Please Scroll down for Article Communications in Statistics -simulation and Computation Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function
Please Scroll down for Article Communications in Statistics -simulation and Computation Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function
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请向下滚动查看文章 统计通信 - 模拟和计算 通过 Copula 函数对金融数据中的时间依赖性进行统计建模 通过 Copula 函数对金融数据中的时间依赖性进行统计建模
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通讯作者：
Francesco Perri
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作者：
F. Domma;S. Giordano;Pier;Francesco Perri
这篇文章可用于研究、教学和私人学习目的。明确禁止以任何形式向任何人大量或系统地复制、再分发、转售、出借或分许可、系统地供应或分发。出版商不提供任何保证，明示或暗示或作出任何表示，内容将是完整的，准确的或最新的。任何说明书、配方和药物剂量的准确性都应通过第一手资料独立核实。对于因使用本材料而直接或间接引起的任何损失、诉讼、索赔、诉讼、要求或费用或损害赔偿，出版商概不负责。在财务分析中，研究两个或多个时间序列之间的相关性以及单变量时间序列中的时间相关性是有用的。本文研究了利用copula概念对单变量金融时间序列的依赖结构进行统计建模。我们把金融收益序列看作一阶马尔可夫过程。采用阿基米德双参数BB7联结来描述两个连续收益之间潜在的依赖结构，采用log-Dagum分布来模拟以偏度和峰度为标志的边际。通过仿真研究对最大似然估计的性能进行了评价。此外，我们将该模型应用于四只股票的日收益，最后，我们说明了当连续收益之间的依赖关系通过联结函数描述时，如何改进其对数据的拟合。
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. In financial analysis it is useful to study the dependence between two or more time series as well as the temporal dependence in a univariate time series. This article is concerned with the statistical modeling of the dependence structure in a univariate financial time series using the concept of copula. We treat the series of financial returns as a first order Markov process. The Archimedean two-parameter BB7 copula is adopted to describe the underlying dependence structure between two consecutive returns, while the log-Dagum distribution is employed to model the margins marked by skewness and kurtosis. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. Furthermore, we apply the model to the daily returns of four stocks and, finally, we illustrate how its fitting to data can be improved when the dependence between consecutive returns is described through a copula function.