The performance of popular stochastic volatility option pricing models during the subprime crisis
Thibaut Moyaert and
Mikael Petitjean
Applied Financial Economics, 2011, vol. 21, issue 14, 1059-1068
Abstract:
Using daily options prices on the Eurostoxx 50 stock index over the whole year 2008, we compare the performance of three popular Stochastic Volatility (SV) models (Heston, 1993; Bates, 1996; Heston and Nandi, 2000), in addition to the traditional Black-Scholes model and a proprietary trading desk model. We show that the most consistent in-sample and out-of-sample statistical performance is obtained for the internal model. However, the Bates model seems to be better suited to Short Term (ST, out-of-the-money) options while the Heston model seems to perform better for medium or Long Term (LT) options. In terms of hedging performance, the Heston and Nandi model exhibits the best average, albeit most volatile, result and the Heston model outperforms the Black-Scholes model in terms of hedging errors, mainly for option contracts that mature in-the-money.
Keywords: Heston; stochastic volatility; out-of-sample; delta hedge; forecasting (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/09603107.2011.562161 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:apfiec:v:21:y:2011:i:14:p:1059-1068
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAFE20
DOI: 10.1080/09603107.2011.562161
Access Statistics for this article
Applied Financial Economics is currently edited by Anita Phillips
More articles in Applied Financial Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().