Exchange Rates of Currencies, Volatility of Bitcoin Returns and Value at Risk (VaR) Analysis
Bibliographic record
Abstract
There has been an increase in curiosity about the relationship between the returns on Bitcoins and returns on exchange rates in the last few years. This is especially important since Bitcoin is becoming more and more well-liked as a substitute for fiat money. This study therefore estimates the dynamic impact of exchange rates and their returns on Bitcoin return and also the value-at-risk (VaR) associated with each exchange rate and Bitcoin. The variance series derived from an estimation of the variations between the current and historical prices of Bitcoin using the exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model was used to compute data on Bitcoin volatility. Accordingly, return on Bitcoins was modelled as the natural logarithm of the difference between current day Bitcoin price and previous day price. The joint ARMA-FIGARCH models were estimated in this study to model the returns on Bitcoins transactions and currency trading rates based on time series from February 1, 2010 to August 30, 2024. The research findings underscore the presence of a significant dynamic adjustment of Bitcoin returns to exchange rate returns across all countries. This goes to indicate that there is a high possibility of incurring losses when making investments with digital currencies like Bitcoin. The originality of the paper lies on the fact the research assessed the effect of returns on currency exchange rates of rich countries and also estimated the dynamic effect of Bitcoin returns on exchange rates of the selected countries. The study establishes a substantial volatility feedback effect for returns on Bitcoins, whereas for each of the currencies, the incidence of a less significant volatility feedback effect was made evident. Investors in the foreign exchange market who chose to maximize profits at a lower risk, trading with the pound sterling/US dollar rate, the Euro/US dollar rate, the Australian dollar/US dollar rate; Canadian dollar/US dollar rate, Swiss Franc/dollar rate, New Zealand dollar/US dollar rate, and Luxembourg Franc/US dollar rate are profitable options. In policy circles, monitoring volatility dynamics is crucial for promoting forex market stability and investor confidence. This research benefits policy makers and marketers of financial assets in OECD countries.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".