上财系列金融风险控制与管理QMRVaRBasedontheVolatilitySkewnessandkurtosis.ppt

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上财系列金融风险控制与管理QMRVaRBasedontheVolatilitySkewnessandkurtosis.ppt

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上财系列金融风险控制与管理QMRVaRBasedontheVolatilitySkewnessandkurtosis

Quantitative Risk Management- VaR and its Extension Ming-Heng Zhang? The more ideas you have, the more thinks you do VaR Based on the Volatility Skewness and Kurtosis David X. Li, david.li@? Main keys To calculating Value-at-Risk (VaR) by use of skewness and kurtosis as well as the standard deviation or volatility explicitly; To construct an approximate confidence interval from the first two moment conditions; Empirical studies show explicitly how the confidence interval is affected by the standard deviation, skewness and kurtosis; use ten years of daily observations on twelve different foreign exchange spot rates and find the new approach captures the extreme tail much better than the standard VaR calculation method used in RiskmetricsTM VaR Based on the Volatility Skewness and Kurtosis Introduction JP Morgan RiskMetricsTM Technical Document the subsequent BIS adoption of VaR risk report for all trading portfolios of financial institutions Zangari (1996), the VaR calculated under the normal assumption underestimates the actual risk since the distribution of many observed financial return series have tails that are fatter than those implied by conditional normal distribution How to incorporate these observations into the VaR calculation is an important issue? VaR Based on the Volatility Skewness and Kurtosis The main two methods to construct VaR if we do not assume normality Parametric Hull, J. and A. White (1998), Value at Risk When Daily Changes in Market Variables are not Normally Distributed, The Journal of Derivatives, Spring 1998, pp. 9-19. non-parametric Kupiec, P. (1995), Techniques for Verifying the Accuracy of Risk Measurement Models, The Journal of Derivatives, Winter 1995, pp. 73-84. VaR Based on the Volatility Skewness and Kurtosis Normalization of Random Variable An approximate confidence interval based on large sample asymptotic theory Underlying theory – estimating function Godambe, V. P. (1991), Estimating Functions, Oxford: Oxford Univer