Stochastic Models for Pricing Weather Derivatives using Constant Risk Premium
Why this work is in the frame
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Bibliographic record
Abstract
Pricing weather derivatives is becoming increasingly useful, especially in developing economies. We describe a statistical model based approach for pricing weather derivatives by modeling and forecasting daily average temperature data which exhibits long-range dependence. We pre-process the temperature data by filtering for seasonality and volatility and fit autoregressive fractionally integrated moving average (ARFIMA) models, employing the preconditioned conjugate gradient (PCG) algorithm for fast computation of the likelihood function. We illustrate our approach using daily temperature data from 1970 to 2008 for cities traded on the Chicago Mercantile Exchange (CME), which we employ for pricing degree days futures contracts. We compare the statistical approach with traditional burn analysis using a simple additive risk loading principle for pricing, where the risk premium is estimated by the method of least squares using data on observed prices and the corresponding estimate of prices from the best model we fit to the temperature data.
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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 | 0.000 | 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 | 0.001 | 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 it