A principal component-guided sparse regression approach for the determination of bitcoin returns

Thanasis Stengos, Theodore Panagiotidis and Orestis Vravosinos

No 2001, Working Papers from University of Guelph, Department of Economics and Finance

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: G12 G15 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2020
New Economics Papers: this item is included in nep-ets, nep-fmk, nep-ore and nep-pay
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Citations: View citations in EconPapers (11)

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Journal Article: A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:gue:guelph:2020-01

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