Asset Pricing in the Presence of Market Microstructure Noise

TL;DR

This paper introduces two models to incorporate market microstructure noise into asset and option pricing, one in continuous time and one in discrete time, providing a way to quantify noise effects on volatility and drift.

Contribution

It develops two novel models that explicitly account for microstructure noise in asset pricing, extending existing frameworks to include noise impact on both volatility and drift.

Findings

Microstructure noise significantly affects volatility estimates.

Discrete model enables extraction of noise impact on drift.

Empirical examples validate the models' ability to isolate noise effects.

Abstract

We present two models for incorporating the total effect of market microstructure noise into dynamic pricing of assets and European options. The first model is developed under a Black-Scholes-Merton, continuous-time framework. The second model is a discrete, binomial tree model developed as an extension of the static Grossman-Stiglitz model. Both models are market complete, providing a unique equivalent martingale measure that establishes a unique map between parameters governing the risk-neutral and real-world price dynamics. We provide empirical examples to extract the coefficients in the model, in particular those coefficients characterizing the influence of the microstructure noise on prices. In addition to isolating the impact of noise on the volatility, the discrete model enables us to extract the noise impact on the drift coefficient. We provide evidence for the primary microstructure noise we believe our empirical examples capture.