Dynamic Spillovers from US (Un)Conventional Monetary Policy to African Equity Markets: A Time-Varying Parameter Frequency Connectedness and Wavelet Coherence Analysis
Bibliographic record
Abstract
Since the implementation of unconventional monetary policies (UMPs) by the US in response to the global financial crisis (GFC) and the COVID-19 pandemic, there have been increasing concerns that these forward guidance and quantitative easing programmes have had spillover effects on global equity markets. We specifically question whether the implementation of these UMPs have had spillovers to African equities, which have been previously speculated to be decoupled from global markets and shocks. Time-varying-parameter (TVP) frequency connectedness and wavelet coherency methods were used to examine the dynamic time-frequency spillovers between daily time series of the US shadow short rate and African equities returns/volatility between 1 January 2007 and 31 March 2023. On one hand, the TVP frequency connectedness analysis reveals robust long-run spillovers from US monetary policy to African equity markets during specific periods: 2009, 2013, 2020, and 2021. These coincide with instances when the Federal Reserve announced their transition from conventional to unconventional monetary practices and vice versa. On the other hand, the wavelet analysis provides insights into the ‘sign’ of the spillovers, indicating mixed phase dynamics during UMPs responding to the GFC. In contrast, anti-phase or negative co-movements characterize UMPs implemented during the COVID-19 pandemic, implying that these policies increased both returns and volatilities to African equities. Altogether, we conclude that US UMP has increasing deteriorated market efficiency and amplified portfolio risk in African equities whilst during ‘normalization’ periods US monetary policy has little transmission effect.
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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 | 0.002 |
| 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) | 0.002 | 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".