Dynamics of the relationship between stock markets and exchange rates during quantitative easing and tightening
Bibliographic record
Abstract
Abstract This study utilizes two complementary models, the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz (TVP-VAR-DY) and the Time-Varying Parameter Vector Autoregressive Baruník–Křehlík (TVP-VAR-BK), to investigate the dynamic volatility transmission between exchange rates and stock returns in major commodity-exporting and -importing countries. The analysis focuses on periods of quantitative easing (QE) and quantitative tightening (QT) from March 15, 2020 to December 30, 2022. The countries examined are Canada and Australia (major commodity exporters) and the UK and Germany (major commodity importers). An essential contribution of this paper is new empirical insights into the dynamics of stock market returns and the transmission of volatility between these markets and exchange rates during the QE and QT periods. The results reveal that causality primarily flows from stock markets to exchange rates, especially during the QT period across all investment horizons. The Toronto Stock Exchange (TSX) emerges as the principal net driver among the markets under study. Furthermore, the Canadian exchange rate (USDCAD) and the Australian Stock Exchange (ASX) are the most significantly affected indices within the network across various investment horizons (excluding the long-term). These findings underscore the importance for investors and policymakers to consider the interplay between exchange rates and stock market returns, particularly in the context of the QE and QT periods, as well as other economic, political, and health-related events. Our findings are relevant to various stakeholders, including governments, traders, portfolio managers, and multinationals.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".