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首页> 外文期刊>Journal of banking & finance >Estimating non-linear serial and cross-interdependence between financial assets
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Estimating non-linear serial and cross-interdependence between financial assets

机译：估计金融资产之间的非线性串联和交叉依赖

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摘要

This paper proposes an approach based on copula families to determine shape and magnitude of non-linear serial and cross-interdependence between returns and volatilities of financial assets. It is evident the predominance of the student's t copula in returns relationships. Association in tails is generally larger than the absolute. There is a fast decrease in association along time, but even after 5 days, there is still dependence between returns. For volatilities, Joe copula predominates in estimated bivariate relationships fit. Clayton copula rotated 180° (survival), Gumbel, BB6 and BB8 copulas also fit some relationships. The magnitude of lagged associations is larger for risks than returns. Persistence in the dependences is very high, and decreases very little after the first lag. The tail dependence has larger values than the absolute in most relationships. We present a practical application of the proposed approach, based on optimal investment allocation and risk prediction.
机译：本文提出了一种基于copula族的方法来确定非线性序列的形状和大小以及金融资产的收益率和波动率之间的相互依赖关系。很明显，学生的t copula在回报关系中占主导地位。尾部的联想通常大于绝对值。随着时间的流逝，联想迅速减少，但是即使在5天之后，回报之间仍然存在依存关系。对于波动率，Joe copula在估计的双变量关系拟合中占主导地位。 Clayton copula旋转了180°（存活），Gumbel，BB6和BB8 copulas也符合一些关系。对于风险而言，滞后关联的规模要大于回报。依赖性的持久性很高，并且在第一次滞后后几乎没有减少。在大多数关系中，尾部相关性的值大于绝对值。我们基于最佳的投资分配和风险预测，提出了该方法的实际应用。

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