The Basis Risk Component of Commercial Bank Stock Returns

Wetmore, Jill L.; Brick, John R.

doi:10.1016/s0148-6195(97)00054-4

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…This is probably the result of their high involvement in international lending. The aforesaid work is further extended by Wetmore and Brick (1998). Essentially, they account for fluctuations in income due to imperfect co‐movements between the return on rate‐sensitive assets and the cost of rate‐sensitive liabilities (basis risk).…”

Section: Nonstandard Modeling Approachesmentioning

confidence: 99%

“…Non‐Standard Approaches Unal and Kane (1988) 1975–1985P/5 large banks, 16 medium banks, 9 small banks, 9 S&LMonthly/Quarterly returns T‐bill and T‐bond, recorded and unrecorded equity, dummiesOLS, SMVAM° / ✓ / AL Born and Moser (1990) 1962–198250 Fed‐member banksDaily discount rate changes (63 changes announced), dummiesOLS, Event study CAR✓ Kane and Unal (1990) 1975–1985P/25 large banks, 54 to 97 medium banks, 25 small banksQuarterly returns long‐term govt. bond, bookable, and unbookable equityGQSRM, Cross section,SMVAM° / AL Choi, Elyasiani, and Kopecky (1992) 1975–1987P/48 largest banks (assets > $10 billion)Monthly 3‐month T‐bill rate, weighted USD index–10 currencies, dummiesOLS, Cross‐section time series✓ Allen and Jagtiani (1997) 1974–1994P/351 depository, 193 security firms, 78 insurance, 246 mutual funds, 255 financial firmsMonthly 3‐month T‐bill rate, dummiesOLS, Cross‐section 60‐month rolling betas✓ Wetmore and Brick (1998) 1986–1995P/66 banks (MCB, super‐regional banks, regional banks)Weekly long‐term bond rate, basis: prime rate minus (Fed funds rate+LIBOR)/2, weighted USD index–10 currenciesOLS, Cross‐section∅ / ✓ Benink and Wolff (2000) 1974–1993P/20 largest U.S. bank holding companiesWeekly 3‐month T‐bill rate, 10‐year T‐bond rate, Survey data for the Fed funds rateOLS Johnston and Madura (2002) 1987–2001P/53 life insurance, 55 health insurance, 128 property insuranceMonthly 30‐year T‐bond yield, NAREIT All‐REIT and Equity‐REIT indicesOLS, SURE, Cross‐section✓ Wetmore (2003) 1990–1997P/7 MCB, 13 super‐regional, 30 regionalWeekly long‐term bond rate, prime rate minus (Fed funds rate+LIBOR)/2, log of the refinancing index (MBAA), weighted USD index–10 currenciesOLS∅ / ✓ Staikouras (2005) 1989–2000P/18 banks, 15 finance firms, 21 insurance, 61 invest. trusts, 30 REIT, 94 industrialWeekly 1‐ and 3‐month T‐bill discount ratesSURE∼ Viale, Kolari, Fraser, and Sorescu (2006) 1986–2003P/All CRSP banks, K. French's non‐financial firmsMonthly 1‐month T‐bill rate, term spread (i.e., 25‐year bond yield minus 90‐day T‐bill), default risk, book‐to‐market, size, human capital component of wealth, gap, loan loss provisionsOLS, Time series cross‐section...…”

Section: Appendix: Research Summary On Financial Institutions' Yimentioning

confidence: 99%

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Financial Intermediaries and Interest Rate Risk: II

Staikouras

2006

Financial Market

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The current work extends and updates the previous survey (Staikouras, 2003) by looking at other aspects of the financial institutions' yield sensitivity. The study starts with an extensive discussion of the origins of asset-liability management and the subsequent work to identify effective ways of measuring and managing interest rate risk. The discussion implicates both regulatory and market-based approaches along with any issues surrounding their applicability. The literature is enriched by recognizing that structural and regulatory shifts affect financial institutions in different ways depending on the size and nature of their activities. It is also noted that such shifts could change the bank's riskiness, and force banks to adjust their balance sheet size by altering their maturity intermediation function. Besides yield changes, market cycles are also held responsible for asymmetric effects on corporate values. Furthermore, nonstandard investigations are considered, where embedded options and basis risk are significant above and beyond the intermediary's rate sensitivity, while shocks to the slope of the yield curve is identified as a new variable. When the discount privilege is modeled as an option, it is shown that its value is incorporated in the equities of qualifying banks. Finally, volatility clustering is further established while constant relative risk aversion is not present in the U.S. market. Although some empirical findings may be quite mixed, there is a general consensus that all forms of systematic risk, risk premia, and the risk-return trade-off do exhibit some form of variability, not only over time but also across corporate sizes and segments.

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Section: Nonstandard Modeling Approachesmentioning

confidence: 99%

Section: Appendix: Research Summary On Financial Institutions' Yimentioning

confidence: 99%

Financial Intermediaries and Interest Rate Risk: II

Staikouras

2006

Financial Market

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“…CD, the prevailing secondary market rate on three-month maturity certificates of deposit, is used as a proxy for the marginal cost of financing the loan. IPrime, the information content of the prime rate, is defined as the spread between the prime and CD rates, which is similar to the proxy for basis risk used by Wetmore and Brick (1998) and which has been found to have explanatory power in discussions of commercial bank stock returns. The data used in this article include both fixed and variable rate loans; therefore, we created a variable FxTStr to account for the influence of the term structure (TStr) on fixed rate loans (FixedRate).…”

Section: Data Description By Chronological Loan Sequencementioning

confidence: 99%

The Pricing of Sequential Bank Loans

Athavale

Edmister

2004

The Financial Review

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The theory of financial intermediation assigns banks a unique role in the resolution of information asymmetry. Banks, in general, obtain private information about the borrower and the project during the screening of loan applicants and during the monitoring of loan recipients. Incumbent banks, in particular, utilize information obtained while monitoring previous loan extensions to resolve information asymmetry when granting subsequent loans. We examine the rate on a sequence of loans to a borrower and find that the incumbent bank information advantage has finite magnitude and is quickly reflected in the pricing of the second loan. We also find that the lending relationship does not deteriorate to the detriment of the borrower. This study also provides further evidence supporting the hypothesis that an incumbent bank resolves information asymmetry during the monitoring of loan extensions.

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