Nonparametric estimation of state-price densities implicit in financial asset prices

• 成果类型: Article
• 署名作者: Ait-Sahalia, Y; Lo, AW
• 署名单位: University of Chicago; National Bureau of Economic Research; Massachusetts Institute of Technology (MIT); International Business Machines (IBM); IBM USA
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/0022-1082.215228
• 发表日期: 1998
• 页码: 499-547
• 关键词: derivative securities; STOCHASTIC-PROCESSES; OPTION PRICES; valuation; models; MARKET; EFFICIENCY; volatility; tests

摘要

Implicit in the prices of traded financial assets are Arrow-Debreu prices or, with continuous states, the state-price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and we derive its asymptotic sampling theory. This estimator provides an arbitrage-free method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an asset-pricing perspective, for example, negative skewness and excess kurtosis for as set returns, and volatility smiles for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.