Bakshi is at the University of Maryland, College Park. Cao is at Pennsylvania State University, University Park. Chen is at The Ohio State University, Columbus. This paper subsumes the previous one under the title “Option Pricing and Hedging Performance with Stochastic Volatility and Stochastic Interest Rates.” We thank Sanjiv Das, Ranjan D'Mello, Jin-Chuan Duan, Helyette Geman, Eric Ghysels, Frank Hatheway, Steward Hodges, Ravi Jagannathan, Andrew Karolyi, Bill Kracaw, Dilip Madan, Victor Ng, Louis Scott, René Stulz, Stephen Taylor, Siegfried Trautmann, Alex Triantis, Alan White, and the anonymous referee. We gratefully acknowledge comments by seminar participants at the 1996 European Finance Association Meetings, 1997 Western Finance Meetings, the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, The Ohio State University, and the University of New Orleans. Any remaining errors are our responsibility alone.
Empirical Performance of Alternative Option Pricing Models
Article first published online: 18 APR 2012
1997 The American Finance Association
The Journal of Finance
Volume 52, Issue 5, pages 2003–2049, December 1997
How to Cite
Bakshi, G., Cao, C. and Chen, Z. (1997), Empirical Performance of Alternative Option Pricing Models. The Journal of Finance, 52: 2003–2049. doi: 10.1111/j.1540-6261.1997.tb02749.x
- Issue published online: 18 APR 2012
- Article first published online: 18 APR 2012
Substantial progress has been made in developing more realistic option pricing models. Empirically, however, it is not known whether and by how much each generalization improves option pricing and hedging. We fill this gap by first deriving an option model that allows volatility, interest rates and jumps to be stochastic. Using S&P 500 options, we examine several alternative models from three perspectives: (1) internal consistency of implied parameters/volatility with relevant time-series data, (2) out-of-sample pricing, and (3) hedging. Overall, incorporating stochastic volatility and jumps is important for pricing and internal consistency. But for hedging, modeling stochastic volatility alone yields the best performance.