Rebalancing Portfolios in Periods of Stress in the Global Economy: Capital Markets vs. WTI, XAU, XAG, XPT
Bibliographic record
Abstract
The purpose of this paper is to estimate whether portfolio diversification is feasible in the capital markets of the Netherlands (AEX), France (CAC 40), Germany (DAX 30), Canada (FTSE 100), Italy (FTSE MIB), Spain (IBEX 35), Russia (IMOEX), and the commodities oil (WTI), silver (XAG), gold (XAU), and platinum (XPT) from January 1st, 2018, to December 31st, 2022. The goal of this analysis is to answer the following question, namely to know if: i) the 2020 and 2022 events accentuated the integration between capital markets and commodities (WTI), silver (XAG), gold (XAU), and platinum (XPT)? The results show that during the Tranquil period, the markets present 41 integrations (in 110 possible), with the French price index (CAC 40) integrating the most with its peers, with 8 integrations (in 10 possible), while the commodities markets present the lowest integrations, with silver (XAG) presenting the most relevant number of integrations (2 in 10 possible) and platinum (XPT) showing only 1 integration (in 10 possible). Oil (WTI) and gold (XAU) have not integrated with any of their peers during this period of tranquillity. During the period of the 2020 and 2022 events, there were 60 integrations in 110 possible. According to the results and when compared to the previous sub-period, the capital markets maintained/decreased the same number of integrations with their peers. Silver (XAG) and platinum (XPT) also showed an increase in the number of integrations. XAG exhibited 5 integrations (out of a possible 10), whereas platinum (XPT) had 7 integrations (out of 10 possible). Gold (XAU) was the only commodity that remained completely segmented from both the capital markets and the commodities under consideration. To answer the diversification question, there is evidence that the gold market (XAU) exhibits safe-haven characteristics during the 2020 and 2022 events; these findings are relevant for individual and institutional investors operating in these financial markets.
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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 | 0.002 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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 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".