A MULTIVARIATE THRESHOLD VARYING CONDITIONAL CORRELATIONS MODEL

In this article, a multivariate threshold varying conditional correlation (TVCC) model is proposed. The model extends the idea of Engle (2002) and Tse and Tsui (2002) to a threshold framework. This model retains the interpretation of the univariate threshold GARCH model and allows for dynamic conditional correlations. Techniques of model identification, estimation, and model checking are developed. Some simulation results are reported on the finite sample distribution of the maximum likelihood estimate of the TVCC model. Real examples demonstrate the asymmetric behavior of the mean and the variance in financial time series and the ability of the TVCC model to capture these phenomena.