Unpredictability Exposure between Cryptocurrency with Different Developing and Emerging Stock Market
Bibliographic record
Abstract
Abstract: This paper studied the relationship between Bitcoin USA and Australia trade volume with respect to three developed and emerging countries stock market index. The developed countries are Untied States of America, Canada, and Australia and three emerging countries stock index were Russia, India, and China. We use daily price returns of the currency against which Bitcoin is traded, these include the American dollar and Australian Dollar from period 17th February 2018 to 31st December 2021. The total observations are 1028. The result suggests that if any fluctuations exit in Bitcoin USA and Bitcoin Australia the Russian stock market does not affect but if any positive shocks occur in Bitcoin USA and Bitcoin Australia the China stock market shows the reverse effect on their stock returns. This study provides the useful information for economic development of the countries with respect to the currencies and stock market index. Also, it helps the investors to prepare the portfolio for making the investments in the different stock market index of the countries and its impact on the currency. Keywords: Bitcoin, EGARCH, ARCH, Stock Market and Ordinary Least Square Model
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".