Implied Stochastic Volatility Models

• 成果类型: Article
• 署名作者: Ait-Sahalia, Yacine; Li, Chenxu; Li, Chen Xu
• 署名单位: Princeton University; National Bureau of Economic Research; Peking University; Renmin University of China
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa041
• 发表日期: 2021
• 页码: 394
• 关键词: maximum-likelihood-estimation; nonparametric-estimation; ASYMPTOTIC-EXPANSION; risk premia; options; approximation; specification; DYNAMICS; returns; FORMULA

摘要

This paper proposes implied stochastic volatility models designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.