Long Memory Value-at-Risk for Longand Short Trading Positionsin the Korean Bond Index
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초록
This article investigates the usefulness of the Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Korean bond indices. For this purpose this study assesses the performance of GARCH, IGARCH and FIGARCH Value-at-Risk (VaR) models based on the normal and Student-t distribution innovations. Our results support the argument that the Student-t distribution model produces more accurate VaR estimates of Korean bond market than the normal distribution models. The results show that considering for long memory and fat-tails performs better in predicting a one-day ahead VaR for both short and long trading positions. Consequently, the FIGARCH model with the Student-t distribution improves the accuracy of volatility forecasting. Correct information on the distributional properties provides for more accurate estimation of VaR for investors and portfolio managers.
Abstract
This article investigates the usefulness of the Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Korean bond indices. For this purpose this study assesses the performance of GARCH, IGARCH and FIGARCH Value-at-Risk (VaR) models based on the normal and Student-t distribution innovations. Our results support the argument that the Student-t distribution model produces more accurate VaR estimates of Korean bond market than the normal distribution models. The results show that considering for long memory and fat-tails performs better in predicting a one-day ahead VaR for both short and long trading positions. Consequently, the FIGARCH model with the Student-t distribution improves the accuracy of volatility forecasting. Correct information on the distributional properties provides for more accurate estimation of VaR for investors and portfolio managers.
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