High-Frequency Market Manipulation Detection with a Markov-modulated Hawkes process
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917915059552256 |
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| author | Fabre, Timothée Toke, Ioane Muni |
| author_facet | Fabre, Timothée Toke, Ioane Muni |
| contents | This work focuses on a self-exciting point process defined by a Hawkes-like intensity and a switching mechanism based on a hidden Markov chain. Previous works in such a setting assume constant intensities between consecutive events. We extend the model to general Hawkes excitation kernels that are piecewise constant between events. We develop an expectation-maximization algorithm for the statistical inference of the Hawkes intensities parameters as well as the state transition probabilities. The numerical convergence of the estimators is extensively tested on simulated data. Using high-frequency cryptocurrency data on a top centralized exchange, we apply the model to the detection of anomalous bursts of trades. We benchmark the goodness-of-fit of the model with the Markov-modulated Poisson process and demonstrate the relevance of the model in detecting suspicious activities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Frequency Market Manipulation Detection with a Markov-modulated Hawkes process Fabre, Timothée Toke, Ioane Muni Methodology Statistical Finance Trading and Market Microstructure This work focuses on a self-exciting point process defined by a Hawkes-like intensity and a switching mechanism based on a hidden Markov chain. Previous works in such a setting assume constant intensities between consecutive events. We extend the model to general Hawkes excitation kernels that are piecewise constant between events. We develop an expectation-maximization algorithm for the statistical inference of the Hawkes intensities parameters as well as the state transition probabilities. The numerical convergence of the estimators is extensively tested on simulated data. Using high-frequency cryptocurrency data on a top centralized exchange, we apply the model to the detection of anomalous bursts of trades. We benchmark the goodness-of-fit of the model with the Markov-modulated Poisson process and demonstrate the relevance of the model in detecting suspicious activities. |
| title | High-Frequency Market Manipulation Detection with a Markov-modulated Hawkes process |
| topic | Methodology Statistical Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2502.04027 |