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Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models

Rick Bohte and Luca Rossini

Papers from arXiv.org

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.

New Economics Papers: this item is included in nep-ets, nep-for, nep-ore and nep-pay
Date: 2019-09
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http://arxiv.org/pdf/1909.06599 Latest version (application/pdf)

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Journal Article: Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models (2019) Downloads
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