← Back to results Dependency Analysis between Bitcoin and Selected Global Currencies Beata Szetela et al.
What the paper says In this research we have tried to identify the relationship between the exchange rate for bitcoin to the leading currencies such as Dollar, Euro, British Pound and Chinese Yuan and Polish zloty as well. We have applied ARMA and GARCH models to model and to analyze the conditional mean and variance. The appliance of GARCH models have identified some dependency in explanation conditional variance between bitcoin and US Dollar, Euro and Yuan, while ARMA analysis have shown no relations between bitcoin and other dependent variables.
34 citations
Open paper page → Cite
Cite this paper https://doi.org/https://doi.org/10.12775/dem.2016.009 Copy URL
Or copy a formatted citation
BibTeX RIS APA Chicago Link
@article{beata2016,
title = {{Dependency Analysis between Bitcoin and Selected Global Currencies}},
author = {Beata Szetela et al.},
journal = {Dynamic Econometric Models},
year = {2016},
doi = {https://doi.org/https://doi.org/10.12775/dem.2016.009},
} TY - JOUR
TI - Dependency Analysis between Bitcoin and Selected Global Currencies
AU - al., Beata Szetela et
JO - Dynamic Econometric Models
PY - 2016
ER - Beata Szetela et al. (2016). Dependency Analysis between Bitcoin and Selected Global Currencies. *Dynamic Econometric Models*. https://doi.org/https://doi.org/10.12775/dem.2016.009 Beata Szetela et al.. "Dependency Analysis between Bitcoin and Selected Global Currencies." *Dynamic Econometric Models* (2016). https://doi.org/https://doi.org/10.12775/dem.2016.009. Dependency Analysis between Bitcoin and Selected Global Currencies
Beata Szetela et al. · Dynamic Econometric Models · 2016
https://doi.org/https://doi.org/10.12775/dem.2016.009 Copy
Paste directly into BibTeX, Zotero, or your reference manager.
Flag this paper Evidence weight Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
F · citation impact 0.89 × 0.4 = 0.36 M · momentum 0.78 × 0.15 = 0.12 V · venue signal 0.50 × 0.05 = 0.03 R · text relevance † 0.50 × 0.4 = 0.20
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.