An empirical analysis of interest risk management in U.S. Commercial Banks
Bibliographic record
Abstract
The present Dissertation discusses the decisions of interest risk management in U.S commercial banks for the last two stages of the previous business cycle. To address the research question, it was used cross-section and time-series quarterly data for multiple panel data regressions with on average 1060 U.S. commercial banks per quarter between 2001 and 2009. The panel data regressions contain variables related to banks’ characteristics and the macroeconomic environment. There are two different dependent variables that represent the two techniques of hedging interest rate risk: i) 1-year maturity gap divided by total assets, and ii) the natural logarithm of the total amount ($-value) of interest rate derivatives for hedging purposes. This Dissertation’s sample is divided in two periods: before the U.S. Subprime Crisis between 2001 and 2007 and during the U.S. Subprime Crisis (2007-2008). These two periods represent an expansion and a recession periods. The findings suggest that smaller banks have a more conservative maturity gap, while larger banks use more interest rate derivatives for hedging purposes. The macroeconomic environment does not seem to have an impact on the maturity gap for large derivatives users while it has for small commercial banks and for large non-derivatives user banks. When the decision is to use or not to use interest rate derivatives, the macroeconomic environment only affects large derivatives users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".