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

TL;DR

This paper compares Bayesian time-varying volatility models for forecasting major cryptocurrencies, demonstrating stochastic volatility with Student-t errors outperforms traditional VAR models in accuracy.

Contribution

It introduces a comparative analysis of Bayesian models with different volatility structures and error distributions for cryptocurrency forecasting.

Findings

Stochastic volatility models outperform VAR benchmarks in point and density forecasts.

Student-t error distribution improves stochastic volatility model performance.

Including macro predictors like S&P 500 enhances forecast accuracy.

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.