英文标题：
《Hawkes process model with a time-dependent background rate and its
application to high-frequency financial data》
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作者：
Takahiro Omi, Yo*****o Hirata and Kazuyuki Aihara
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最新提交年份：
2017
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英文摘要：
A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method. Our model can capture not only the slow time-variation, such as in the intraday seasonality, but also the rapid one, which follows a macroeconomic news announcement. By analyzing the tick data of the Nikkei 225 mini, we find that (i) our model is better fitted to the data than the Hawkes models with a constant background rate or a slowly varying background rate, which have been commonly used in the field of quantitative finance; (ii) the improvement in the goodness-of-fit to the data by our model is significant especially for sessions where considerable fluctuation of the background rate is present; and (iii) our model is statistically consistent with the data. The branching ratio, which quantifies the level of the endogeneity of markets, estimated by our model is 0.41, suggesting the relative importance of exogenous factors in the market dynamics. We also demonstrate that it is critically important to appropriately model the time-dependent background rate for the branching ratio estimation.
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中文摘要：
为了分析高频金融数据，建立了背景率随时间变化的霍克斯过程模型。在我们的模型中，背景率的对数由一个具有相对大量可变宽度基函数的线性模型建模，参数由贝叶斯方法估计。我们的模型不仅可以捕捉到缓慢的时间变化，如日内季节性变化，还可以捕捉到宏观经济新闻发布后的快速时间变化。通过分析日经225指数的tick数据，我们发现（i）我们的模型比在定量金融领域常用的具有恒定背景率或缓慢变化背景率的霍克斯模型更适合数据；（ii）我们的模型对数据拟合优度的改善是显著的，尤其是对于背景率波动较大的会话；（iii）我们的模型与数据在统计学上是一致的。我们的模型估计的分支比率（量化市场内生性水平）为0.41，表明市场动态中外生因素的相对重要性。我们还证明，为分支比估计建立与时间相关的背景率模型至关重要。
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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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