英文标题：
《Quantifying horizon dependence of asset prices: a cluster entropy
  approach》
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作者：
L. Ponta and A. Carbone
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最新提交年份：
2020
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英文摘要：
  Market dynamic is quantified in terms of the entropy $S(\\tau,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\\widetilde{p}_{t,n}$. The entropy $S(\\tau,n)$ is defined according to Shannon as $\\sum P(\\tau,n)\\log P(\\tau,n),$ with $P(\\tau,n)$ the probability for the cluster to occur with duration $\\tau$. \\par The investigation is performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \\& Poor 500 indexes downloaded from the Bloomberg terminal. The cluster entropy $S(\\tau,n)$ is analysed in raw and sampled data over a broad range of temporal horizons $M$ varying from one to twelve months over the year 2018. The cluster entropy $S(\\tau,n)$ is integrated over the cluster duration $\\tau$ to yield the Market Dynamic Index $I(M,n)$, a synthetic figure of price dynamics. A systematic dependence of the cluster entropy $S(\\tau,n)$ and the Market Dynamic Index $I(M,n)$ on the temporal horizon $M$ is evidenced. \\par Finally, the Market Horizon Dependence}, defined as $H(M,n)=I(M,n)-I(1,n)$, is compared with the horizon dependence of the pricing kernel with different representative agents obtained via a Kullback-Leibler entropy approach. The Market Horizon Dependence $H(M,n)$ of the three assets is compared against the values obtained by implementing the cluster entropy $S(\\tau,n)$ approach on artificially generated series (Fractional Brownian Motion).
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中文摘要：
市场动态根据价格序列$p\\t$和移动平均值$widetilde{p}{t，n}之间的交叉点形成的集群的熵$S（\\tau，n）$进行量化。Shannon将熵$S（\\tau，n）$定义为$\\和P（\\tau，n）\\log P（\\tau，n），$和$P（\\tau，n）$集群在持续时间$\\tau$内发生的概率。\\par调查是根据从彭博终端下载的纳斯达克综合指数、道琼斯工业平均指数和标准普尔500指数的高频数据进行的。在2018年一到十二个月的时间范围内，对原始和抽样数据中的聚类熵S（\\ tau，n）$进行了分析。在集群持续时间$\\tau$内，对集群熵$\\S（\\tau，n）$进行积分，得出市场动态指数$\\I（M，n）$，这是价格动态的一个综合数字。证明了集群熵$S（\\ tau，n）$和市场动态指数$I（M，n）$在时间范围$M$上的系统依赖性。\\最后，将定义为$H（M，n）=I（M，n）-I（1，n）$的市场视界依赖性}与通过Kullback-Leibler熵方法获得的具有不同代表性代理的定价核的视界依赖性进行比较。将这三种资产的市场范围依赖性$H（M，n）$与通过对人工生成的序列（分数布朗运动）实施聚类熵$S（\\ tau，n）$方法获得的值进行比较。
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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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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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