Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models

We propose semi-parametric CUSUM tests to detect a change point in the correlation structures of non–linear multivariate models with dynamically evolving volatilities. The asymptotic distributions of the proposed statistics are derived under mild conditions. We discuss the applicability of our method to the most often used models, including constant conditional correlation (CCC), dynamic conditional correlation (DCC), BEKK, corrected DCC and factor models. Our simulations show that, our tests have good size and power properties. Also, even though the near–unit root property distorts the size and power of tests, de–volatizing the data by means of appropriate multivariate volatility models can correct such distortions. We apply the semi–parametric CUSUM tests in the attempt to date the occurrence of financial contagion from the U.S. to emerging markets worldwide during the great recession.

本文提出半参数CUSUM检验（semi-parametric CUSUM tests），用以检测带有动态演化波动率的非线性多元模型的相关结构变点。所提统计量的渐近分布可在宽松条件下推导获得。本文探讨了所提方法在主流常用模型中的适用性，涵盖常数条件相关（Constant Conditional Correlation, CCC）模型、动态条件相关（Dynamic Conditional Correlation, DCC）模型、BEKK模型、修正版DCC模型以及因子模型。模拟实验结果显示，所提检验具备良好的检验水平与功效表现。此外，即便近单位根特性会对检验的水平与功效造成扭曲，借助合适的多元波动率模型对数据进行波动率标准化处理，仍可修正此类偏差。本文将半参数CUSUM检验应用于大衰退期间美国向全球新兴市场传导金融传染的发生时点识别任务。