American option pricing under stochastic volatility: An empirical evaluation

被引:5
|
作者
AitSahlia F. [1 ]
Goswami M. [1 ]
Guha S. [1 ]
机构
[1] Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611
关键词
American option pricing; Approximate dynamic programming; Indirect inference; Model calibration; S&P 100 index; Stochastic volatility;
D O I
10.1007/s10287-008-0083-2
中图分类号
学科分类号
摘要
Over the past few years, model complexity in quantitative finance has increased substantially in response to earlier approaches that did not capture critical features for risk management. However, given the preponderance of the classical Black-Scholes model, it is still not clear that this increased complexity is matched by additional accuracy in the ultimate result. In particular, the last decade has witnessed a flurry of activity in modeling asset volatility, and studies evaluating different alternatives for option pricing have focused on European-style exercise. In this paper, we extend these empirical evaluations to American options, as their additional opportunity for early exercise may incorporate stochastic volatility in the pricing differently. Specifically, the present work compares the empirical pricing and hedging performance of the commonly adopted stochastic volatility model of Heston (Rev Financial Stud 6:327-343, 1993) against the traditional constant volatility benchmark of Black and Scholes (J Polit Econ 81:637-659, 1973). Using S&P 100 index options data, our study indicates that this particular stochastic volatility model offers enhancements in line with their European-style counterparts for in-the-money options. However, the most striking improvements are for out-of-the-money options, which because of early exercise are more valuable than their European-style counterparts, especially when volatility is stochastic. © 2008 Springer-Verlag.
引用
收藏
页码:189 / 206
页数:17
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