Asset Pricing in the Presence of Market Friction Noise

We present two models for incorporating the total effect of market friction noise into the dynamic pricing of assets and European options. The first model is developed under a continuous-time Black–Scholes–Merton 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 and provide 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 of the model, in particular those coefficients characterizing the influence of the frictions 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 market friction that we believe our empirical examples capture.

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Publication Details

Journal
Journal of risk and financial management
Published
2026-03-26
DOI
https://doi.org/10.3390/jrfm19040243
Primary Topic
Complex Systems and Time Series Analysis
Type
article
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article

Asset Pricing in the Presence of Market Friction Noise

Peter Yegon, Svetlozar T. Rachev, W. Brent Lindquist
Journal of risk and financial management
Complex Systems and Time Series Analysis
article

Asset Pricing in the Presence of Market Friction Noise

Peter Yegon, Svetlozar T. Rachev, W. Brent Lindquist
article en

Abstract

We present two models for incorporating the total effect of market friction noise into the dynamic pricing of assets and European options. The first model is developed under a continuous-time Black–Scholes–Merton 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 and provide 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 of the model, in particular those coefficients characterizing the influence of the frictions 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 market friction that we believe our empirical examples capture.

Journal of risk and financial managementVol. 19(4)
Texas Tech University (US)
Openalex Percentile: Top 11%
Complex Systems and Time Series Analysis
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