Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices

Fan, Rui and Taylor, Stephen John and Sandri, Matteo (2018) Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices. Journal of Futures Markets, 38 (1). pp. 83-103. ISSN 0270-7314

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Abstract

This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are historical lognormal densities with dispersion determined by forecasts of realized variances obtained from 5-minute returns. Three further sets are defined by transforming risk-neutral and historical densities into real-world densities. The most accurate method applies the risk transformation to the Black-Scholes densities. This method outperforms all others for 87% of the comparisons made using the likelihood criterion.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Futures Markets
Additional Information:
This is the peer reviewed version of the following article: Fan R, Taylor SJ, Sandri M. Density forecast comparisons for stock prices, obtained from high-frequency returns and daily option prices. J Futures Markets. 2018;38:83–103. https://doi.org/10.1002/fut.21859 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002fut.21859/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2000
Subjects:
?? economics, econometrics and finance(all)financegeneral business,management and accountingeconomics and econometricsaccountingbusiness, management and accounting(all)discipline-based research ??
ID Code:
85884
Deposited By:
Deposited On:
10 Apr 2017 12:26
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Sep 2024 00:28