Exchange Rate Determinants & Exchange Rate Risk Hedging: An Empirical Study Applied on the Case of a Tunisian Company
Bibliographic record
Abstract
The aim of this paper is to propose an appropriate method that could assist decision-makers in the finance department responsible for hedging against the exchange risk yielding a better strategy to shield the company from the undesired scenarios of loss.Our research interrogation related to an intelligent solution devoted to the minimization of the currency risk incurred by our Tunisian studied Holding when trading on the foreign exchange market.The study focused on the four most involved currencies in the Tunisian and Foreign trading market: USD, EUR, GBP, and JPY.Our sampling period runs from January 03, 2011 to June 30, 2021.First, the results suggest that the key interest rate and the foreign exchange reserves are most determinant variables compared to the other variables.Second, we present a feasible procedure to hedge against currency risk consisting of five steps, through a developed Artificial Intelligence based program, a correlation analysis and the Temporal Causality Model.Finally, we create our different hedging scenarios and the desired exchange rate is forecasted for a period (from 1 month to 12 months).Based on the forecasted exchange rate values, the studied Holding is able to conclude the most advantageous forward hedging contract by choosing the term of the contract at which the exchange rate level is the most profitable rate according to its position in the market.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".