Determinants of Financial Stability and Development in South Africa: Insights from a Quantile ARDL Model of the South African Financial Cycle
Bibliographic record
Abstract
This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst volatile conditions. Using monthly data from 2000 to 2024, the research employs a quantile autoregressive distributed lag (QARDL) model to capture the heterogeneity and persistence of macro-financial linkages across the financial cycle’s distribution. The use of the QARDL model in this study allows for capturing asymmetric and quantile-specific relationships that traditional linear models might overlook. Findings reveal that monetary policy, and the housing sector are key drivers of long-term financial development in South Africa, showing positive effects. Conversely, exchange rate movements, inflation, money supply, and macroprudential policy dampen financial development. Short-term financial booms are associated with GDP growth, credit, share, and housing prices. Money supply and inflation are more closely linked to burst phases. These results underscore the importance of policy coordination, particularly between monetary and macroprudential authorities, to balance promoting financial development and ensuring stability in emerging markets. This study contributes to the empirical literature and offers practical insights for policymakers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".