Assessing the applicability of the Actuaries Climate Index within weather derivatives framework
Bibliographic record
Abstract
The global climate change has emerged as one of the most complex and pressing challenges confronting humanity. With the impact of climate change intensifying, a growing number of industries, such as agriculture, insurance, energy, and tourism, find themselves increasingly vulnerable to weather-related risks. In response to this challenge, industries, organizations, and even individuals have the opportunity to utilize weather derivatives as a means to manage or mitigate these risks. Weather derivatives are financial instruments designed to hedge against adverse weather conditions, with their value contingent upon specific weather variables. Since 2016 the Actuaries Climate IndexTM(ACI) has integrated diverse climate variables indicative of extreme weather conditions. The ACI serves to enhance the comprehension of climate trends and their potential impacts among actuaries, insurance companies, and policymakers through a monitoring tool focused on climate change indices. Drawing on data from twelve subregions across the United States and Canada, the ACI assesses six climate indicators, including high and low-temperature extremes, precipitation, drought, extreme wind, and sea level rise. Weather derivatives with underlying variables sourced from the components of the ACI, this study seeks to evaluate the effectiveness of the ACI in managing weather-related risks for market participants. Additionally, it aims to highlight the ACI’s potential as a dependable benchmark for pricing weather-related financial instruments, potentially encouraging wider adoption of the index in future risk management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".