Asset allocation in extreme market conditions: a comparative analysis between developed and emerging economies
Bibliographic record
Abstract
This study makes use of the Extreme Value Theory, based on the Generalised Pareto Distribution and the Generalised Extreme Value Distribution, to construct efficient portfolios during periods of turmoil. The portfolios are constructed by combining different assets constituted by their positions in emerging and developed stock markets, with the aim of identifying which assets combinations provide optimal portfolio allocations during turmoil periods. For the developed stock markets, the study uses the French CAC 40, the Canadian S&P/TSX, the United Kingdom FTSE 100, the Japanese NIKKEI 225 and the United States S&P500 indices and returns. Five emerging stock markets indices are used, namely, the Brazilian BOVESPA, the Chinese SHCOMP the Indian S&P BSE SENEX, Indonesian JSI and the Turkish BIST 100. The data sample spans from August 1997 to August 2019 and include major economic and financial crises. Our findings show that for the different portfolios constructed, the estimated shape, location, and scale parameters differ depending on the Extreme Value Theory distribution under investigation. Moreover, based on the Generalised Pareto Distribution and the Generalised Extreme Value Distribution for portfolio optimisation, the results of the study show that during extreme conditions investors are prone to allocate more weight to developed stock market assets than to emerging markets. This confirms that developed economies are safe havens, especially during extreme market conditions. Moreover, the GPD is superior as it provides maximum risk-reward ratios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".