Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models
Rick Bohte and
Papers from arXiv.org
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.
New Economics Papers: this item is included in nep-ets, nep-for, nep-ore and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://arxiv.org/pdf/1909.06599 Latest version (application/pdf)
Journal Article: Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1909.06599
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().