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

15 Pages Posted: 16 Jan 2020

See all articles by Theodore Panagiotidis

Theodore Panagiotidis

University of Macedonia - Department of Economics

Thanasis Stengos

University of Guelph - Department of Economics

Orestis Vravosinos

New York University - Department of Economics

Date Written: December 27, 2019

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

JEL Classification: G12; G15

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

Theodore Panagiotidis (Contact Author)

University of Macedonia - Department of Economics ( email )

Thessaloniki, 54006
Greece

HOME PAGE: http://users.uom.gr/~tpanag/

Thanasis Stengos

University of Guelph - Department of Economics ( email )

50 Stone Road East
Guelph, Ontario N1G 2W1
Canada

Orestis Vravosinos

New York University - Department of Economics ( email )

19 West 4th Street
New York, NY 10003
United States

HOME PAGE: http://https://orestisvravosinos.netlify.com/

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
12
Abstract Views
151
PlumX Metrics