What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

31 Pages Posted: 20 Jan 2023

See all articles by Federico P. Cortese

Federico P. Cortese

CNR - IMATI

Petter N. Kolm

New York University (NYU) - Courant Institute of Mathematical Sciences

Erik Lindstrom

Lund University

Date Written: January 19, 2023

Abstract

We apply the statistical sparse jump model, a recently developed, interpretable and robust regime switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies.

The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new.

In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.

Keywords: Clustering, Blockchain, Cryptocurrencies, Feature Selection, Regime Switching, Unsupervised Learning

JEL Classification: G10, C32

Suggested Citation

Cortese, Federico and Kolm, Petter N. and Lindstrom, Erik, What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach (January 19, 2023). Available at SSRN: https://ssrn.com/abstract=4330421 or http://dx.doi.org/10.2139/ssrn.4330421

Federico Cortese

CNR - IMATI ( email )

via Bassini 15
Milano, 20133
Italy

HOME PAGE: http://https://www.imati.cnr.it/mypage.php?idk=PG-196

Petter N. Kolm (Contact Author)

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY 10012
United States

Erik Lindstrom

Lund University ( email )

Box 117
Lund, SC Skane S221 00
Sweden

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