Regime switching forecasting for cryptocurrencies

Ilyas Agakishiev (), Wolfgang Karl Härdle (), Denis Becker () and Xiaorui Zuo ()
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Ilyas Agakishiev: Humboldt Universität zu Berlin
Wolfgang Karl Härdle: Humboldt Universität zu Berlin
Denis Becker: NTNU Business School
Xiaorui Zuo: Shaw Foundation

Digital Finance, 2025, vol. 7, issue 1, No 6, 107-131

Abstract: Abstract There are many ways to model complex time series. The simplest approach is to increase the complexity, and thus, the flexibility of the model, for the entire time series. As an example, one could use a neural network. Another solution would be to change the parameters of a model dependent on the “state” or “regime” of the time series. A typical example here would be the Hidden Markov model (HMM). This paper combines the two concepts to create a Reinforcement Learning (RL) model that adds variables that depend on the state of the time series. To test the concept, the RL model is used with cryptocurrency data to determine the share to invest into the cryptocurrency index CRIX in order to maximize wealth. The results have shown that cryptocurrency metadata is useful as supplementary data for analysis of the respective prices. The Reinforcement learning model with regimes shows potential for investment management, but comes with some caveats.

Keywords: Regime switching; Machine learning; Crypto currencies; Reinforcement learning; FinTech (search for similar items in EconPapers)
JEL-codes: C14 C15 C63 C87 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42521-024-00123-2

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