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MIDAS models in banking sector – systemic risk comparison

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Języki publikacji
EN
Abstrakty
EN
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.
Słowa kluczowe
Wydawca
Rocznik
Strony
165--181
Opis fizyczny
Bibliogr. 22 poz., tab., wykr.
Twórcy
autor
  • AGH University of Science and Technology in Cracow, Department of Applications of Mathematics in Economics
autor
  • University of Graz, Institute of Banking and Finance
autor
  • Jagiellonian University in Krakow, Institute of Economics, Finance and Management
Bibliografia
  • [1] Acharya, V.V., Pedersen, L.H., Philippon, T. and Richardson, M. (2010) ‘Measuring Systemic Risk’, Technical Report, Department of Finance, NYU.
  • [2] Adrian, T. and Brunnermeier, M.K. (2011) ‘CoVaR,’ FRB of New York. Staff Report No. 348.
  • [3] Amado, C. and Teräsvirta, T. (2013) ‘Modelling volatility by variance decomposition’, Journal of Econometrics, vol. 175, pp. 142–153.
  • [4] Andreou, A. and Ghysels, E. (2004) ‘The impact of sampling frequency and volatility estimators on change-point tests’, Journal of Financial Econometrics, vol. 2 (2), pp. 290–318.
  • [5] Asgharian, H., Hou, A.J. and Javed, F. (2013) ‘The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach’, Journal of Forecasting, vol. 32(7), pp. 600–612.
  • [6] Banulescu, G.D. and Dumitrescu, E.I. (2015) ‘Which Are the SIFIs? A Component Expected Shortfall Approach to Systemic Risk’, Journal of Banking and Finance, vol. 50, pp. 575–588.
  • [7] Baele, L., Bekaert, G., and Inghelbrecht, K. (2010). ‘The Determinants of Stock and Bond Return Comovements’, Review of Financial Studies, vol. 23, pp. 2374–2428.
  • [8] Bauwens, L. and Storti, G. (2009) ‘A Component GARCH Model with Time Varying Weights’, Studies in Nonlinear Dynamics & Econometrics, vol. 13, pp. 1–33.
  • [9] Benoit, S., Colletaz, G., Hurlin, C. and Perignon, C. (2013) ‘A Theoretical and Empirical Comparison of Systemic Risk Measures’, HEC Paris Research Paper No. FIN-2014-1030.
  • [10] Brownlees, C.T. and Engle, R. (2012) ‘Volatility, Correlation and Tails for Systemic Risk Measurement’, Working Paper, NYU-Stern.
  • [11] Colacito, R., Engle, R. and Ghysels, E. (2011) ‘A component model for dynamic correlations,’ Journal of Econometrics, vol. 164, pp. 45–59.
  • [12] Conrad, C. and Loch, K. (2011) ‘Anticipating Long-Term Stock Market Volatility’, Journal of Applied Econometrics, vol. 30(7), pp. 1090–1114.
  • [13] Conrad, C., Loch, K. and Rittler, D. (2014) ‘On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets’, Journal of Empirical Finance, vol. 29, pp. 26-40.
  • [14] Ding, Z., and Granger, C. (1996) Modeling volatility persistence of speculative returns: A new approach, Journal of Econometrics, vol. 73, pp. 185–215.
  • [15] Engle, R. (2002) ‘Dynamic Conditional Correlation – A Simple Class of Multivariate GARCH Models’, Journal of Business and Economic Statistics, vol. 20, pp. 339–350.
  • [16] Engle, R.F. and Rangel, J.G. (2008) ‘The Spline-GARCH Model for LowFrequency Volatility and Its Global Macroeconomic Causes’, The Review of Financial Studies, vol. 21, pp. 1187–1222.
  • [17] Engle, R.F. and Lee, G. (1999) ‘A permanent and transitory component model of stock return volatility,’ in Engle R. and White H. (eds.) Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger, Oxford University Press, pp. 475–497.
  • [18] Engle, R.F., Ghysels, E. and Sohn, B. (2013) ‘Stock market volatility and macroeconomic fundamentals’, The Review of Economics and Statistics, vol. 95(3), pp. 776–797.
  • [19] Ghysels, E., Santa-Clara, P. and Valkanov, R. (2006a) ‘Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies’, Journal of Econometrics, vol. 131, pp. 59–95.
  • [20] Ghysels, E., Sinko, A. and Valkanov, R. (2006b) ‘MIDAS Regressions: Further Results and New Directions’, Econometric Reviews, vol. 26, pp. 53–90.
  • [21] Popescu, A. and Turcu, C. (2014) ‘Systemic Sovereign Risk in Europe: an MES and CES Approach’, Revue d’économie politique, vol. 124(6), pp. 899–925.
  • [22] Scaillet, O. (2005) ‘Nonparametric Estimation of Conditional Expected Shortfall’, Insurance and Risk Management Journal, vol. 74, pp. 639–660.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d33f7606-4422-4059-a2ce-e0c2d24dc0ac
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