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Federal Home Loan Bank advances and bank risk

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  • Travis Davidson
  • W. Simpson

Abstract

The Federal Home Loan Bank system (FHLB) has evolved into a major source of liquidity for the banking system with the demonstrated ability to borrow over a trillion dollars in world financial markets based on an implied U. S. Treasury guarantee. The FHLB loans the borrowed funds to commercial banks at reduced rates that are not adjusted for the risk of an individual bank. Moral hazard could cause member banks using FHLB loans to increase financial leverage and exposure to high risk assets. Conversely, the FHLB offers banks additional liquidity and specialized debt instruments that help them manage interest rate risk. We use dynamic panel generalized method of moments estimation to test the relation between FHLB advances and bank risk. We find that if banks have relatively normal default probabilities, advances are not associated with increased bank risk but, instead, advances are related to decreased interest rate risk. However, when bank default probabilities are high, our evidence suggests advances and higher bank risk are related. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Travis Davidson & W. Simpson, 2016. "Federal Home Loan Bank advances and bank risk," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 137-156, January.
    • Handle: RePEc:spr:jecfin:v:40:y:2016:i:1:p:137-156
    DOI: 10.1007/s12197-014-9300-8
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    References listed on IDEAS

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    1. Berger, Allen N. & Bouwman, Christa H.S., 2013. "How does capital affect bank performance during financial crises?," Journal of Financial Economics, Elsevier, vol. 109(1), pages 146-176.
    2. Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    3. Adam Ashcraft & Morten L. Bech & W. Scott Frame, 2010. "The Federal Home Loan Bank System: The Lender of Next‐to‐Last Resort?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(4), pages 551-583, June.
    4. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    5. Stojanovic, Dusan & Vaughan, Mark D. & Yeager, Timothy J., 2008. "Do Federal Home Loan Bank membership and advances increase bank risk-taking?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 680-698, May.
    6. Han, Chirok & Phillips, Peter C. B., 2010. "Gmm Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity," Econometric Theory, Cambridge University Press, vol. 26(1), pages 119-151, February.
    7. Keeley, Michael C, 1990. "Deposit Insurance, Risk, and Market Power in Banking," American Economic Review, American Economic Association, vol. 80(5), pages 1183-1200, December.
    8. Lisa K. Ashley & Elijah Brewer & Nancy E. Vincent, 1998. "Access to FHLBank advances and the performance of thrift institutions," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 22(Q II), pages 33-52.
    9. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    10. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    11. Billett, Matthew T. & Garfinkel, Jon A. & O'Neal, Edward S., 1998. "The cost of market versus regulatory discipline in banking," Journal of Financial Economics, Elsevier, vol. 48(3), pages 333-358, June.
    12. Wintoki, M. Babajide & Linck, James S. & Netter, Jeffry M., 2012. "Endogeneity and the dynamics of internal corporate governance," Journal of Financial Economics, Elsevier, vol. 105(3), pages 581-606.
    13. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

    1. Sriya Anbil & Mark A. Carlson & Mary-Frances Styczynski, 2021. "The Effect of the PPPLF on PPP Lending by Commercial Banks," Finance and Economics Discussion Series 2021-030, Board of Governors of the Federal Reserve System (U.S.).
    2. James Cash Acrey & Wayne Y. Lee & Timothy J. Yeager, 2019. "Can Federal Home Loan Banks effectively self-regulate lending to influential banks?," Journal of Banking Regulation, Palgrave Macmillan, vol. 20(2), pages 197-210, June.
    3. Angelos Kanas & Panagiotis D. Zervopoulos, 2022. "Federal home loan bank advances and systemic risk," Review of Quantitative Finance and Accounting, Springer, vol. 59(4), pages 1525-1557, November.

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    More about this item

    Keywords

    Federal Home Loan Bank Advances; Moral hazard; Liquidity; Leverage; Credit risk; Interest rate risk; G21; G28;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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