Market Linkages and Their Impact on G7 Economies: Exploring Network Connectedness
Bibliographic record
Abstract
This project departs from the well-established finding in macrofinance literature that financial variables have a significant impact on macroeconomic variables. Building on this, we investigate the volatility connectedness among the stock markets of the Group of Seven (G7) countries, which account for a significant portion of global economic output and stock market capitalization. Using the Diebold-Yilmaz Connectedness Index (DYCI) framework, we analyze the connectedness of the G7 stock markets over the period from January 2010 to June 2024. We assess how volatility spills across these markets, particularly in response to major global events such as the 2011 U.S. credit rating downgrade, the 2013 "Taper Tantrum," the 2016 U.S. presidential election, and the COVID-19 pandemic. The findings reveal that market connectedness is highly dynamic, with the U.S. consistently acting as the primary connectedness source, followed by Germany and France during times of market stress. Japan, in contrast, is predominantly a net receiver of volatility. The results further highlight the varying roles of the G7 markets in volatility connectedness, indicating limited roles for the UK, Italy and Canada. The study also explores the relative importance of each market as a shock propagator, finding that the U.S. has the highest shock propagation capacity, while Japan consistently has the lowest. Consistent with the literature, our findings reveal a strong relationship between market volatility and the macroeconomic policy impacts of G7 economies, particularly during key market and economic episodes. These insights contribute to the understanding of economic and financial market interaction and provide valuable implications forpolicymakers and investors navigating the interconnected global 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".