Modeling the Dependence of Conditional Correlations on Market Volatility

Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns and several studies have shown that the market volatility is a major determinant of the correlations. We extend some models to include explicitly the dependence of the correlations on the market volatility. The models differ by the way—linear or nonlinear, direct or indirect—in which the volatility influences the correlations. Using a wide set of models with two measures of market volatility on two datasets, we find that for some models, the empirical results support to some extent the statistical significance and the economic significance of the volatility effect on the correlations, but the presence of the volatility effect does not improve the forecasting performance of the extended models. Supplementary materials for this article are available online.

已有多款模型被提出，用于刻画金融收益时间序列间条件相关系数的动态变化规律；多项研究表明，市场波动率（market volatility）是相关系数的核心决定因素。本文对部分模型进行拓展，将相关系数对市场波动率的显式依赖关系纳入模型框架。不同拓展模型的差异体现在波动率影响相关系数的路径上，具体可分为线性/非线性、直接/间接等类别。本文基于两类市场波动率测度方法，在两个数据集上开展了多组模型实证分析，结果显示：对于部分模型，实证结果在一定程度上验证了波动率对相关系数的影响兼具统计显著性与经济显著性，但纳入该波动率影响并未提升拓展模型的预测性能。本文的补充材料可在线获取。