Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following the popular copula-GARCH approach for modeling dependent MTS data, a framework based on a GMMN-GARCH approach is presented. First, ARMA-GARCH models are utilized to capture the serial dependence within each univariate marginal time series. Second, if the number of marginal time series is large, principal component analysis (PCA) is used as a dimension-reduction step. Last, the remaining cross-sectional dependence is modeled via a GMMN, the main contribution of this work. GMMNs are highly flexible and easy to simulate from, which is a major advantage over the copula-GARCH approach. Applications involving yield curve modeling and the analysis of foreign exchange-rate returns demonstrate the utility of the GMMN-GARCH approach, especially in terms of producing better empirical predictive distributions and making better probabilistic forecasts.
翻译:生成瞬时匹配网络(GMMNs)是多变时间序列联合创新分布的依附模式。按照流行的千兆赫-加沙赫(GARCH)方法来模拟依赖性多边贸易体系数据,介绍了基于GMN-加沙赫(GMN-GARCH)方法的框架。首先,ARMA-加沙赫(GMMN-GARCH)模型用来在每个单象形边际时间序列中捕捉序列依赖性。第二,如果边际时间序列的数量很大,则主要组成部分分析(PCA)用作一个减少维度的步骤。最后,其余的跨部门依赖性通过GMN(GM)模型来模拟,这是这项工作的主要贡献。GMNM(GM)非常灵活,而且易于模拟,这是Copula-加沙赫(GARCH)方法的一大优势。涉及产量曲线建模和分析外汇回报的应用证明了GMN-加沙赫(GMN-GRCH)方法的效用,特别是在产生更好的实证预测分布和作出更好的预测性预测方面。