The Choice of Interest Rate Models and Its Effect on Bank Capital Requirements Regulation and Financial Stability
Bibliographic record
Abstract
According to the Basel regulation banks may use internal risk models to measure interest rate risk and calculate regulatory capital requirements. Under its pillar II the Basel framework grants leeway to banks in their choice of these models. We therefore focus on how well interest rate models describe real interest rate movements empirically and which impact the model choice has on the economic value of bank equity during the financial crisis. Furthermore, we address the question how different choices of interest rate models affect the overall financial stability. To this end we estimate eight different interest rate models for three different currencies (USD, EUR, CHF) using the Generalized Method of Moments (GMM). Then we approximate the balance sheet of a typical Swiss bank during the financial crisis and run Monte Carlo simulations of the balance sheet using the estimated interest rate models. Our results show that the required economic value of equity for a bank varies considerably with the different choices of interest rate models. However, the interest rate models which are empirically best fitting do not imply aggregate financial stability. Thus, banks’ choices of interest rate models to calculate regulatory capital requirements may have a crucial impact on overall financial stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".