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Can the Skewed Student-t Distribution Assumption Provide Accurate Estimates of Value-at-Risk?

윤성민|강상훈
한국재무관리학회 (Korea Financial Management Association)|2007.09|재무관리연구 (THE KOREAN JOURNAL OF FINANCIAL MANAGEMENT)|제24권 제3호|pp. 153-186

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참고문헌

키워드

GARCHFIGARCHValue-at-Risk(VaR)RiskMetricsExpected ShortfallSkewed Student-t
주제분류경영학

초록

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk (VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed- Student-t distributions in two daily returns of the Korean Composite Stock Index (KOSPI) and Korean Won-US Dollar (KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

Abstract

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk (VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed- Student-t distributions in two daily returns of the Korean Composite Stock Index (KOSPI) and Korean Won-US Dollar (KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

참고문헌 (52)

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