Nonparametric option pricing under shape restrictions

被引:160
作者
Aït-Sahalia, Y [1 ]
Duarte, J
机构
[1] Princeton Univ, Dept Econ, Princeton, NJ 08544 USA
[2] NBER, Princeton, NJ 08544 USA
[3] Univ Washington, Dept Finance & Business Econ, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
state price density; kernel; local polynomials; regression; constraints; monotonicity; convexity; STATE-CONTINGENT CLAIMS; DERIVATIVE SECURITIES; STOCHASTIC-PROCESSES; CHOICE MODELS; ASSET PRICES; REGRESSION; MONOTONE; VALUATION; CONSISTENCY; IMPLICIT;
D O I
10.1016/S0304-4076(03)00102-7
中图分类号
F [经济];
学科分类号
02 ;
摘要
Frequently, economic theory places shape restrictions on functional relationships between economic variables. This paper develops a method to constrain the values of the first and second derivatives of nonparametric locally polynomial estimators. We apply this technique to estimate the state price density (SPD), or risk-neutral density, implicit in the market prices of options. The option pricing function must be monotonic and convex. Simulations demonstrate that nonparametric estimates can be quite feasible in the small samples relevant for day-to-day option pricing, once appropriate theory-motivated shape restrictions are imposed. Using S&P 500 option prices, we show that unconstrained nonparametric estimators violate the constraints during more than half the trading days in 1999, unlike the constrained estimator we propose. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:9 / 47
页数:39
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