英文标题：
《Directed Continuous-Time Random Walk with memory》
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作者：
Jaros{\\l}aw Klamut, Tomasz Gubiec
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最新提交年份：
2018
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英文摘要：
  We propose a new Directed Continuous-Time Random Walk (CTRW) model with memory. As CTRW trajectory consists of spatial jumps preceded by waiting times, in Directed CTRW, we consider the case with only positive spatial jumps. Moreover, we consider the memory in the model as each spatial jump depends on the previous one. Our model is motivated by the financial application of the CTRW presented in [Phys. Rev. E 82:046119][Eur. Phys. J. B 90:50]. As CTRW can successfully describe the short term negative autocorrelation of returns in high-frequency financial data (caused by the bid-ask bounce phenomena), we asked ourselves to what extent the observed long-term autocorrelation of absolute values of returns can be explained by the same phenomena. It turned out that the bid-ask bounce can be responsible only for the small fraction of the memory observed in the high-frequency financial data.
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中文摘要：
提出了一种新的带记忆的有向连续时间随机游动（CTRW）模型。由于CTRW轨迹由等待时间之前的空间跳跃组成，在定向CTRW中，我们考虑只有正空间跳跃的情况。此外，我们考虑模型中的记忆，因为每个空间跳跃都依赖于前一个空间跳跃。我们的模型是基于[Phys.Rev.E 82:046119][Eur.Phys.J.B 90:50]中提出的CTRW的财务应用。由于CTRW能够成功地描述高频金融数据中收益的短期负自相关（由买卖反弹现象引起），我们问自己，观察到的收益绝对值的长期自相关在多大程度上可以用相同的现象来解释。事实证明，买卖反弹只能对高频金融数据中观察到的一小部分内存负责。
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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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