Robust parametric tests of constant conditional correlation in a MGARCH model
研究了多元GARCH模型中恒定条件相关性假设的检验方法，提出了对非正态性稳健的新检验，蒙特卡洛模拟显示其具有良好的统计性质。
This article provides a rigorous asymptotic treatment of new and existing asymptotically valid conditional moment (CM) testing procedures of the constant conditional correlation (CCC) assumption in a multivariate GARCH model. Full and partial quasi maximum likelihood estimation (QMLE) frameworks are considered, as is the robustness of these tests to non-normality. In particular, the asymptotic validity of the LM procedure proposed by Tse (2000 Tse, Y. K. (2000). A test for constant correlations in a multivariate GARCH model. Journal of Econometrics 98 (1):107–127.[Crossref], [Web of Science ®] , [Google Scholar]) is analyzed, and new asymptotically robust versions of this test are proposed for both estimation frameworks. A Monte Carlo study suggests that a robust Tse test procedure exhibits good size and power properties, unlike the original variant which exhibits size distortion under non-normality.