Measuring Dynamic Connectedness with Large Bayesian VAR Models
Dimitris Korobilis and
Kamil Yilmaz ()
Koç University-TUSIAD Economic Research Forum Working Papers from Koc University-TUSIAD Economic Research Forum
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.
Keywords: Connectedness; Vector autoregression; Time-varying parameter model; Rolling window estimation; Systemic risk; Financial institutions. (search for similar items in EconPapers)
JEL-codes: C32 G17 G21 (search for similar items in EconPapers)
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Working Paper: Measuring Dynamic Connectedness with Large Bayesian VAR Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:koc:wpaper:1802
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