The Gumbel Copula Method for Estimating Value at Risk: Evidence from Telecommunication Stocks in Indonesia during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has had a substantial and far-reaching impact on global economic growth, extending its effects to Indonesia as well. Various sectors have witnessed a decline in stock returns as a consequence. Interestingly, the telecommunications sector has bucked this trend by experiencing an increase in stock returns, defying the negative implications of the pandemic. The relationship between returns and risk is inherently intertwined, necessitating a meticulous risk assessment. In response to this need, the Value at Risk (VaR) method has emerged as a rapidly growing and widely adopted risk measurement tool. Among the techniques employed for VaR estimation, the Monte Carlo simulation stands out due to its flexibility and comprehensiveness in accommodating factors such as time variance, volatility, returns, fat tails, and extreme scenarios. The Gumbel copula method, known for its heightened sensitivity to high-risk events, is utilized for VaR estimation on abnormal stock returns. This study aims to quantify the Value at Risk by leveraging the estimated Gumbel copula parameter for the return on the shares of PT. Indosat Ooredoo Hutchison Tbk, and PT. Smartfren Telecom Tbk during the COVID-19 pandemic. At a 90% confidence level, the VaR is determined to be 7.6%. Notably, this estimate closely aligns with the actual values, underscoring the reliability of the VaR estimation conducted using the Gumbel copula parameter estimator. Therefore, this model serves as a robust reference, particularly suitable when dealing with investment return data that deviate from the normal distribution, while considering the unique stock return characteristics within each dataset.
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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 | 0.007 | 0.005 |
| 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 | 0.001 | 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".