Option Pricing in Stochastic Volatility Models Driven by Fractional Jump-Diffusion Processes

Omid Jenabi and Nazar Dahmardeh Ghale No

International Journal of Finance, Insurance and Risk Management, 2018, vol. 8, issue 1, 1374

Abstract: In this paper, we propose a fractional stochastic volatility jump-diffusion model which extends the Bates (1996) model, where we model the volatility as a fractional process. Extensive empirical studies show that the distributions of the logarithmic returns of financial asset usually exhibit properties of self-similarity and long-range dependence and since the fractional Brownian motion has these two important properties, it has the ability to capture the behavior of underlying asset price. Further incorporating jumps into the stochastic volatility framework gives further freedom to financial mathematicians to fit both the short and long end of the implied volatility surface. We propose a stochastic model which contains both fractional and jump process. Then we price options using Monte Carlo simulations along with a variance reduction technique (antithetic variates). We use market data from the S&P 500 index and we compare our results with the Heston and Bates model using error measures. The results show our model greatly outperforms previous models in terms of estimation accuracy.

Keywords: Hurst exponent; Jump-Diffusion; Fractional stochastic volatility model; Option pricing; Long-range dependence. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:ers:ijfirm:v:8:y:2018:i:1:p:1374

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