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

Author

Listed:
  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

  • Christina Christou

    (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

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.

Suggested Citation

  • Elie Bouri & Christina Christou & Rangan Gupta, 2022. "Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models," Working Papers 202213, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202213
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    References listed on IDEAS

    as
    1. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
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    Cited by:

    1. Feng, Wenjun & Zhang, Zhengjun, 2023. "Risk-weighted cryptocurrency indices," Finance Research Letters, Elsevier, vol. 51(C).
    2. Tomas Pečiulis & Nisar Ahmad & Angeliki N. Menegaki & Aqsa Bibi, 2024. "Forecasting of cryptocurrencies: Mapping trends, influential sources, and research themes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1880-1901, September.
    3. He, Mengxi & Shen, Lihua & Zhang, Yaojie & Zhang, Yi, 2023. "Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor," Finance Research Letters, Elsevier, vol. 58(PA).
    4. 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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