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Measuring Dynamic Connectedness with Large Bayesian VAR Models

Korobilis, D and Yilmaz, K (2018) Measuring Dynamic Connectedness with Large Bayesian VAR Models. Working Paper. Essex Finance Centre Working Papers, Colchester. (Unpublished)

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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.

Item Type: Monograph (Working Paper)
Uncontrolled Keywords: Connectedness, Vector autoregression, Time-varying parameter model, Rolling window estimation, Systemic risk, Financial institutions
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 02 Jan 2018 11:37
Last Modified: 07 Aug 2019 21:15
URI: http://repository.essex.ac.uk/id/eprint/20937

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