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Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models

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  • Bouri, Elie
  • Christou, Christina
  • Gupta, Rangan

Abstract

The returns of cryptocurrencies tend to co-move, with their degree of co-movement being contingent on the (bullish- or bearish-) states. Given this, we use standard factor models and regime-switching factor loadings to forecast the returns of a specific cryptocurrency based on its lagged information and informational contents of 14 other cryptocurrencies, with these 15 together constituting 65% of the market capitalization. Considering top five cryptocurrencies namely, Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin, we find significant forecastability and evidence that factor models, in general, outperform the benchmark random-walk model, with the regime-switching versions standing out in the majority of the cases.

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  • Bouri, Elie & Christou, Christina & Gupta, Rangan, 2022. "Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003993
    DOI: 10.1016/j.frl.2022.103193
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    Cited by:

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    2. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Cryptocurrencies; Factor model; Markov-switching; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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