Russia’s Invasion of Ukraine and Implications for the Ukrainian Hryvnia and the Russian Ruble
Bibliographic record
Abstract
This paper examines the interest rates, expected spot rates, and inflation rates of the Ukrainian Hryvnia (₴) to the Russian Ruble (₽). Our methodology analyzes international parity relationships, including Purchasing Power Parity (PPP) and the International Fisher Effect (IFE). Focusing on the four years from two years before Russia’s initial invasion of Ukraine on February 24, 2022, i.e., February 2, 2020 to post-conflict up to December 2023, we hypothesize that the geopolitical tensions induced by the invasion have led to significant fluctuations and high volatility of these currencies. Additionally, we propose that the economic consequences of the invasion, such as disruptions to trade and food and supply shortages, may further affect these variables by increasing domestic and international money financing, therefore triggering higher inflation rates. Our analyses indicate that spot rates predicted using international parity relationships suggest the weakening of the Ukrainian Hryvnia after the Russia-Ukraine conflict. Our findings shed light on the magnitude and direction of currency movements, providing insights into the economic ramifications of the conflict. Specifically, we aim to elucidate how the invasion has impacted the region's exchange rates and economic stability.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".