Title

Improving Asset Price Prediction When All Models are False

Author Info

Garland Durham, University of ColomboFollow
John Geweke, University of Technology, Sydney

Recommended Citation

Postprint version. Published in Journal of Financial Econometrics, Volume 12, Issue 2, Spring April 1, 2014, pages 278-306.

NOTE: At the time of publication, the author Garland Durham was not yet affiliated with Cal Poly.

The definitive version is available at https://doi.org/10.1093/jjfinec/nbt001.

Abstract

This study considers three alternative sources of information about volatility potentially useful in predicting daily asset returns: daily returns, intraday returns, and option prices. For each source of information the study begins with several alternative models, and then works from the premise that all of these models are false to construct a single improved predictive distribution for daily S&P 500 index returns. The prediction probabilities of the optimal pool exceed those of the conventional models by as much as 5.29%. The optimal pools place substantial weight on models using each of the three sources of information about volatility.

Disciplines

Finance

Copyright

2014 Oxford Journals