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.

Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models / R. Bohte, L. Rossini. - In: JOURNAL OF RISK AND FINANCIAL MANAGEMENT. - ISSN 1911-8074. - 12:3(2019), pp. 150.1-150.18. [10.3390/jrfm12030150]

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

L. Rossini
2019

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.
Bayesian VAR; cryptocurrency; Bitcoin; forecasting; density forecasting; time-varying volatility
Settore SECS-P/05 - Econometria
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/833767
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