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. (C) 2013 Elsevier B.V. All rights reserved.
机构:
Department of Statistics, University of Oxford, United KingdomDepartment of Statistics, University of Oxford, United Kingdom
Djanga, Emmanuel
Zhang, Chao
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机构:
Department of Statistics, University of Oxford, United Kingdom
Oxford-Man Institute of Quantitative Finance, University of Oxford, United KingdomDepartment of Statistics, University of Oxford, United Kingdom