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

Dieses Objekt wird bereitgestellt von:
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

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