The importance of the volatility risk premium for volatility forecasting |
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| Institution: | 1. Zeppelin University, 88045 Friedrichshafen, Germany;2. ICMA Centre, Henley Business School, University of Reading, Reading RG6 6BA, UK;1. Department of Economics and CEFIN, University of Modena and Reggio Emilia, Italy;2. Department of Economics and Social Sciences, Sapienza University of Rome, Italy;3. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium;1. School of Finance, Southwestern University of Finance and Economics, Chengdu, Sichuan, PR China;2. School of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, PR China;1. University of Liverpool, UK;2. University of Glasgow, UK;1. The Norwegian University of Science and Technology, NO-7491 Trondheim, Norway;2. Lillehammer University College, NO-2624 Lillehammer, Norway;1. Columbia Business School and NBER, United States;2. European Central Bank, Germany |
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| Abstract: | In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major energy markets. Using regression models and statistical loss functions, we find compelling evidence to suggest that the risk premium adjusted implied volatility significantly outperforms other models, including its unadjusted counterpart. Our main finding holds for different choices of volatility estimators and competing time-series models, underlying the robustness of our results. |
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| Keywords: | Volatility forecasting Volatility risk premium Implied volatility |
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