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Value-at-Risk Analysis of the Long Memory Volatility Process£ºThe Case of Individual Stock Returns

  • Sang Hoon Kang School of Commerce, University of South Australia.
  • Seong-Min Yoon Department of Economics, Pusan National University
This article investigated the relevance of the skewed Student-t distribution innovation in analyzing volatility stylized facts, namely, volatility clustering, volatility asymmetry, and volatility persistence, in three individual Korean shares. For this purpose, we assessed the performance of RiskMetrics and two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH) with the normal, Student-t, and skewed Student-t distribution innovations. From the results of the empirical VaR analysis, the skewed Student-t distribution innovation provided more accurate VaR calculations, in capturing stylized facts in the volatility of three sample returns. Thus, the correct assumption of return distribution might improve the estimated performance of VaR models in the Korean stock market.

  • Sang Hoon Kang
  • Seong-Min Yoon
This article investigated the relevance of the skewed Student-t distribution innovation in analyzing volatility stylized facts, namely, volatility clustering, volatility asymmetry, and volatility persistence, in three individual Korean shares. For this purpose, we assessed the performance of RiskMetrics and two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH) with the normal, Student-t, and skewed Student-t distribution innovations. From the results of the empirical VaR analysis, the skewed Student-t distribution innovation provided more accurate VaR calculations, in capturing stylized facts in the volatility of three sample returns. Thus, the correct assumption of return distribution might improve the estimated performance of VaR models in the Korean stock market.
Value-at-Risk (VaR),Long Memory,Asymmetry,Fat Tails,Rescaled Range (R/S) Analysis