Economic Scenario Generators: Calibration, Simulation and Comparison from an ALM Perspective
Bibliographic record
Abstract
We are living in a time when the power of today?s computers enables even highly complex simulations to run in a reasonable amount of time. Furthermore, in recent years, the financial and actuarial competencies have been progressively converging. In this thesis, we dive into the world of Economic Scenario Generators (ESGs). These financial tools come in a time, especially after the still-ongoing financial crisis, when financial analysis is no longer envisioned as a black box, nor its answers as the irrefutable truth. Indeed, it should be regarded as an instrument for gaining insight into how we can tame the uncertainty and overcome the complexity of the financial world. Nowadays, ESGs are becoming an invaluable support tool for management in making informed decisions, not a decision maker by itself. This thesis distinguishes the available ESGs into 3 major classes: econometrics-based, pricing-based, and hybrid-based. We, then, delve into the underlying models, the calibration, and the simulation of three representative ESGs: the Wilkie ESG, the Barrie & Hibbert ESG, and the risklab ESG, respectively. Finally, we compare how the three fare through an Asset-Liability Management (ALM) study.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".