Abstract
This study proposes a procedure for using a generalized error distribution (GED) model in conjunction with historical simulation when computing Value-at-Risk (VaR). We collected data from the New York S&P 500, the London Times FTSE 100 and the Frankfurt Index to evaluate the VaR approaches. The sample period is from January 1, 1990 to December 31, 2001. Based on analysis of conservativeness, accuracy and efficiency, we show that incorporating GED model updating into historical simulation method is a substantial improvement.
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