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

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2,872 daily observations). The principal component-guided sparse regression is employed, introduced by Tay et al. (2018). 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.

Keywords: bitcoin; cryptocurrency; bubble; sparse regression; LASSO; PC-LASSO; principal component; flexible least squares

JEL Classification: G12; G15

Suggested Citation: Suggested Citation

Panagiotidis, Theodore and Stengos, Thanasis and Vravosinos, Orestis, A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns (December 27, 2019). Available at SSRN: https://ssrn.com/abstract=3510816 or http://dx.doi.org/10.2139/ssrn.3510816