Pricing Options with Hybrid Stochastic Volatility Models

Glynis Jones () and Roman Makarov ()
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Glynis Jones: Wilfrid Laurier University, Department of Mathematics
Roman Makarov: Wilfrid Laurier University, Department of Mathematics

A chapter in Mathematical and Computational Approaches in Advancing Modern Science and Engineering, 2016, pp 549-560 from Springer

Abstract: Abstract We introduce a hybrid stochastic volatility Stochastic volatility model where the asset price process follows the Heston model and interest rates are governed by a two-factor stochastic model. Two cases are considered. First, it is assumed that interest rates and asset prices are uncorrelated. The characteristic function method is used to derive semi-analytical pricing formulae for plain vanilla options. In the second case we introduce a correlation between the asset price process and the short rate process and use Monte Carlo simulations for pricing options. To reduce the stochastic error, we implement the control variate method where an estimator of the option value for the uncorrelated case is used as a control variate. The options are priced with a varying correlation coefficient. We observe that the control variate method allows us to speed up Monte Carlo computations by a factor with the magnitude of several hundreds. The efficiency of the method is higher for smaller values of the correlation coefficient. We then study the impact a correlation between the two processes has on option prices. It has been noticed that the call option price is an increasing function of the correlation coefficient.

Keywords: Interest Rate; Option Price; Call Option; Stochastic Volatility Model; Equivalent Martingale Measure (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-30379-6_50

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DOI: 10.1007/978-3-319-30379-6_50

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