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Copula Methods for Forecasting Multivariate Time Series

Handbook of Economic Forecasting, 2013, Vol.2, p.899-960

2013 Elsevier B.V. ;ISSN: 1574-0706 ;ISBN: 9780444627322 ;ISBN: 0444627324 ;DOI: 10.1016/B978-0-444-62731-5.00016-6

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  • Title:
    Copula Methods for Forecasting Multivariate Time Series
  • Author: Patton, Andrew
  • Subjects: Correlation ; Density forecasting ; Dependence ; Tail risk ; Volatility
  • Is Part Of: Handbook of Economic Forecasting, 2013, Vol.2, p.899-960
  • Description: 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.
  • Publisher: Elsevier B.V
  • Language: English
  • Identifier: ISSN: 1574-0706
    ISBN: 9780444627322
    ISBN: 0444627324
    DOI: 10.1016/B978-0-444-62731-5.00016-6
  • Source: ScholarVox International
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