Forecasting the Risk of Cryptocurrencies: Comparison and Combination of GARCH and Stochastic Volatility Models

Prüser Jan ()
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Prüser Jan: Fakultät Statistik, TU Dortmund, 44221 Dortmund, Germany

Journal of Time Series Econometrics, 2024, vol. 16, issue 2, 83-108

Abstract: The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t-distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.

Keywords: cryptocurrencies; GARCH; stochastic volatility; model combination (search for similar items in EconPapers)
JEL-codes: C53 C58 G32 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jtse-2023-0039

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