2020/02/25 by Marius Hofert, Hofert, Marius, Avinash Prasad +3 · 2 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #00A72 #60E05 #62H99 #62M10 #65C10 #65C60 #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Stock Market Forecasting Methods #cs.LG #msc:00A72 #msc:60E05 #msc:62H99 #msc:62M10 #msc:65C10 #msc:65C60 #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.10645
openalex publication_date 2020/02/25 · arxiv created 2021/10/01 · arxiv updated 2021/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.