What determines bitcoin liquidity? A penalized regression approach

ABSTRACT

We investigate which factors contribute most to the liquidity of Bitcoin, using a diverse universe of candidate factors reflecting key developments in the crypto market and the global economy. The empirical analysis relies on three regularized linear regression methods, viz. LASSO, adaptive LASSO, and elastic net. We also apply a cross-fit partialing-out LASSO instrumental-variables regression model, as a supplementary approach to handle endogeneity. Findings reveal that trading volume and realized volatility of Bitcoin, cryptocurrency hacks, Ethereum liquidity, and public attention are the most common drivers of liquidity, irrespective of the penalized regression approach and liquidity proxy adopted. Our evidence confirms the paramountcy of cryptocurrency-specific factors over global economic and financial ones in influencing Bitcoin liquidity.

KEYWORDS:

• Bitcoin
• liquidity determinants
• LASSO
• adaptive LASSO
• elastic net

JEL CLASSIFICATION:

• C32
• C53
• G10

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The earliest price data available for Ethereum govern the beginning date of the sample.

² Appendix A reports the largest cryptocurrency hacking incidents considered in the analysis.

³ For space considerations, graphs of coefficient paths wherein the AR estimator is the regressand are not shown here, but available upon request.