Record Details

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

The International Journal of Latest Trends in Finance and Economic Sciences

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Field Value
 
Title Option Pricing in Stochastic Volatility Models Driven by Fractional Jump-Diffusion Processes
 
Creator Jenabi, Omid; Sistan and Baluchestan University
Dahmardeh Ghale No, Nazar; Sistan and Baluchestan University
 
Subject —
Hurst exponent; Jump-Diffusion; Fractional stochastic volatility model; Option pricing; Long-range dependence
 
Description 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.
 
Publisher International Journal of Latest Trends in Finance and Economic Sciences
 
Contributor —
 
Date 2018-10-25
 
Type —
 
Format application/pdf
 
Identifier http://ojs.excelingtech.co.uk/index.php/IJLTFES/article/view/JD
10.2047/ijltfesvol8iss1-1374-1385
 
Source International Journal of Latest Trends in Finance and Economic Sciences; Vol 8, No 1 (2018): June; 1374-1385
 
Language en
 
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