Volatility Spillovers and Market Integration: A Dynamic Connectedness Analysis of Emerging and Developed Stock Markets (2010–2024)
Bibliographic record
Abstract
This study investigates the dynamic volatility spillovers and market connectedness between emerging and developed stock markets over the period 2010–2024. Employing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model combined with the Diebold-Yilmaz connectedness framework, we analyze the magnitude, direction, and time-varying nature of volatility transmission across fifteen major stock market indices. The sample includes eight developed markets (United States, United Kingdom, Germany, France, Japan, Canada, Australia, and Switzerland) and seven emerging markets (China, India, Brazil, Russia, South Africa, Mexico, and Indonesia). Our empirical findings reveal that developed markets, particularly the United States, serve as dominant transmitters of volatility spillovers, while emerging markets predominantly act as net receivers. The total connectedness index exhibits significant time variation, with pronounced spikes during the European sovereign debt crisis, the Chinese stock market turbulence of 2015–2016, and most notably during the COVID-19 pandemic. The results demonstrate that crisis periods substantially intensify cross-market volatility linkages, reducing the benefits of international portfolio diversification precisely when they are most needed. These findings carry significant implications for international investors, portfolio managers, and policymakers seeking to understand systemic risk transmission and develop effective risk management strategies in an increasingly interconnected global financial system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".