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Measuring Dynamic Connectedness with Large Bayesian VAR Models

Author

Listed:
  • Dimitris Korobilis

    (University of Essex)

  • Kamil Yilmaz

    (Koc University)

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.

Suggested Citation

  • Dimitris Korobilis & Kamil Yilmaz, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Koç University-TUSIAD Economic Research Forum Working Papers 1802, Koc University-TUSIAD Economic Research Forum.
    • Handle: RePEc:koc:wpaper:1802
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Connectedness; Vector autoregression; Time-varying parameter model; Rolling window estimation; Systemic risk; Financial institutions.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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