Measuring Dynamic Connectedness with Large Bayesian VAR Models

Dimitris Korobilis and Kamil Yilmaz (kyilmaz@ku.edu.tr)

Essex Finance Centre Working Papers from University of Essex, Essex Business School

Abstract: We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz(2014)(DYCI).We show that the connectedness index from the TVP-VAR model captures abrupt turning points better than the one obtained from rolling-windows VAR estimates. As the TVP-VAR based DYCI shows more pronounced jumps during important crisis moments, it captures the intensification of tensions in financial markets more accurately and timely than the rolling-windows based DYCI. Finally, we show that the TVP-VAR based index performs better in forecasting systemic events in the American and European financial sectors as well.

Keywords: Connectedness; Vector autoregression; Time-varying parameter model; Rolling window estimation; Systemic risk; Financial institutions (search for similar items in EconPapers)
Date: 2018-01
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (54)

Downloads: (external link)
https://repository.essex.ac.uk/20937/ original version (application/pdf)

Related works:
Working Paper: Measuring Dynamic Connectedness with Large Bayesian VAR Models (2018)
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:esy:uefcwp:20937

Access Statistics for this paper

More papers in Essex Finance Centre Working Papers from University of Essex, Essex Business School Contact information at EDIRC.
Bibliographic data for series maintained by Nikolaos Vlastakis (nvlast@essex.ac.uk).