Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures
Bibliographic record
Abstract
Aggregate Risk Analysis is a computationally intensive and a data intensive problem, thereby making the application of high-performance computing techniques interesting. In this paper, the design and implementation of a parallel Aggregate Risk Analysis algorithm on multi-core CPU and many-core GPU platforms are explored. The efficient computation of key risk measures, including Probable Maximum Loss (PML) and the Tail Value-at-Risk (TVaR) in the presence of both primary and secondary uncertainty for a portfolio of property catastrophe insurance treaties is considered. Primary Uncertainty is the the uncertainty associated with whether a catastrophe event occurs or not in a simulated year, while Secondary Uncertainty is the uncertainty in the amount of loss when the event occurs. A number of statistical algorithms are investigated for computing secondary uncertainty. Numerous challenges such as loading large data onto hardware with limited memory and organising it are addressed. The results obtained from experimental studies are encouraging. Consider for example, an aggregate risk analysis involving 800,000 trials, with 1,000 catastrophic events per trial, a million locations, and a complex contract structure taking into account secondary uncertainty. The analysis can be performed in just 41 seconds on a GPU, that is 24x faster than the sequential counterpart on a fast multi-core CPU. The results indicate that GPUs can be used to efficiently accelerate aggregate risk analysis even in the presence of secondary uncertainty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".