Publications [#238547] of Andrew J. Patton

Journal Articles

1. Patton, A, Copula methods for forecasting multivariate time series, vol. 2 (January, 2013), pp. 899-960, Elsevier, ISSN 1574-0706
   (last updated on 2024/08/25)

   Abstract:
   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.