Does the option market produce superior forecasts of noise-corrected volatility measures?

Gael Margaret Martin, Andrew Reidy and Jill Wright
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Andrew Reidy: Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia, Postal: Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia
Jill Wright: Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia, Postal: Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia

Journal of Applied Econometrics, 2009, vol. 24, issue 1, pages 77-104

Abstract: This paper assesses the robustness of the relative performance of spot- and options-based volatility forecasts to the treatment of microstructure noise. Robustness of the results to the method of constructing option-implied forecasts is also investigated. Using a test for superior predictive ability, model-free implied volatility, which exploits information in the volatility 'smile', and at-the-money implied volatility, which does not, are both tested as benchmark forecasts of a range of alternative volatility proxies. The results provide compelling evidence against the model-free forecast for three Dow Jones Industrial Average stocks, over a 2001-2006 evaluation period. In contrast, the at-the-money implied volatility forecast is given strong support for the three equities over this period. Neither benchmark is supported for the S&P500 index. Importantly, the main qualitative results are invariant to the method of noise correction used in measuring future volatility. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2009
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Related works:
Working Paper: Does the Option Market Produce Superior Forecasts of Noise-Corrected Volatility Measures? (2007)
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