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McMC estimation of multiscale stochastic volatility models with applications

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  • Han, Chuan-Hsiang
  • Molina, German
  • Fouque, Jean-Pierre

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.

Suggested Citation

  • Han, Chuan-Hsiang & Molina, German & Fouque, Jean-Pierre, 2014. "McMC estimation of multiscale stochastic volatility models with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 103(C), pages 1-11.
    • Handle: RePEc:eee:matcom:v:103:y:2014:i:c:p:1-11
    DOI: 10.1016/j.matcom.2013.07.005
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    References listed on IDEAS

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    1. Fouque,Jean-Pierre & Papanicolaou,George & Sircar,Ronnie & Sølna,Knut, 2011. "Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives," Cambridge Books, Cambridge University Press, number 9780521843584.
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    Cited by:

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    3. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
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    6. J.-P. Fouque & Y. F. Saporito, 2018. "Heston stochastic vol-of-vol model for joint calibration of VIX and S&P 500 options," Quantitative Finance, Taylor & Francis Journals, vol. 18(6), pages 1003-1016, June.
    7. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    8. Min-Ku LEE & Sung-Jin YANG, PhD & Jeong-Hoon KIM, 2017. "Pricing Vulnerable Options with Constant Elasticity of Variance versus Stochastic Elasticity of Variance," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 233-247.
    9. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.

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