Measuring the Speed of Convergence of Stock Prices: A Nonparametric and Nonlinear Approach
Bibliographic record
Abstract
This paper evaluates the speed of convergence across national stock markets employing a nonlinear, nonparametric stochastic model of the relative stock price. To estimate the persistence of the relative stock price, we employ an operational algorithm that is based on two statistical notions: the short memory in mean (SMM) and the short memory in distribution (SMD). Using MSCI stock price indices of the G7 countries, we obtain strong empirical evidence of convergence of national stock prices in France, Germany, and the UK vis-à-vis the US index. Also, we obtain much faster convergence rates from our nonlinear models in comparison with those from linear alternatives. On the contrary, our results imply very limited evidence of convergence for Canada, Italy, and Japan. Similarly weak evidence of convergence was obtained from non-G7 developed countries. Our simulation exercise for portfolio switching strategies overall confirms the validity of empirical findings in the present paper.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".