Portfolio diversification benefits of alternative currency investment in Bitcoin and foreign exchange markets
Bibliographic record
Abstract
Abstract This study examines the portfolio diversification benefits of alternative currency trading in Bitcoin and foreign exchange markets. The following methods are applied for the analysis: the spillover index method of Diebold and Yilmaz (Int J Forecast 28(1): 57–66, 2012. 10.1016/j.ijforecast.2011.02.006 ), the spillover asymmetry measures of Barunik et al. (J Int Money Finance 77: 39–56, 2017. 10.1016/j.jimonfin.2017.06.003 ), and the frequency connectedness method of Barunik and Křehlík (J Financ Econom 16(2): 271–296, 2018. 10.1093/jjfinec/nby001 ). The findings identify the presence of low-level integration and asymmetric volatility spillover as well as a dominant role of short horizon spillover among Bitcoin markets and foreign exchange pairs for six major trading currencies (US dollar, euro, Japanese yen, British pound sterling, Australian dollar, and Canadian dollar). Bitcoin is found to provide significant portfolio diversification benefits for alternative currency foreign exchange portfolios. Alternative currency Bitcoin trading in euro is found to provide the most significant portfolio diversification benefits for foreign exchange portfolios consisting of major trading currencies. The findings of the study regarding spillover dynamics and portfolio diversification capabilities of the Bitcoin market for foreign exchange markets of major trading currencies have significant implications for portfolio diversification and risk minimization.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| 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".