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U.S. Banking Sector Risk in an Era of Regulatory Change: A Bivariate GARCH Approach

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  • Brooks, Robert D.
  • Faff, Robert W.
  • McKenzie, Michael D.
  • Ho, Yew Kee

Abstract

This paper assesses the impact of regulatory change on the risk and returns of the U.S. banking industry. The impact of five major regulatory changes on banking sector risk was assessed using daily data for eighteen major U.S. regional banks, money center banks and savings and loan type depository institutions. Risk in this case was proxied via the use of an M-GARCH model which generates time dependent conditional beta estimates. The evidence obtained suggests that the impact of deregulation and reregulation on banking sector risk is case specific. Further, the results obtained show that the market model incorporating dummy variables, which has proven so popular amongst existing studies, discards important information about the variability of beta which the time varying conditional betas capture. Copyright 2000 by Kluwer Academic Publishers

Suggested Citation

  • Brooks, Robert D. & Faff, Robert W. & McKenzie, Michael D. & Ho, Yew Kee, 2000. "U.S. Banking Sector Risk in an Era of Regulatory Change: A Bivariate GARCH Approach," Review of Quantitative Finance and Accounting, Springer, vol. 14(1), pages 17-43, January.
    • Handle: RePEc:kap:rqfnac:v:14:y:2000:i:1:p:17-43
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    Citations

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    Cited by:

    1. Susan Ryan & Andrew C. Worthington, 2002. "Time-Varying Market, Interest Rate and Exchange Rate Risk in Australian Bank Portfolio Stock Returns: A Garch-M Approach," School of Economics and Finance Discussion Papers and Working Papers Series 112, School of Economics and Finance, Queensland University of Technology.
    2. Long, Ling & Tsui, Albert K. & Zhang, Zhaoyong, 2014. "Estimating time-varying currency betas with contagion: New evidence from developed and emerging financial markets," Japan and the World Economy, Elsevier, vol. 30(C), pages 10-24.
    3. Anders Johansson, 2009. "An analysis of dynamic risk in the Greater China equity markets," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 7(3), pages 299-320.
    4. McKenzie, Michael D. & Brooks, Robert D. & Faff, Robert W. & Ho, Yew Kee, 2000. "Exploring the economic rationale of extremes in GARCH generated betas The case of U.S. banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 40(1), pages 85-106.
    5. Prabhath Jayasinghe & Albert K. Tsui, 2009. "Time-Varying Currency Betas : Evidence from Developed and Emerging Markets," Finance Working Papers 22761, East Asian Bureau of Economic Research.
    6. Brooks, Robert D. & Faff, Robert W. & Fry, Tim R. L., 2001. "GARCH modelling of individual stock data: the impact of censoring, firm size and trading volume," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 11(2), pages 215-222, June.
    7. Henryk Gurgul & Paweł Majdosz, 2006. "The impact of institutional investors on risk and stock return autocorrelations in the context of the Polish pension reform," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(2), pages 5-30.
    8. Jayasinghe, Prabhath & Tsui, Albert K. & Zhang, Zhaoyong, 2014. "New estimates of time-varying currency betas: A trivariate BEKK approach," Economic Modelling, Elsevier, vol. 42(C), pages 128-139.
    9. Stephanos Papadamou & Costas Siriopoulos, 2012. "Banks’ lending behavior and monetary policy: evidence from Sweden," Review of Quantitative Finance and Accounting, Springer, vol. 38(2), pages 131-148, February.

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