New approaches of the multivariate GARCH residual: application to foreign exchange rates
提出了两种过滤多元GARCH模型残差相关性的新方法，通过预测外汇汇率任务评估其效果，相比其他方法能更好地近似独立因子，避免复杂高阶依赖建模。
Two formulations are proposed to filter out correlations in the residuals of the multivariate GARCH model. The first approach estimates the correlation matrix as a parameter and transforms any joint distribution to match a specific correlation matrix. The second approach converts time series data into an uncorrelated residuals using the eigenvalue decomposition of the correlation matrix. The empirical performance of these methods is evaluated through a prediction task involving foreign exchange rates and is compared with other methodologies based on out-of-sample log-likelihood and Value at Risk. These approaches approximate the DCC-GARCH residuals with independent factors, thereby avoiding the modeling of complex higher-order dependencies.