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MIDAS models in banking sector – systemic risk comparison

Author

Listed:
  • Henryk Gurgul

    () (AGH University of Science and Technology in Cracow, Department of Applications of Mathematics in Economics)

  • Roland Mestel

    () (University of Graz, Institute of Banking and Finance)

  • Robert Syrek

    () (Jagiellonian University in Krakow, Institute of Economics, Finance and Management)

Abstract

This paper shows the application of MIDAS based models in systemic risk assessment in banking sector. We consider two popular measures of systemic risk i.e. Marginal Expected Shortfall and Delta Conditional Value at Risk. The GARCH-MIDAS model is used in modelling conditional volatilities. The long-run component is modeled using realized volatility. The conditional correlation, second step of modelling, is described with DCC-MIDAS model. This is novel approach in respect to classical TARCH and DCC modelling. Whereas the information contained in macroeconomic variables, if available, can help to predict short and long-term components, this is the promising option in improvement of systemic risk assessment.

Suggested Citation

  • Henryk Gurgul & Roland Mestel & Robert Syrek, 2017. "MIDAS models in banking sector – systemic risk comparison," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 18(2), pages 165-181.
  • Handle: RePEc:agh:journl:v:18:y:2017:i:2:p:165-181
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    References listed on IDEAS

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    1. 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.
    2. Alexandra Popescu & Camelia Turcu, 2014. "Systemic Sovereign Risk in Europe: an MES and CES Approach," Revue d'économie politique, Dalloz, vol. 124(6), pages 899-925.
    3. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    4. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    5. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    6. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    7. Sylvain Benoit & Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2013. "A Theoretical and Empirical Comparison of Systemic Risk Measures," Working Papers halshs-00746272, HAL.
    8. Banulescu, Georgiana-Denisa & Dumitrescu, Elena-Ivona, 2015. "Which are the SIFIs? A Component Expected Shortfall approach to systemic risk," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 575-588.
    9. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 290-318.
    10. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    11. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    12. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    13. Amado, Cristina & Teräsvirta, Timo, 2013. "Modelling volatility by variance decomposition," Journal of Econometrics, Elsevier, vol. 175(2), pages 142-153.
    14. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
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    Keywords

    systemic risk measures; GARCH-MIDAS; DCC-MIDAS;

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