Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/202976 
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 1802
Publisher: 
Koç University-TÜSİAD Economic Research Forum (ERF), Istanbul
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.
Subjects: 
Connectedness
Vector autoregression
Time-varying parameter model
Rolling window estimation
Systemic risk
Financial institutions
JEL: 
C32
G17
G21
Document Type: 
Working Paper

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