Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models
提出半参数CUSUM检验，用于检测多元波动率模型中相关结构的变点，推导了渐近分布，模拟显示检验效果良好，并应用于识别美国向新兴市场的金融传染时点。
We propose semiparametric CUSUM tests to detect a change-point in the correlation structures of nonlinear 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 semiparametric CUSUM tests in the attempt to date the occurrence of financial contagion from the US to emerging markets worldwide during the great recession. Supplementary materials for this article are available online.