We propose semi-parametric CUSUM tests to detect a change point in the correlation structures of non–linear multivariate models with dy- namically 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 appro- priate 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.
- Change point detection
- Time varying correlation structure
- Volatility processes
- Monte Carlo simulation
- Contagion effect