ANALYSIS OF THE IMPACT OF EXCHANGE RATE VOLATILITY ON THE SOUTH AFRICAN GOVERNMENT BOND MARKET
Bibliographic record
Abstract
Although government bond markets in Africa have been growing steadily, one of the factors inhibiting the growth of these markets is that only a few African economies have clear access to global financial markets. This factor has been worsened by the volatility of exchange rates of these countries, which could potentially affect bonds’ yields adversely. This paper empirically investigated the impact of exchange rate volatility on the South African bond market and the economy as a whole. A quantitative technique was utilised with a Johansen cointegration estimation technique to determine whether the variables were cointegrated and to determine the effect of exchange rate volatility on the bond market and the economy. GARCH was used to generate exchange rate volatility from rand/US dollar exchange rate series, which was then used with other variables in a VECM for the main estimation. Monthly datasets from January 2000 to December 2018 were analysed with variables such as exchange rate, bond yields, real GDP and CPI included. The results from the Johansen cointegration test indicated that the variables have a long-run relationship. Furthermore, results from the VECM estimation indicates that volatility with regard to the external value of interest payment on government bonds discourages investment in the South African bond market. Inflation and economic growth are also found to have positive and negative effects on bond yield, respectively. The overall results from the study suggest that exchange rate volatility is one of the factors limiting the potential of the economy’s bond market by discouraging foreign investment in the market. To this end, both the monetary and fiscal authorities in the country need to work together to formulate and implement policies that would reduce the volatility in the South African currency. Key Words: bond market, exchange rate, GARCH, VECM, volatility
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".