英文标题：
《Apparent criticality and calibration issues in the Hawkes self-excited
  point process model: application to high-frequency financial data》
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作者：
Vladimir Filimonov, Didier Sornette
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最新提交年份：
2014
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英文摘要：
  We present a careful analysis of possible issues on the application of the self-excited Hawkes process to high-frequency financial data. We carefully analyze a set of effects leading to significant biases in the estimation of the \"criticality index\" n that quantifies the degree of endogeneity of how much past events trigger future events. We report a number of model biases that are intrinsic to the estimation of brnaching ratio (n) when using power law memory kernels. We demonstrate that the calibration of the Hawkes process on mixtures of pure Poisson process with changes of regime leads to completely spurious apparent critical values for the branching ratio (n~1) while the true value is actually n=0. More generally, regime shifts on the parameters of the Hawkes model and/or on the generating process itself are shown to systematically lead to a significant upward bias in the estimation of the branching ratio. We also demonstrate the importance of the preparation of the high-frequency financial data and give special care to the decrease of quality of the timestamps of tick data due to latency and grouping of messages to packets by the stock exchange. Altogether, our careful exploration of the caveats of the calibration of the Hawkes process stresses the need for considering all the above issues before any conclusion can be sustained. In this respect, because the above effects are plaguing their analyses, the claim by Hardiman, Bercot and Bouchaud (2013) that financial market have been continuously functioning at or close to criticality (n~1) cannot be supported. In contrast, our previous results on E-mini S&P 500 Futures Contracts and on major commodity future contracts are upheld.
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中文摘要：
我们对自激霍克斯过程应用于高频金融数据可能存在的问题进行了仔细分析。我们仔细分析了一系列影响，这些影响导致了“临界指数”n估计中的重大偏差，该指数量化了过去事件触发未来事件的内生性程度。我们报告了一些模型偏差，这些偏差是在使用幂律记忆核时，对brnaching比率（n）的估计所固有的。我们证明，在纯泊松过程的混合物上，随着区域的变化，霍克斯过程的校准会导致分支比（n~1）的完全虚假的表观临界值，而真实值实际上是n=0。更一般地说，霍克斯模型参数和/或生成过程本身的状态变化表明，系统性地导致分支比估计的显著向上偏差。我们还展示了准备高频金融数据的重要性，并特别注意由于延迟和证券交易所将消息分组到数据包而导致的滴答数据时间戳质量下降。总之，我们对霍克斯过程校准注意事项的仔细探索强调，在得出任何结论之前，需要考虑所有上述问题。在这方面，由于上述影响困扰着他们的分析，Hardiman、Bercot和Bouchaud（2013）关于金融市场一直在或接近临界状态（n~1）下持续运行的说法无法得到支持。相比之下，我们之前关于E-mini标准普尔500指数期货合约和主要大宗商品期货合约的结果得到了支持。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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