Bayesian Joint Estimation of the Hurst Parameter and Volatility with Applications to Fractional Option Pricing

Fractional Brownian motion has been widely used in financial modeling to capture long-range dependence and persistent behavior observed in asset dynamics. In the fractional Black--Scholes framework, accurate estimation of the Hurst parameter is essential, since estimation uncertainty can directly affect option pricing results. In this paper, we propose a Bayesian framework for joint inference on the Hurst parameter and volatility in fractional stochastic differential equation models. In contrast to approaches based solely on point estimation, the proposed method propagates posterior uncertainty directly into option pricing distributions under the fractional Black--Scholes model.
Simulation studies are conducted across multiple values of the Hurst parameter and sample sizes to evaluate estimation accuracy, posterior coverage, and pricing uncertainty. The results demonstrate stable posterior inference and coherent uncertainty quantification for both model parameters and option prices. The methodology is further illustrated using WTI crude oil and natural gas data under different market regimes. The empirical analysis indicates that differences in market behavior are driven primarily by changes in volatility rather than strong long-range dependence, while posterior option price distributions reflect substantial variation in pricing uncertainty across regimes. These findings highlight the importance of incorporating joint parameter uncertainty in fractional financial models and demonstrate the practical value of Bayesian methods for option pricing applications.