurement systems, which are described in Figure 1: 

From market data, construct the distribution of risk factors (e.g., normal, empirical, or other).



Collect the portfolio positions and map them onto the risk factors.



Use the risk engine to construct the distribution of portfolio profit and losses over the selected period. This can be summarized by a Value-at-Risk (VAR) number, which represents the worst loss that will not be exceeded at the pre-specified confidence level.

Positions Global Repository

Trades from front office Data feed with current prices

Risk Factors Historical Market Data

Model

Mapping

Risk Engine3a

Positions

Data Warehouse

Portfolio Distribution

Value at Risk Reports

Distribution of Risk Factors

Risk Warehouse

Fig. 1. Components of a risk measurement system The key feature of this system is that it is position-based. Traditionally, risk measures have been built from returns-based information. The latter is easy and cheap to implement. It also accounts for dynamic trading of the portfolio. On the other hand,


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returns-based risk measures suffer from severe drawbacks. They offer no data for new instruments, markets, and managers. They do not capture—or rather, are very slow at identifying—style drift. They may not reveal hidden risks. Lo (2001) gives the example of a hypothetical fund, called Capital Decimation Partners, which seems to perform very well, with a high Sharpe ratio. In this case, the fund holds a leveraged short position in an equity index option. As long as the option is not exercised, the portfolio generates a positive and steady return. The returns-based VAR is totally misleading. More generally, returns-based risk measures give no insight into the risk drivers of the portfolio. Most of these drawbacks are addressed by position-based risk measures. They can be applied to new instruments, markets, and managers. These use the most current position information, which should reveal style drift or hidden risks. For example, Jorion (2007) shows that the risk of Capital Decimation Partners can be captured and controlled effectively by position-based risk systems. In addition, position-based systems can be used for forward-looking stress tests. Position-based risk systems, on the other hand, have drawbacks. First, they require more resources and are expensive to implement. A large bank could have several million positions, in which case aggregation at the top level is a major technology challenge. Second, position-based risk measures assume that the portfolio is frozen over the horizon and ignores dynamic trading. To some extent, this problem can be mitigated by more frequent risk measurement. Finally, position-based systems are susceptible to errors in data and models. They require modeling all positions from the ground up, repricing instruments as a function of movements in the risk factors. In some cases,


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standard approaches based on a fixed historical window are inappropriate.1 In others, the modeling of instruments is quite complex, leading to model risk. Even so, position-based risk measures are vastly more informative than returnsbased risk measures. This explains why all modern risk management architectures rely on position-level information. This does not mean that returns-based information is useless, however. In some cases, it can be combined with position-based information for more realistic risk measures. Also, risk managers need to backtest their risk systems. This involves systematic comparisons of the actual returns with the risk forecasts. With a wellcalibrated system, the number of cases of losses worse than VAR, also called exceptions, should correspond to the confidence level. For example, backtests of a 1-day VAR at the 99 percent level of confidence over a period of one year should yield, on average, 2 to 3 exceptions per year (actually, 1% times 252, or about 2.5). Too many exceptions should lead the risk manager to re-examine the models. In spite of all this apparatus, a number of banks suffered major losses during the credit crisis. In 2007 alone, for example, UBS suffered losses of $19 billion from positions in mortgage-backed securities alone. Can we conclude from this information that its risk management system was flawed?

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Jorion (2008) analyzes the conventional application of VAR measures to Mergers and Acquisition (M&A) arbitrage portfolios. Such trading strategies involve payoffs that have discontinuous: either the acquisition goes through or not. This leads to skewed distributions that cannot be measured well with conventional risk methods using moving windows based on recent historical data. On the other hand, knowledge of the positions can be used to develop more realistic, forward-looking model of portfolio risk.


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2. Classification of Risks To analyze this point, risks can be classified into three categories: “known knowns,” “known unknowns,” and “unknown unknowns,” corresponding to different levels of uncertainty.

2.1 Known Knowns Let us start with a flawless risk measurement system, where all the risks are perfectly measured. This implies that the risk manager correctly identifies all the risk factors and properly measures their distribution as well as the exposures of the current portfolio, leading to an appropriate description of the distribution of total profits and losses. Top management then decides on a particular risk-return profile for the business. In this case, losses can still occur due to a combination of bad luck and the fact that management accepted too much exposure. As an example, take a long/short equity portfolio with an equity beta of 0.5. Figure 2 describes the distribution of annual return on U.S. equities since 1871. This information can be used to build a distribution of returns for the portfolio in question. The S&P index lost 38% in 2008. As a result, this portfolio should have lost 0.5 times 38%, or around 19%. This loss is a combination of bad luck (i.e., a very large fall in the S&P index, but not unprecedented as U.S. stocks lost 43% in 1931) and exposure (i.e., having a high beta). If the distribution was properly measured, the outcome matched the risk forecast. In this case, the risk measurement system was flawless.

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1931

2008 1937

2002 1974 1930 1917 1907

2001 1973 1966 1957 1941 1920 1903 1893 1890 1884 1876

2000 1990 1981 1977 1969 1962 1953 1946 1940 1939 1934 1932 1929 1914 1913 1910 1887 1883 1877 1873

2007 2005 1994 1992 1987 1984 1978 1970 1960 1956 1948 1947 1923 1912 1911 1906 1902 1896 1895 1894 1892 1889 1888 1882 1881 1875

2006 2004 1993 1988 1986 1979 1972 1971 1968 1965 1964 1959 1952 1949 1944 1926 1921 1919 1916 1900 1899 1897 1886 1878 1874 1872 1871

2003 1999 1998 1996 1983 1982 1976 1967 1963 1961 1951 1943 1942 1925 1924 1922 1918 1909 1905 1901 1898 1891 1885 1880

1997 1995 1991 1989 1985 1980 1975 1955 1950 1945 1938 1936 1927 1915 1904

1958 1935 1928 1908

1954 1933 1879