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

Theodore Panagiotidis, Thanasis Stengos and Orestis Vravosinos

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.

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Year of publication:	2020
Authors:	Panagiōtidēs, Theodōros ; Stengos, Thanasēs ; Vravosinos, Orestis
Published in:	Journal of risk and financial management : JRFM. - Basel : MDPI, ISSN 1911-8074, ZDB-ID 2739117-6. - Vol. 13.2020, 2/33, p. 1-10
Subject:	bitcoin \| bubble \| cryptocurrency \| flexible least squares \| LASSO \| PC-LASSO \| principal component \| sparse regression \| Regressionsanalyse \| Regression analysis \| Virtuelle Währung \| Virtual currency \| Schätztheorie \| Estimation theory \| Spekulationsblase \| Bubbles

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Type of publication:	Article
Type of publication (narrower categories):	Aufsatz in Zeitschrift ; Article in journal
Language:	English
Other identifiers:	10.3390/jrfm13020033 [DOI] hdl:10419/239110 [Handle]
Source:	ECONIS - Online Catalogue of the ZBW

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