摘要翻译：
利用半马尔可夫方法，通过一个收益模型研究了交易股票的高频价格动态。更准确地说，我们假设日内收益率用离散时间齐次半马尔可夫过程描述，隔夜收益率用马尔可夫链建模。在此基础上，推导了第一次通过时间分布方程和波动率自相关释放函数。理论结果与实际数据的经验结果进行了比较。特别是，我们分析了从2007年1月1日到2010年12月底意大利股市的高频数据。半马尔可夫假设也通过假设的非参数检验得到检验。
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英文标题：
《A semi-Markov model for price returns》
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作者：
Guglielmo D'Amico and Filippo Petroni
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最新提交年份：
2011
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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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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
  We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday return are described by a discrete time homogeneous semi-Markov process and the overnight returns are modeled by a Markov chain. Based on this assumptions we derived the equations for the first passage time distribution and the volatility autocorreletion function. Theoretical results have been compared with empirical findings from real data. In particular we analyzed high frequency data from the Italian stock market from first of January 2007 until end of December 2010. The semi-Markov hypothesis is also tested through a nonparametric test of hypothesis.
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PDF链接：
https://arxiv.org/pdf/1103.6143