An empirical investigation of multiperiod tail risk forecasting models |
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Institution: | 1. Department of Finance, School of Business, Hohai University, China;2. Department of Management Science and Engineering, School of Business, Hohai University, China;1. College of Management and Economics, Tianjin University, Tianjin 300072, PR China;2. DCU Business School, Dublin City University, Glasnevin, Dublin 9, Ireland;3. School of Finance, Nankai University, Tianjin 300350, PR China |
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Abstract: | In the context of multiperiod tail risk (i.e., VaR and ES) forecasting, we provide a new semiparametric risk model constructed based on the forward-looking return moments estimated by the stochastic volatility model with price jumps and the Cornish–Fisher expansion method, denoted by SVJCF. We apply the proposed SVJCF model to make multiperiod ahead tail risk forecasts over multiple forecast horizons for S&P 500 index, individual stocks and other representative financial instruments. The model performance of SVJCF is compared with other classical multiperiod risk forecasting models via various backtesting methods. The empirical results suggest that SVJCF is a valid alternative multiperiod tail risk measurement; in addition, the tail risk generated by the SVJCF model is more stable and thus should be favored by risk managers and regulatory authorities. |
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Keywords: | Backtest Expected shortfall Multiperiod risk forecasting Value-at-risk |
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