Forecasting of dependence, market, and investment risks of a global index portfolio
Bibliographic record
Abstract
Abstract This paper undertakes an in‐sample and rolling‐window comparative analysis of dependence, market, and portfolio investment risks on a 10‐year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r‐vines, c‐vines, d‐vines), IGARCH(1,1) RiskMetrics value‐at‐risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value‐at‐risk, conditional drawdown‐at‐risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".