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首页> 外文期刊>European Journal of Operational Research >Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm
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Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm

机译：测量金融时间序列之间的等级相关系数：一种基于GARCH-copula的序列比对算法

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摘要

This paper presents a novel four-stage algorithm for the measurement of the rank correlation coefficients between pairwise financial time series. In first stage returns of financial time series are fitted as skewed-t distributions by the generalized autoregressive conditional heteroscedasticity model. In the second stage, the joint probability density function (PDF) of the fitted skewed-t distributions is computed using the symmetrized Joe-Clayton copula. The joint PDF is then utilized as the scoring scheme for pairwise sequence alignment in the third stage. After solving the optimal sequence alignment problem using the dynamic programming method, we obtain the aligned pairs of the series. Finally, we compute the rank correlation coefficients of the aligned pairs in the fourth stage. To the best of our knowledge, the proposed algorithm is the first to use a sequence alignment technique to pair numerical financial time series directly, without initially transforming numerical values into symbols. Using practical financial data, the experiments illustrate the method and demonstrate the advantages of the proposed algorithm.
机译：本文提出了一种新颖的四阶段算法，用于测量成对金融时间序列之间的排名相关系数。在第一阶段，金融时间序列的收益通过广义自回归条件异方差模型拟合为偏态t分布。在第二阶段，使用对称的Joe-Clayton copula计算拟合的t分布的联合概率密度函数（PDF）。然后将联合PDF用作第三阶段中成对序列比对的评分方案。使用动态规划方法解决最佳序列比对问题后，我们获得了序列的比对。最后，我们在第四阶段计算对齐对的秩相关系数。据我们所知，该算法是第一个使用序列比对技术直接将数字金融时间序列配对的算法，而无需首先将数字值转换为符号。实验使用实际的财务数据说明了该方法，并证明了所提算法的优势。

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