Measure of Volatility and Its Forecasting: Evidence from Naira / Dollar Exchange Rate
Bibliographic record
Abstract
In the last five decades, Box Jenkins methodology has been in existence to model univariate time series data but fails or has limitations on modeling volatility. Most financial time series data do exhibit heavy tail and thick distribution, to this effect various parametric and semi-parametric non –linear time series models have been proposed two or three decades ago to capture volatility. However, this research entails measuring volatility and its forecasting using time series exchange rate annual data over the period from 1981 to 2020 (wide periodicity). The exchange rate was transformed to return, and parametric non –linear time series was modeled on it. It was found out that GARCH (1,2) reveals continuous volatility for short while and was the best model to predict the exchange rate volatility based on the evidence from measurement volatility tool; RMSE, MAE, MAPE among other extensions of GARCH models; EGARCH and TGARCH. EGARCH (1, 4) captures the asymmetry effect revealing that negative shocks will persistently have an effect on the volatility of the naira/dollar exchange rate.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".