Arbeitspapier
Measuring dynamic connectedness with large Bayesian VAR models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 1802
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Financial Forecasting and Simulation
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- Thema
-
Connectedness
Vector autoregression
Time-varying parameter model
Rolling window estimation
Systemic risk
Financial institutions
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Korobilis, Dimitris
Yılmaz, Kamil
- Ereignis
-
Veröffentlichung
- (wer)
-
Koç University-TÜSİAD Economic Research Forum (ERF)
- (wo)
-
Istanbul
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Korobilis, Dimitris
- Yılmaz, Kamil
- Koç University-TÜSİAD Economic Research Forum (ERF)
Entstanden
- 2018