Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models

Elie Bouri (elie.elbouri@lau.edu.lb), Christina Christou (christina.christou@ouc.ac.cy) and Rangan Gupta
Additional contact information
Elie Bouri: School of Business, Lebanese American University, Lebanon
Christina Christou: School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus

No 202213, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Cryptocurrencies; Factor Model; Markov-switching; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2022-02
New Economics Papers: this item is included in nep-for, nep-mon, nep-ore, nep-pay and nep-rmg
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Citations: View citations in EconPapers (7)

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