A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation

• 成果类型: Article
• 署名作者: Chernov, M; Ghysels, E
• 署名单位: Columbia University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/S0304-405X(00)00046-5
• 发表日期: 2000
• 页码: 407-458
• 关键词: derivative securities; efficient method of moments; state price densities; stochastic volatility models; Filtering

摘要

The purpose of this paper is to bridge two strands of the literature, one pertaining to the objective or physical measure used to model an underlying asset and the other pertaining to the risk-neutral measure used to price derivatives. We propose a generic procedure using simultaneously the fundamental price, S-t, and a set of option contracts [(sigma(it)(I))(i=1,m)] where m greater than or equal to 1 and sigma(it)(I) is the Black-Scholes implied volatility. We use Heston's (1993. Review of Financial Studies 6, 327-343) model as an example, and appraise univariate and multivariate estimation of the model in terms of pricing and hedging performance. Our results, based on the S&P 500 index contract, show dominance of univariate approach, which relies solely on options data. A by-product of this finding is that we uncover a remarkably simple volatility extraction filter based on a polynomial lag structure of implied volatilities. The bivariate approach, involving both the fundamental security and an option contract, appears useful when the information from the cash market reflected in the conditional kurtosis provides support to price long term. (C) 2000 Elsevier Science S.A. All rights reserved.