Pricing Taiwan option market with GARCH and stochastic volatility

Hung-Hsi Huang, Ching-Ping Wang and Shiau-Hung Chen

Applied Financial Economics, 2011, vol. 21, issue 10, 747-754

Abstract: This study compares the out-of-sample performances among Black-Scholes (B-S), Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in the Taiwan option market. Using Absolute Relative Pricing Error (ARPE) as the performance criterion, the empirical result reveals that the performance for GARCH is the best, and SV slightly dominates B-S. Additionally, this study performs the regression of ARPE on time-to-maturity, moneyness and a binary variable that is set to unity, if the option is a call and to zero in the case of a put. For the three models, the regression result displays that the pricing error is consistently decreasing in time-to-maturity and moneyness, and the out-of-sample performance in puts are more accurate than those in calls. Since the corresponding R2 of the regression in GARCH is the smallest, the pricing error for the other two models is relatively severe with respect to the three explanatory variables.

Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/09603107.2010.535786 (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:10:p:747-754

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAFE20

DOI: 10.1080/09603107.2010.535786

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 ().