Estimating the parameters of stochastic volatility models using option price data

Hurn, A. Stan, Lindsay, Kenneth A., & McClelland, Andrew J. (2015) Estimating the parameters of stochastic volatility models using option price data. Journal of Business and Economic Statistics, 33(4), pp. 579-594.

[img] Accepted Version (PDF 1MB)
Administrators only until 27 April 2017 | Request a copy from author

View at publisher


This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 89591
Item Type: Journal Article
Refereed: Yes
Keywords: stochastic volatility, jumps, risk premia, maximum likelihood, particle filter
DOI: 10.1080/07350015.2014.981634
ISSN: 0735-0015
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2015 American Statistical Association
Copyright Statement: The Version of Record of this manuscript has been published and is available in Journal of Business and Economic Statistics, 27 October 2015,
Deposited On: 28 Oct 2015 02:39
Last Modified: 07 Sep 2016 00:56

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page