Original Research

Fractional Black–Scholes option pricing, volatility calibration and implied Hurst exponents in South African context

Emlyn Flint, Eben Maré
South African Journal of Economic and Management Sciences | Vol 20, No 1 | a1532 | DOI: https://doi.org/10.4102/sajems.v20i1.1532 | © 2017 Emlyn Flint, Eben Maré | This work is licensed under CC Attribution 4.0
Submitted: 26 December 2015 | Published: 29 March 2017

About the author(s)

Emlyn Flint, Department of Mathematics and Applied Mathematics, University of Pretoria, South Africa
Eben Maré, Department of Mathematics and Applied Mathematics, University of Pretoria, South Africa


Background: Contingent claims on underlying assets are typically priced under a framework that assumes, inter alia, that the log returns of the underlying asset are normally distributed. However, many researchers have shown that this assumption is violated in practice. Such violations include the statistical properties of heavy tails, volatility clustering, leptokurtosis and long memory. This paper considers the pricing of contingent claims when the underlying is assumed to display long memory, an issue that has heretofore not received much attention.

Aim: We address several theoretical and practical issues in option pricing and implied volatility calibration in a fractional Black–Scholes market. We introduce a novel eight-parameter fractional Black–Scholes-inspired (FBSI) model for the implied volatility surface, and consider in depth the issue of calibration. One of the main benefits of such a model is that it allows one to decompose implied volatility into an independent long-memory component – captured by an implied Hurst exponent – and a conditional implied volatility component. Such a decomposition has useful applications in the areas of derivatives trading, risk management, delta hedging and dynamic asset allocation.

Setting: The proposed FBSI volatility model is calibrated to South African equity index options data as well as South African Rand/American Dollar currency options data. However, given the focus on the theoretical development of the model, the results in this paper are applicable across all financial markets.

Methods: The FBSI model essentially combines a deterministic function form of the 1-year implied volatility skew with a separate deterministic function for the implied Hurst exponent, thus allowing one to model both observed implied volatility surfaces as well as decompose them into independent volatility and long-memory components respectively. Calibration of the model makes use of a quasi-explicit weighted least-squares optimisation routine.

Results: It is shown that a fractional Black–Scholes model always admits a non-constant implied volatility term structure when the Hurst exponent is not 0.5, and that 1-year implied volatility is independent of the Hurst exponent and equivalent to fractional volatility. Furthermore, we show that the FBSI model fits the equity index implied volatility data very well but that a more flexible Hurst exponent parameterisation is required to fit accurately the currency implied volatility data.

Conclusion: The FBSI model is an arbitrage-free deterministic volatility model that can accurately model equity index implied volatility. It also provides one with an estimate of the implied Hurst exponent, which could be very useful in derivatives trading and delta hedging.


fractional Brownian motion; Hurst exponent; implied volatility; calibration; equity; currency; volatility surface


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Crossref Citations

1. Options Pricing by Monte Carlo Simulation, Binomial Tree and BMS Model: a comparative study of Nifty50 options index
Ali Bendob, Naima Bentouir
Journal of Banking and Financial Economics  vol: 1/2019  issue: 11  first page: 79  year: 2019  
doi: 10.7172/2353-6845.jbfe.2019.1.4