Implied Stochastic Volatility Models

被引：17

作者：

Ait-Sahalia, Yacine ^{[1
,2
]}

Li, Chenxu ^{[3
]}

Li, Chen Xu ^{[4
]}

机构：

[1] Princeton Univ, Dept Econ, Princeton, NJ 08544 USA

[2] NBER, Cambridge, MA 02138 USA

[3] Peking Univ, Guanghua Sch Management, Beijing, Peoples R China

[4] Renmin Univ China, Sch Business, Beijing 100872, Peoples R China

来源：

REVIEW OF FINANCIAL STUDIES | 2021年 / 34卷 / 01期

基金：

中国国家自然科学基金;

关键词：

MAXIMUM-LIKELIHOOD-ESTIMATION; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC-EXPANSION; RISK PREMIA; OPTIONS; APPROXIMATION; SPECIFICATION; DYNAMICS; RETURNS; FORMULA;

D O I：

10.1093/rfs/hhaa041

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

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.

引用

页码：394 / 450

页数：57

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