VAR Estimation with Power Ewma Model - Conservativeness, Accuracy and Efficiency
Financial asset returns are well-known to be non-normal and leptokurtic with the tails fatter than normal distribution. The Standard EWMA estimator with the normality assumption (used in JP Morgan's RiskMetrics(R) model) will be inefficient and lead to understate the true value of risk if the asset returns are fat-tailed distributed. On the basis of the power exponential distribution (also known as the generalized error distribution) the family EWMA estimators, nesting Power EWMA, Standard EWMA and Robust EWMA, are proposed by Guermat amp; Harris (2002). Using these newly developed estimators, we first forecast the VaR of daily returns for TAIEX, FTSE 100, and DJIA. Subsequently, the back-testing is performed to evaluate the VaR models. Performance assessment is based on a range of measures that address the conservativeness, accuracy and efficiency of each model. The results demonstrate that the members of the family of EWMA estimators based on power exponential distribution rather than normal distribution offer a superior coverage for the extreme risk over the RiskMetrics(R) estimator, and show that Power EWMA performs excellent accuracy in VaR estimation
Year of publication: |
[2008]
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Authors: | Liu, Mei-Ying |
Other Persons: | Wu, Kenny (contributor) ; Lee, Hsien-Feng (contributor) |
Publisher: |
[2008]: [S.l.] : SSRN |
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