Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models

Rick Bohte and Luca Rossini
Additional contact information
Rick Bohte: School of Business and Economics, Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands

JRFM, 2019, vol. 12, issue 3, 1-18

Abstract: This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalised 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 cryptopredictors 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 is shown to outperform the standard normal approach.

Keywords: Bayesian VAR; cryptocurrency; Bitcoin; forecasting; density forecasting; time-varying volatility (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://www.mdpi.com/1911-8074/12/3/150/pdf (application/pdf)
https://www.mdpi.com/1911-8074/12/3/150/ (text/html)

Related works:
Working Paper: 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) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:12:y:2019:i:3:p:150-:d:268406

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().