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上财系列金融风险控制与管理QMRImprovingValueatRiskforNonNormalReturndistributions
Quantitative Risk Management - VaR and its Extension Ming-Heng Zhang? Improving Value at Risk for NON-Normal Retrun Distributions Authors – Doowoo Nam and Benton E.Gup Abstract – Non-normality of asset return distributions to develop a new VaR methodology to better handle skewness and fat-tailedness than existing approaches, while at the same time being more flexible than the historical simulation approaches. To adopt the g-and-h distribution Improving Value at Risk for NON-Normal Return Distributions Intro One of hottest research themes in finance since the mid-1990s 1994J.P.Morgen RiskMetricsTM,1994 Bank Industry European Union’s Capital Adequacy Directive (CAD),1993 Basle Committee on Banking Supervision,1996 The Federal Reserve System, 1996 …. … GARCH-model Improving Value at Risk for NON-Normal Return Distributions Overview of VaR Model Time horizon - T Confidence level - CL The probability of losses from adverse changes in value ?V Methodologies Parametric, analytical, or variance-covariance Nonparametric, or historical simulatoin Monte Carlo simulation Extreme Value theory Assessment on the existing papers Improving Value at Risk for NON-Normal Return Distributions The g-and-h VaR methodology(Transform Function) Skewness – S/g = 0 Kurtosis – K/h != 0 Location - A Scale - B Transform Function Special case Improving Value at Risk for NON-Normal Return Distributions The g-and-h VaR methodology(Transform Function) quantitles with the quantitle of the normal Estimation Alternative of Skewness and kurtosis Estimate the corresponding value of g by varying the quantitles and then estimate h given the value of g Improving Value at Risk for NON-Normal Return Distributions The g-and-h VaR methodology(Transform Function) VaR Based on the g-and-h distribution (the relationship should be proofed) Improving Value at Risk for NON-Normal Return Distributions Estimation of VaR and Backtesting Data Description Treasury Bill and the foreign exchange rate B
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