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Measuring Spot Variance Spillovers when (Co)variances are Time†varying – The Case of Multivariate GARCH Models

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  • Matthias R. Fengler
  • Helmut Herwartz

Abstract

We propose global and disaggregated spillover indices that allow us to assess variance and covariance spillovers, locally in time and conditionally on time†t information. Key to our approach is the vector moving average representation of the half†vectorized ‘squared’ multivariate GARCH process of the popular BEKK model. In an empirical application to a four†dimensional system of broad asset classes (equity, fixed income, foreign exchange and commodities), we illustrate the new spillover indices at various levels of (dis)aggregation. Moreover, we demonstrate that they are informative of the value†at†risk violations of portfolios composed of the considered asset classes.

Suggested Citation

  • Matthias R. Fengler & Helmut Herwartz, 2018. "Measuring Spot Variance Spillovers when (Co)variances are Time†varying – The Case of Multivariate GARCH Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 135-159, February.
    • Handle: RePEc:bla:obuest:v:80:y:2018:i:1:p:135-159
    DOI: 10.1111/obes.12191
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    Citations

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    Cited by:

    1. Hafner, Christian M. & Herwartz, Helmut & Maxand, Simone, 2022. "Identification of structural multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 212-227.
    2. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2024. "Volatility spillovers across the spot and futures oil markets after news announcements," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    3. Helmut Herwartz & Alberto Saucedo, 2020. "Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 387-402, May.
    4. Francesco Caloia & Andrea Cipollini & Silvia Muzzioli, 2018. "On the financial connectedness of the commodity market: a replication of the Diebold and Yilmaz (2012) study," Department of Economics 0131, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    5. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers:an Application to International Credit Risk Transmission," Working Papers hal-03338209, HAL.
    6. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
    7. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2021. "Political uncertainty, COVID-19 pandemic and stock market volatility transmission," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    8. Fengler, Matthias & Polivka, Jeannine, 2021. "Proxy-identification of a structural MGARCH model for asset returns," Economics Working Paper Series 2103, University of St. Gallen, School of Economics and Political Science, revised Oct 2024.
    9. Fengler, Matthias & Polivka, Jeanine, 2022. "Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model," VfS Annual Conference 2022 (Basel): Big Data in Economics 264010, Verein für Socialpolitik / German Economic Association.
    10. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019. "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, vol. 84(C).

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