Forecasting the LIBOR‐Federal Funds Rate Spread During and After the Financial Crisis
Bibliographic record
Abstract
In this paper, we examine the point and density forecast accuracy of econometric models, surveys and futures rates in predicting the LIBOR‐Federal Funds Rate (LIBOR‐FF) spread during and after the financial crisis. We provide evidence that the futures market forecast outperforms all competing forecasts during and after the financial crisis and that its predictive density is well calibrated. Our results also suggest that the predictive accuracy of the econometric models improves in the post‐crisis period. We argue that the post‐2009 improvement in the econometric models' forecasts is attributable to the absence of LIBOR manipulation. The economic significance of the uncovered predictability is assessed using a trading strategy. Our results suggest that trading based on the futures market and econometric forecasts generates positive risk‐adjusted returns. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:345–374, 2016
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".