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

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Author Info

  • Gael M. Martin

    (Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia)

  • Andrew Reidy

    (Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia)

  • Jill Wright

    (Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia)

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.

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File URL: http://hdl.handle.net/10.1002/jae.1033
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 24 (2009)
Issue (Month): 1 ()
Pages: 77-104
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Handle: RePEc:jae:japmet:v:24:y:2009:i:1:p:77-104

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References

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Cited by:
  1. Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
  2. Le-Yu Chen & Jerzy Szroeter, 2009. "Hypothesis testing of multiple inequalities: the method of constraint chaining," CeMMAP working papers CWP13/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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