IDEAS home Printed from https://ideas.repec.org/p/koc/wpaper/1802.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1802.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    4. repec:oup:jfinec:v:14:y:2016:i:1:p:81-127. is not listed on IDEAS
    5. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    6. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    7. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    8. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    9. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    10. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    11. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    12. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    13. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    14. Uhlig, Harald, 1994. "What Macroeconomists Should Know about Unit Roots: A Bayesian Perspective," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 645-671, August.
    15. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
    16. Ivailo Arsov & Elie Canetti & Laura E. Kodres & Srobona Mitra, 2013. "Near-Coincident Indicators of Systemic Stress," IMF Working Papers 13/115, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:koc:wpaper:1802. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sumru Oz) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/dekoctr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.