Forecast performance of implied volatility and the impact of the volatility risk premium
AbstractForecasting volatility has received a great deal of research attention, with the relative performance of econometric models based on time-series data and option implied volatility forecasts often being considered. While many studies find that implied volatility is the preferred approach, a number of issues remain unresolved. Implied volatilities are risk-neutral forecasts of spot volatility, whereas time-series models are estimated on risk-adjusted or real world data of the underlying. Recently, an intuitive method has been proposed to adjust these risk-neutral forecasts into their risk-adjusted equivalents, possibly improving on their forecast accuracy. By utilising recent econometric advances, this paper considers whether these risk-adjusted forecasts are statistically superior to the unadjusted forecasts, as well as a wide range of model based forecasts. It is found that an unadjusted risk-neutral implied volatility is an inferior forecast. However, after adjusting for the risk premia it is of equal predictive accuracy relative to a number of model based forecasts.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 45.
Date of creation: 21 Jul 2009
Date of revision:
Implied volatility; volatility forecasts; volatility models; volatility risk premium; model confidence sets;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G00 - Financial Economics - - General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-09-26 (All new papers)
- NEP-ETS-2009-09-26 (Econometric Time Series)
- NEP-FOR-2009-09-26 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Patton, Andrew J., 2011.
"Volatility forecast comparison using imperfect volatility proxies,"
Journal of Econometrics,
Elsevier, vol. 160(1), pages 246-256, January.
- Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
- Peter Hansen & Asger Lunde & James M. Nason, 2003.
"Choosing the Best Volatility Models:The Model Confidence Set Approach,"
2003-05, Brown University, Department of Economics.
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," Working Paper 2003-28, Federal Reserve Bank of Atlanta.
- Jorion, Philippe, 1995. " Predicting Volatility in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 50(2), pages 507-28, June.
- Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
- Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
- Scott I. White & Adam E. Clements & Stan Hurn, 2004. "Discretised Non-Linear Filtering for Dynamic Latent Variable Models: with Application to Stochastic Volatility," Econometric Society 2004 Australasian Meetings 46, Econometric Society.
- Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(02), pages 256-271, August.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (School of Economics and Finance) The email address of this maintainer does not seem to be valid anymore. Please ask School of Economics and Finance to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.