A Fractional Version of the Stochastic Alpha–Beta–Rho (SABR) Model within a Fast-varying Stochastic Environment (FVSE)

Abel ZONGO *

D´epartement de Math´ematiques, Universit´e Joseph KI-ZERBO, 03 BP 7021, Ouagadougou, Burkina Faso.

Ywo Josue BAZIE

D´epartement de Math´ematiques, Universit´e Joseph KI-ZERBO, 03 BP 7021, Ouagadougou, Burkina Faso.

Raogo Frank Emile 1er Jumeau KABORE

D´epartement de Math´ematiques, Universit´e Joseph KI-ZERBO, 03 BP 7021, Ouagadougou, Burkina Faso.

S. Pierre Clovis NITIEMA

´epartement des Math´ematiques de D´ecision, Universit´e Thomas SANKARA, 12 BP 417, Ouagadougou, Burkina Faso.

*Author to whom correspondence should be addressed.


Abstract

This paper proposes a fractional extension of the SABR model that better captures the irregular behavior of volatility, particularly its long-memory and rough characteristics. Relying on empirical evidence showing that log-volatility follows a fractional Brownian motion dynamics with a low Hurst exponent, and embedding volatility within a fast-varying stochastic environment, the model is formulated through a fractional stochastic differential equation combining fractional dynamics with high-frequency stochastic drivers. This approach improves the modeling of implied volatility surfaces (smile and skew) while preserving analytical tractability for practical applications such as calibration and options pricing. The proposed fractional SABR model thus proves to be robust in capturing volatility smiles and realistic in accounting for long-memory volatility dynamics, offering a promising framework for market calibration, volatility forecasting, and further theoretical or numerical developments.

Keywords: SABR model, Bessel process, stochastic volatility, fractional Brownian motion, volatility smile


How to Cite

ZONGO, Abel, Ywo Josue BAZIE, Raogo Frank Emile 1er Jumeau KABORE, and S. Pierre Clovis NITIEMA. 2025. “A Fractional Version of the Stochastic Alpha–Beta–Rho (SABR) Model Within a Fast-Varying Stochastic Environment (FVSE)”. Asian Journal of Probability and Statistics 27 (9):10-27. https://doi.org/10.9734/ajpas/2025/v27i9800.

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