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Time-Varying Betas of Banking Sectors

This paper analyzes the evolution of the systematic risk of the banking industries in eight advanced countries using weekly data from 1990 to 2012. Time-varying betas are estimated by means of a Bayesian state-space model with stochastic volatility, whose results are contrasted with those of the standard M-GARCH and rolling-regression models. We show that both country-specific and global events affect the perceived systematic risk, while the impact of the latter differs considerably across countries. Finally, our results do not support the previous findings that the systematic risk of the banking sector was underestimated before the last financial crisis.

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Article provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.

Volume (Year): 62 (2012)
Issue (Month): 6 (December)
Pages: 485-504

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Handle: RePEc:fau:fauart:v:62:y:2012:i:6:p:485-504
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  1. Michelle L. Barnes & Jose Lopez, 2005. "Alternative measures of the Federal Reserve banks' cost of equity capital," Working Paper Series 2005-06, Federal Reserve Bank of San Francisco.
  2. Buch, Claudia M. & Koch, Cathérine Tahmee & Koetter, Michael, 2009. "Margins of international banking: is there a productivity pecking order in banking, too?," Discussion Paper Series 2: Banking and Financial Studies 2009,12, Deutsche Bundesbank, Research Centre.
  3. Sascha Mergner & Jan Bulla, 2008. "Time-varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques," The European Journal of Finance, Taylor & Francis Journals, vol. 14(8), pages 771-802.
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  5. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-31, February.
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  7. Nicola Cetorelli & Linda S. Goldberg, 2012. "Follow the money: quantifying domestic effects of foreign bank shocks in the Great Recession," Staff Reports 545, Federal Reserve Bank of New York.
  8. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  9. Milan Rippel & Ivo Jánský, 2011. "Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility," Working Papers IES 2011/27, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2011.
  10. Lie, Frida & Brooks, Robert & Faff, Robert, 2000. "Modelling the Equity Beta Risk of Australian Financial Sector Companies," Australian Economic Papers, Wiley Blackwell, vol. 39(3), pages 301-11, September.
  11. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
  12. Garratt, Rodney & Mahadeva, Lavan & Svirydzenka, Katsiaryna, 2011. "Mapping systemic risk in the international banking network," Bank of England working papers 413, Bank of England.
  13. Groenewold, Nicolaas & Fraser, Patricia, 1999. "Time-varying estimates of CAPM betas," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(4), pages 531-539.
  14. Michael R King, 2009. "The cost of equity for global banks: a CAPM perspective from 1990 to 2009," BIS Quarterly Review, Bank for International Settlements, September.
  15. Caporale, Tony, 2012. "Time varying CAPM betas and banking sector risk," Economics Letters, Elsevier, vol. 115(2), pages 293-295.
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