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Testing for Self-excitation in Financial Events: A Bayesian Approach

机译：金融事件中的自激测试：贝叶斯方法

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Self-exciting temporal point processes are used to model a variety of financial event data including order flows, trades, and news. In this work, we take a Bayesian approach to inference and model comparison in self-exciting processes. We discuss strategies to compute marginal likelihood estimates for the univariate Hawkes process, and describe a Bayesian model comparison scheme. We demonstrate on currency, cryp-tocurrency and equity limit order book data that the test captures excitatory dynamics.

机译：自激励时间点过程用于对各种金融事件数据建模，包括订单流，交易和新闻。在这项工作中，我们采用贝叶斯方法对自激过程进行推理和模型比较。我们讨论了为单变量Hawkes过程计算边际似然估计的策略，并描述了贝叶斯模型比较方案。我们在货币，超短期交易和股票极限订单簿数据上证明该测试可捕捉兴奋性动态。

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《ECML PKDD 2018 Workshops》|2018年|95-102|共8页
会议地点 Dublin(IE)
作者
Ali Caner Tuerkmen; Ali Taylan Cemgil;
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作者单位

Department of Computer Engineering, Bogazici University, Bebek, 34342 Istanbul, Turkey;

Department of Computer Engineering, Bogazici University, Bebek, 34342 Istanbul, Turkey;

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正文语种 eng
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Testing for Self-excitation in Financial Events: A Bayesian Approach

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