Multivariate range-based EGARCH models
基于动态条件相关和共范围框架，构建了两种新的多元基于范围的EGARCH模型，在货币和ETF数据上，这些模型在预测方差协方差矩阵和构建最小方差投资组合方面优于现有模型。
The dynamic conditional correlation (DCC) and co-range models are two main frameworks used to incorporate range-based univariate volatility. Using the two approaches, we construct novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, and a co-range CARR (CRCARR) model. We compare these models with five existing models over twelve forecast horizons, ranging from one to twelve weeks, covering currencies and ETFs. Among the eight models, the DCC-REGARCH and CRREGARCH models show the best performance in out-of-sample forecasting of the variance-covariance matrix across a range of market conditions and forecast horizons. These models also generate the lowest variance and turnover for global minimum-variance (GMV) portfolios in the majority of cases. • Two multivariate REGARCH models are built based on the DCC and corange frameworks. • Two data sets, currencies and ETFs are employed to assess the developed models. • The two models are evaluated over twelve forecasting horizons. • The developed models outperform the competing range-based models.