Investing in SkewsMADAN, DILIP B.; MCPHAIL, GAVIN S.
2000 The Journal of Risk Finance
doi: 10.1108/eb022941
Asset allocation has primarily focused its attention on attaining mean variance efficiency by employing diversification strategies following the portfolio selection methodologies of Markowitz1952. These are important principles that have given rise to a large variety of diversified investment choices in mutual funds that now outnumber the available choices for investment in stocks. Paralleling this development has been a growing interest in the second odd moment describing returns, the level of skewness. The empirical stability of return skewness has been noted in Beedles and Simkowitz1980. Earlier, the importance of skewness for portfolio selection was studied by Arditi and Levy1975 and Kraus and Litzenberger1976. More recently, motivated by the persistence skews observed in option markets Bates1991, Bakshi Kapadia, and Madan 2000 take up the issue of studying the links between the statistical and risk neutral skews, while Harvey and Siddique1999 address the asset pricing implications of investor preferences for skewness. Evidence is also presented by Carr, Geman, Madan, and Yor 2000 that the primary model for diversified returns is that of a pure jump return process reflecting both, excess kurtosis and skewness.
ModelIndependent Measures of Volatility ExposureKURUC, ALVIN
2000 The Journal of Risk Finance
doi: 10.1108/eb022942
The development of standardized measures of institutionwide volatility exposures has so far lagged that for measures of asset price and interestrate exposurelargely because it is difficult to reconcile the various mathematical models used to value options. Recent mathematical results, however, can be used to construct standardized measures of volatility exposure. We consider here techniques for reconciling vegas for financial options valued using stochastic models that may be mathematically inconsistent with each other.
Capital Requirement A New Method Based on Extreme Price VariationsLONGIN, FRANOIS
2000 The Journal of Risk Finance
doi: 10.1108/eb022945
From a regulatory point of view, as explained by Dimson and Marsh 1994, 1995, the amount of capital required by a financial institution to ensure an acceptably small probability of failure should depend on the risk associated with the assets detained in its portfolio. Dimson and Marsh 1994 conduct an empirical study on long and short equity trading books of securities firms acting as market makers. They consider different existing regulations the comprehensive approach, as applied in the United States by the Securities and Exchange Commission the buildingblock approach, as proposed by the Basle Committee on Banking Supervision, and incorporated in the European Community 1992 Capital Adequacy Directive CAD and the portfolio approach, which in the U.K. forms part of the rules of the Securities and Futures Authority 1992. All three methods are compared via the position risk requirement PRR that determines the amount of capital that financial institutions have to put aside. As shown by the authors in their empirical study, the methods proposed by the international regulators are barely related to the risk of the portfolios Only for the national U.K. rules, the PRR and the risk of a portfolio show positive correlation.
Sending the Herd Off the Cliff Edge The Disturbing Interaction Between Herding and MarketSensitive Risk Management PracticesPERSAUD, AVINASH
2000 The Journal of Risk Finance
doi: 10.1108/eb022947
In the international financial arena, G7 policymakers chant three things more marketsensitive risk management, stronger prudential standards, and improved transparency. The message is that we do not need a new world order, but we can improve the workings of the existing one. While many believe this is an inadequate response to the financial crises of the last two decades, few argue against risk management, prudence, and transparency. Perhaps we should, especially with regards to marketsensitive risk management and transparency. The underlying idea behind this holy trinity is that it better equips markets to reward good behavior and penalize the bad, across governments and market players. However, while the market is discerning in the long run, there is now compelling evidence that in the short run, market participants find it hard to distinguish between the good and the unsustainable they herd and contagion is common.
Efficient RiskReturn Frontiers for Credit RiskMAUSSER, HELMUT; ROSEN, DAN
2000 The Journal of Risk Finance
doi: 10.1108/eb022948
The riskreturn tradeoff has been a central tenet of portfolio management since the seminal work of Markowitz 1952. The basic premise, that higher expected returns can only be achieved at the expense of greater risk, leads naturally to the concept of an efficient frontier. The efficient frontier defines the maximum return that can be achieved for a given level of risk or, alternatively, the minimum risk that must be incurred to earn a given return. Traditionally, market risk has been measured by the variance or standard deviation of portfolio returns, and this measure is now widely used for credit risk management as well. For example, in the popular CreditMetrics methodology J.P. Morgan 1997, the standard deviation of credit losses is used to compute the marginal risk and risk contribution of an obligor. Kealhofer 1998 also uses standard deviation to measure the marginal risk and, further, discusses the application of meanvariance optimization to compute efficient portfolios. While this is reasonable when the distribution of gains and losses is normal, variance is an inappropriate measure of risk for the highly skewed, fattailed distributions characteristic of portfolios that incur credit risk. In this case, quantilebased measures that focus on the tail of the loss distribution more accurately capture the risk of the portfolio. In this article, we construct credit risk efficient frontiers for a portfolio of bonds issued in emerging markets, using not only the variance but also quantilebased risk measures such as expected shortfall, maximum percentile losses, and unexpected percentile losses.