Estimating the Value-at-Risk (VaR) in stock investment of insurance companies: An application of the extreme value theory
Bibliographic record
Abstract
As a capital market investment, stocks have risks that must be managed. Therefore, investors should consider the returns and risks of investment products. This study aims to estimate the risk of insurance companies' loss when investing. The method used to estimate the level of risk is Value at Risk (VaR) based on Extreme Value Theory (EVT). The data used is secondary data in the form of daily stock closing prices from two insurance companies, AXA General Insurance and BRI Insurance, from January 2016 to January 2022. The data were used to estimate the risk value according to the EVT principle. As a result, Insurance AXA General Insurance, with 5.91% liquidity, has the lowest VaR value with a 99% confidence level, while BRI Insurance has 5.04%. We concluded from these results that AXA General Insurance has a lower investment risk. It means that each company has a different risk value. Therefore, investors should know these risk factors when choosing a company.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".