Details
| Original language | English |
|---|---|
| Pages (from-to) | 303-320 |
| Number of pages | 18 |
| Journal | Journal of Banking and Finance |
| Volume | 40 |
| Issue number | 1 |
| Publication status | Published - Mar 2014 |
| Externally published | Yes |
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.
Keywords
- Implied volatility, Volatility forecasting, Volatility risk premium
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Finance
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Journal of Banking and Finance, Vol. 40, No. 1, 03.2014, p. 303-320.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The importance of the volatility risk premium for volatility forecasting
AU - Prokopczuk, Marcel
AU - Wese Simen, Chardin
N1 - Funding information: Marcel Prokopczuk gratefully acknowledges financial support from the British Academy. We thank two anonymous referees, Davide Avino, Christian Dorion, Olaf Korn, Ogonna Nneji and seminar participants at the FDIC Annual Derivatives Securities and Risk Management Conference (2013), the European Meeting of the Econometric Society (2013), the Meeting of the German Finance Association (2013), the Financial Management Association Meeting (2013), Zeppelin University and the University of Reading for valuable comments.
PY - 2014/3
Y1 - 2014/3
N2 - 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.
AB - 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.
KW - Implied volatility
KW - Volatility forecasting
KW - Volatility risk premium
UR - http://www.scopus.com/inward/record.url?scp=84891422302&partnerID=8YFLogxK
U2 - 10.1016/j.jbankfin.2013.12.002
DO - 10.1016/j.jbankfin.2013.12.002
M3 - Article
AN - SCOPUS:84891422302
VL - 40
SP - 303
EP - 320
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
SN - 0378-4266
IS - 1
ER -