Relationship between Correlations and Volatilities of Global Equity Returns: An Empirical Study of the Eurozone Debt Crisis
Bibliographic record
Abstract
The objective of this study was to investigate the relationship between correlations of global equity returns and volatilities, in which equity markets are divided into two areas: one is PIIGS area (Portugal, Italy, Ireland, Greece and Spain) and the other is non-PIIGS area. Weekly index prices are collected spanning from January 5, 2001 to January 27, 2012, a total of 578 observations. Current study firstly used the best-fitted ARMA-GARCH model on each stock market and then utilized the diagonal AG-DCC model to derive the dynamic conditional correlations. The empirical finding suggests an overall regional factor denoted by PIIGS volatility (or volatility ratio) and a global factor by the U.S. counterpart during the Eurozone debt crisis. The finding of negative correlation between correlations and volatilities (or volatility ratio), mainly attributed to the PIIGS, is not in line with that of Cappiello, Engle, and Sheppard (2006). Moreover, the correlations of Germany with the other equity markets are not explained by the regional factor but by the global factor. The reason is that Germany has been the Europe’s most powerful economy and also plays a pivotal role in the management of Eurozone debt crisis. Lastly, investors may gain benefits from international diversification investment by including assets of PIIGS as well as either the Asian or the developed stock markets.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".