Measuring dynamic connectedness with large Bayesian VAR models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 1802

Classification and Topics

• Classification: 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
• Subject: Connectedness ; Vector autoregression ; Time-varying parameter model ; Rolling window estimation ; Systemic risk ; Financial institutions

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Korobilis, Dimitris ; Yılmaz, Kamil
• Event: Veröffentlichung
• (who): Koç University-TÜSİAD Economic Research Forum (ERF)
• (where): Istanbul
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/202976
• Last update: 03.01.2025025, 10:42 AM CET