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A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

1
Department of Economics, University of Macedonia, Thessaloniki 54636, Greece
2
Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada
3
Department of Economics, New York University, New York, NY 10003, USA
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(2), 33; https://doi.org/10.3390/jrfm13020033
Received: 16 November 2019 / Revised: 16 January 2020 / Accepted: 3 February 2020 / Published: 13 February 2020
We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period. View Full-Text
Keywords: bitcoin; cryptocurrency; bubble; sparse regression; LASSO; PC-LASSO; principal component; flexible least squares bitcoin; cryptocurrency; bubble; sparse regression; LASSO; PC-LASSO; principal component; flexible least squares
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Panagiotidis, T.; Stengos, T.; Vravosinos, O. A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns. J. Risk Financial Manag. 2020, 13, 33.

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