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Bayesian Inference for the Jump-Diffusion Model with M Jumps

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  • Maciej Kostrzewski

Abstract

In this article, we propose a new class of models—jump-diffusion models with M jumps (JD(M)J). These structures generalize the discretized arithmetic Brownian motion (for logarithmic rates of return) and the Bernoulli jump-diffusion model. The aim of this article is to present Bayesian tools for estimation and comparison of JD(M)J models. Presented methodology is illustrated with two empirical studies, employing both simulated and real-world data (the S&P100 Index).

Suggested Citation

  • Maciej Kostrzewski, 2014. "Bayesian Inference for the Jump-Diffusion Model with M Jumps," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(18), pages 3955-3985, September.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:18:p:3955-3985
    DOI: 10.1080/03610926.2012.755202
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    Cited by:

    1. Maciej Kostrzewski, 2016. "Bayesian SVLEDEJ Model for Detecting Jumps in Logarithmic Growth Rates of One Month Forward Gas Contract Prices," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(3), pages 161-179, September.
    2. Tunaru, Radu & Zheng, Teng, 2017. "Parameter estimation risk in asset pricing and risk management: A Bayesian approach," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 80-93.

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