Modeling high‐dimensional time‐varying dependence using dynamic D‐vine models. (13th June 2016)

• Record Type: Journal Article
• Authors: Almeida, Carlos; Czado, Claudia; Manner, Hans
• Other Names: Yashchin Emmanuel guestEditor.
• Is Part Of: Applied stochastic models in business and industry. Volume 32:Number 5(2016:Sep./Oct.)
• Page Start: 621
• Page End: 638
• Publication Date: 2016-06-13
• Subjects: stock return dependence -- time‐varying copula -- D‐vines -- efficient importance sampling -- sequential estimation -- generalized autoregressive score; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2182
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract : We consider the problem of modeling the dependence among many time series. We build high‐dimensional time‐varying copula models by combining pair‐copula constructions with stochastic autoregressive copula and generalized autoregressive score models to capture dependence that changes over time. We show how the estimation of this highly complex model can be broken down into the estimation of a sequence of bivariate models, which can be achieved by using the method of maximum likelihood. Further, by restricting the conditional dependence parameter on higher cascades of the pair copula construction to be constant, we can greatly reduce the number of parameters to be estimated without losing much flexibility. Applications to five MSCI stock market indices and to a large dataset of daily stock returns of all constituents of the Dax 30 illustrate the usefulness of the proposed model class in‐sample and for density forecasting. Copyright © 2016 John Wiley & Sons, Ltd.