摘要翻译:
我们研究属于意大利证券交易所(Borsa Italiana)富时MIB指数的逐条财务回报。我们可以确认以前检测到的非平稳性。然而,在以前的文献中对其他高频金融数据的缩放性质只近似有效。作为实证分析的结果,我们提出了一种基于非齐次正态复合泊松过程的描述非平稳收益的简单方法。我们用实证结果对该模型进行了检验,结果表明该模型能够近似再现高频金融时间序列的几个程式化事实。在此基础上,利用蒙特卡罗模拟方法,利用Akaike信息准则(AIC)、贝叶斯信息准则(BIC)和Hannan-Quinn信息准则(HQ)对该模型类的顺序选择进行了分析。为了进行比较,我们还对ACD(自回归条件持续时间)模型进行了类似的蒙特卡罗实验。我们的结果表明,对于复合泊松型模型,信息准则对小参数数的选择效果最好,而对于ACD模型,模型选择过程在某些情况下效果不佳。
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英文标题:
《Modeling non-stationarities in high-frequency financial time series》
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作者:
Linda Ponta, Mailan Trinh, Marco Raberto, Enrico Scalas, Silvano
Cincotti
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最新提交年份:
2017
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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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英文摘要:
We study tick-by-tick financial returns belonging to the FTSE MIB index of the Italian Stock Exchange (Borsa Italiana). We can confirm previously detected non-stationarities. However, scaling properties reported in the previous literature for other high-frequency financial data are only approximately valid. As a consequence of the empirical analyses, we propose a simple method for describing non-stationary returns, based on a non-homogeneous normal compound Poisson process. We test this model against the empirical findings and it turns out that the model can approximately reproduce several stylized facts of high-frequency financial time series. Moreover, using Monte Carlo simulations, we analyze order selection for this model class using three information criteria: Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan-Quinn information criterion (HQ). For comparison, we also perform a similar Monte Carlo experiment for the ACD (autoregressive conditional duration) model. Our results show that the information criteria work best for small parameter numbers for the compound Poisson type models, whereas for the ACD model the model selection procedure does not work well in certain cases.
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PDF链接:
https://arxiv.org/pdf/1212.0479