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Volatility Dynamics and Mixed Jump-GARCH Model Based Jump Detection in Financial Markets

Author

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  • Min Zhu

    (Shanghai Normal University)

  • Yuping Song

    (Shanghai Normal University)

  • Xin Zheng

    (Shanghai Normal University)

Abstract

In this paper, we introduce a novel parametric approach for detecting jumps in daily frequency data. Our jump detection method leverages the characteristics of volatility to distinguish the presence or absence of jumps. By specifying a model in terms of the mixture of GARCH and jump-GARCH, we identify jumps based on the posterior probability of states yielded by the fitted model. The EM algorithm is employed to resolve the parameters in the model. Through Monte Carlo experiments, we evaluate the performance of our parametric jump detection approach, the mixed jump-GARCH model, in comparison to an alternative test. The results indicate that our approach demonstrates superior overall performance of both sensitivity and reliability in jump detection than its benchmark models. Empirical evidence further supports these findings, particularly highlighting the mixed jump-GARCH model’s ability to identify several significant jumps associated with key events, such as the 2008 US financial crisis and the 2020 Covid-19 pandemic. Importantly, these jumps are ignored by the benchmark nonparametric test.

Suggested Citation

  • Min Zhu & Yuping Song & Xin Zheng, 2025. "Volatility Dynamics and Mixed Jump-GARCH Model Based Jump Detection in Financial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2545-2577, May.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10633-1
    DOI: 10.1007/s10614-024-10633-1
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    More about this item

    Keywords

    Jumps detection; Gaussian mixture distribution; Jump-GARCH model; EM algorithm;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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