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

U.S. Banking Sector Risk in an Era of Regulatory Change: A Bivariate GARCH Approach 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1023/A:1008324023419
Publisher site
See Article on Publisher Site

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.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Oct 8, 2004

References

  • Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge
    Baillie, R.T.; Myers, R.J.
  • On the Assessment of Risk
    Blume, M.

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