Copula Methods for Forecasting Multivariate Time Series

Andrew Patton

Chapter Chapter 16 in Handbook of Economic Forecasting, 2013, vol. 2, pp 899-960 from Elsevier

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.

Keywords: Dependence; Correlation; Tail risk; Volatility; Density forecasting (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (100)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/B9780444627315000166
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofch:2-899

DOI: 10.1016/B978-0-444-62731-5.00016-6

Access Statistics for this chapter

More chapters in Handbook of Economic Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().