McMC estimation of multiscale stochastic volatility models with applications

Chuan-Hsiang Han (), German Molina and Jean-Pierre Fouque

Mathematics and Computers in Simulation (MATCOM), 2014, vol. 103, issue C, 1-11

Abstract: In this paper we propose to use Markov chain Monte Carlo methods to estimate the parameters of stochastic volatility models with several factors varying at different time scales. The originality of our approach, in contrast with classical factor models is the identification of two factors driving univariate series at well-separated time scales. This is tested with simulated data as well as foreign exchange data. Furthermore, we exploit the model calibration problem of implied volatility surface by postulating a computational scheme, which consists of McMC estimation and variance reduction techniques in MC/QMC simulations for option evaluation under multi-scale stochastic volatility models. Empirical studies and its extension are discussed.

Keywords: Time scales in volatility; Markov chain Monte Carlo; Multifactor model; Implied volatility surface; Model calibration (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:103:y:2014:i:c:p:1-11

DOI: 10.1016/j.matcom.2013.07.005

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