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Estimation of Jump-Diffusion Process vis Empirical Characteristic Function

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  • Michael Rockinger
  • Maria Semenova

Abstract

This article proposes an estimation procedure for the affine stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint (here bi-variate) unconditional characteristic function for the stochastic process for which we derive a closed form expression. The estimation of the general model and of various restrictions, on S&P 500 data, is performed using the continuous empirical characteristic function method. The estimation suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant.

Suggested Citation

  • Michael Rockinger & Maria Semenova, 2005. "Estimation of Jump-Diffusion Process vis Empirical Characteristic Function," FAME Research Paper Series rp150, International Center for Financial Asset Management and Engineering.
    • Handle: RePEc:fam:rpseri:rp150
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    File URL: http://www.swissfinanceinstitute.ch/rp150.pdf
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    References listed on IDEAS

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    Cited by:

    1. Djouadi, Seddik M. & Maroulas, Vasileios & Pan, Xiaoyang & Xiong, Jie, 2017. "Consistency and asymptotics of a Poisson intensity least-squares estimator for partially observed jump–diffusion processes," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 8-16.

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    More about this item

    Keywords

    Modeling asset prices; Affine-jump-diffusions; Characteristic functions; Stochastic volatility; Empirical estimation;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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