Exchange rate dynamics of Naira in relation to international currencies: Some simulation results
Bibliographic record
Abstract
This study evaluates exchange rate dynamics between the Naira and global currencies, utilizing weekly data from 2008 to 2024. The exchange rates, NGN/USD, NGN/CAD, NGN/AUD, NGN/EUR, and NGN/JPY were analyzed to explore the impact of macroeconomic determinants such as interest rate differentials, market volatility, and inflation rate differentials on exchange rates. The study employed ARIMA regression, and the wavelength techniques. The results climax the nuanced interplay between global financial flows and local economic conditions in determining exchange rates of the Naira against global currencies. The result on inflation differential aligns with the purchasing power parity (PPP) theory, which suggests that currencies adjust to offset inflation disparities. The market volatility result partly implies that periods of heightened market turbulence tend to favor the EUR, driving up the demand for the Euro and subsequently appreciating its value against the Naira. This finding aligns with traditional financial theories that associate risk-aversion behavior with movements towards stronger, less volatile currencies during times of market stress. The results of the study validate the interest rate parity theory, which posits that higher interest rates in one country attract foreign capital, influencing exchange rate dynamics. The detail level estimates of the decomposition component of wavelet results underscore the relevance of micro-level factors and short-lived fluctuations that may be influenced by weekly market activities, speculative trading, or sudden external shocks. These findings imply that the dynamics of exchange rates are complex, with both prolonged patterns and transient oscillations influencing the currency's overall movement. So, a comprehensive understanding of exchange rate behavior requires consideration of both the broader macroeconomic environment and the micro-level, weekly variations that induce volatility in the currency markets. The research findings emphasize the importance of stable monetary policies to manage currency fluctuation risk. These findings have significant implications for policymakers, financial institutions, and investors in the context of managing exchange rate risk in volatile markets.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 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".