Estimation of Exchange Rate Volatility via GARCH Model: Case Study Sudan (1978 – 2009)
Bibliographic record
Abstract
This paper aims to estimate volatility of exchange rate that was caused by inconsistent economic policies adopted by consecutive governments who failed to realize realistic exchange rate of the Sudanese pound. The consequences were mutual influence of high inflation rate, deterioration of the productive sectors, continuous internal and external deficits and depreciation of the exchange rate. To estimate the volatility of the exchange rate EGARCH (1,1) was used. The leverage effect term is negative and statistically different from zero, indicating the existence of the leverage effect (negative correlation between past returns and future volatility. As the past few years prove highly leveraged financial systems can have crises that increase the volatility of asset prices. These results indicate the possibility of a simultaneous feedback between the exchange rate and uncertainty and the response of the exchange rate to news about general price level (CPI)l, money stock, and current account which are the main determinants..
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".