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

Min Zhu (), Yuping Song () and Xin Zheng ()
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Min Zhu: Shanghai Normal University
Yuping Song: Shanghai Normal University
Xin Zheng: Shanghai Normal University

Computational Economics, 2025, vol. 65, issue 5, No 4, 2545-2577

Abstract: 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.

Keywords: Jumps detection; Gaussian mixture distribution; Jump-GARCH model; EM algorithm (search for similar items in EconPapers)
JEL-codes: C22 C58 G15 G41 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10633-1

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