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Asset Pricing in the Presence of Market Microstructure Noise

Published: October 31, 2025 | arXiv ID: 2511.00308v1

By: Peter Yegon, W. Brent Lindquist, Svetlozar T. Rachev

Potential Business Impact:

Helps predict stock prices better by understanding market "noise."

Business Areas:
Prediction Markets Financial Services

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.

Country of Origin
🇺🇸 United States

Page Count
28 pages

Category
Quantitative Finance:
Pricing of Securities