A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations
提出一种新的多元GARCH模型，在条件相关性中引入多元阈值，并开发了适用于高维数据的两步估计方法。利用美国股票和债券市场数据验证，该模型在预测条件相关性方面优于其他多元GARCH模型。
We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using U.S. stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations.