Bilateral Integration Measures and Risk Attitudes in Large Stock Markets
Bibliographic record
Abstract
This paper examines whether developed markets are more internationally integrated than emerging markets. A new bivariate regime switching model is constructed in order to take into account both international integration regime and segmentation regime, capture the endogenous and interactive effects between large markets, and pay attention to the economic structure of the price of variance risk. We estimated such regime switching model for 24 large stock markets and the US market as a reference market. That is, the regime reflects whether each of the 24 markets is integrated with the US. As a result, the structures of representative investor's risk attitude, or that of the price of variance risk, in each of the following 15 markets are almost the same; Canada, France, Italy, Australia, Hong Kong, Netherlands, Spain, Sweden, Switzerland, Brazil, South Korea, Taiwan, Indonesia, Mexico and Saudi Arabia. In such markets, these "international integration measures" defined as the (smoothed) probability of international integration regime are on average high, declining before the 2008 global financial crisis, but rising again after the crisis. This means that non-home-biased strategies such as an international diversification have advantages over home-biased strategies such as a domestic concentration except just before the crisis. In addition, the difference between the international integration measures of developed and emerging markets included in these markets is extremely small. In other words, being an emerging market does not mean that the market is segmented. Summing up the above results, it can be concluded that the international diversification is strongly recommended in these markets regardless of country or period.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".