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Improving Asset Price Prediction When All Models are False
Journal of Financial Econometrics
  • Garland Durham, University of Colombo
  • John Geweke, University of Technology, Sydney
Publication Date
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.
Citation Information
Garland Durham and John Geweke. "Improving Asset Price Prediction When All Models are False" Journal of Financial Econometrics Vol. 12 Iss. 2 (2014) p. 278 - 306
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