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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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    Cited by:

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    2. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    3. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers ECARES 2019-09, ULB -- Universite Libre de Bruxelles.
    4. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    5. Barigozzi, M. & Hallin, M. & Soccorsi, S. & Von Sachs, R., 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," LIDAM Discussion Papers ISBA 2019024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Eddie Gerba & Danilo Leiva-Leon, 2020. "Macro-financial interactions in a changing world," Working Papers 2018, Banco de España;Working Papers Homepage.
    7. Boeckelmann Lukas & Stalla-Bourdillon Arthur, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    8. So Jung Hwang & Hyunduk Suh, 2018. "Analyzing Dynamic Connectedness in Korean Housing Markets," Inha University IBER Working Paper Series 2018-4, Inha University, Institute of Business and Economic Research.
    9. Y'erali Gandica & Sophie B'ereau & Jean-Yves Gnabo, 2019. "A multilevel analysis to systemic exposure: insights from local and system-wide information," Papers 1910.08611, arXiv.org.
    10. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    11. Manel Youssef & Khaled Mokni & Ahdi Noomen Ajmi, 2021. "Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    12. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    13. Sowmya Subramaniam & David Gabauer & Rangan Gupta, 2018. "On the Transmission Mechanism of Asia-Pacific Yield Curve Characteristics," Working Papers 201864, University of Pretoria, Department of Economics.
    14. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    15. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    16. Wang, Peiwan & Zong, Lu, 2020. "Contagion effects and risk transmission channels in the housing, stock, interest rate and currency markets: An Empirical Study in China and the U.S," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    17. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    18. Marius Cristian Acatrinei, 2020. "Spillover index for European business cycle," Journal of Financial Studies, Institute of Financial Studies, vol. 5(9), pages 49-57, November.

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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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