Copula methods for forecasting multivariate time series

Published

Journal Article

Copula-based models provide a great deal of flexibility in modeling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to flexibility, this often also facilitates estimation of the model in stages, reducing the computational burden. This chapter reviews the growing literature on copula-based models for economic and financial time series data, and discusses in detail methods for estimation, inference, goodness-of-fit testing, and model selection that are useful when working with these models. A representative data set of two daily equity index returns is used to illustrate all of the main results. © 2013 Elsevier B.V.

Full Text

Duke Authors

Cited Authors

  • Patton, A

Published Date

  • August 21, 2013

Published In

Volume / Issue

  • 2 /

Start / End Page

  • 899 - 960

International Standard Serial Number (ISSN)

  • 1574-0706

Digital Object Identifier (DOI)

  • 10.1016/B978-0-444-62731-5.00016-6

Citation Source

  • Scopus