Microstructural Biases in Empirical Tests of Option Pricing Models

Abstract

This paper examines how noise in observed option prices arising from discrete prices and other microstructural frictions affects empirical tests of option models and risk-neutral density estimation. The discrete tick size alone introduces enough noise to make model comparisons difficult, especially for lower-priced stocks. We demonstrate that microstructural noise can lead to incorrect inferences in the univariate diffusion test of Bakshi, Cao, and Chen (2000), the transition density diffusion test of Ait-Sahalia (2002), and the speed-of-convergence test of Carr and Wu (2003). We also show that microstructural noise induces a bias into the implied risk-neutral moment estimators of Bakshi, Kapadia, and Madan (2003). Even in active, liquid option markets, observation error is likely to reduce significantly the power of tests, and in some cases represents an important source of bias.