The Impact of Exchange Rate and Interest Rate Volatility on Stock Market Returns
Why this work is in the frame
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Bibliographic record
Abstract
This paper sought to examine the impacts of the exchange rate and interest rate volatility on stock market returns within the economies of three major African countries namely, Ghana, Nigeria and South Africa using Exponential General Autoregressive Conditional Heteroscedasticity (EGARCH) and Threshold GARCH (TGARCH) also known as GJR-GARCH estimation methods. We find a positive shock to interest rate and exchange rate lead to persistence rise in the price levels over the 60-month horizon. We find the majority of coefficients are positive compared to negative coefficients. Our results indicate that response asymmetries of positive and negative impact interest rate and exchange rate changes on all stock market returns. Overall, country-analysis suggests that coefficients of interest rate and exchange rate for Ghana are typically negative. Both GJR-GARCH and EGARCH methods show that good news has an impact on volatility more than bad news under different assumptions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it