Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models
Abstract
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.
Keywords
Cite
@article{arxiv.1909.06599,
title = {Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models},
author = {Rick Bohte and Luca Rossini},
journal= {arXiv preprint arXiv:1909.06599},
year = {2019}
}
Comments
Forthcoming in "Journal of Risk and Financial Management"