Bibliographic record
Abstract
Non-maturing deposits (NMDs) are a significant source of liquidity for banks, making research into their modelling and forecasting invaluable. However, NMDs have no explicit expiration date, posing liquidity risks and complicating management. This research develops models and a framework to explain, predict, and manage variations in non-maturing deposits. Aggregate savings and transaction deposit data from an African bank were analysed to test the methodologies. The Trend-Fourier model, leveraging historical trends and Fourier analysis, forecasted 90-day deposit volumes. The model revealed prominent cyclicalities and monthly trends in deposit account volumes. Benchmarking showed high accuracy for savings deposit volumes, while transaction deposit volumes were less accurate, suggesting simpler models might be suitable. Additionally, a risk metric called LVaR (Liquidity Value at Risk) was proposed. Two approaches for calculating the LVaR were tested. An exceedance test demonstrated notable accuracy for savings deposit volumes but struggled with transaction deposits. Results indicated savings deposit volumes were more predictable than transaction deposits. These findings could enhance banks’ balance sheet management by improving non-maturing deposit forecasting. The proposed methodologies could be utilized for internal and regulatory purposes, such as calculating the liquidity coverage ratio under Basel regulations.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".