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.

Cortese, F., Kolm, P., Lindström, E. (2023). What drives cryptocurrency returns? A sparse statistical jump model approach. DIGITAL FINANCE, 5(3-4), 483-518 [10.1007/s42521-023-00085-x].

What drives cryptocurrency returns? A sparse statistical jump model approach

Cortese, Federico P.
Primo
;
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.
Articolo in rivista - Articolo scientifico
62H30; 62M05; 62P05; 68T99; 91G15; Blockchain; C32; Clustering; Cryptocurrencies; Feature selection; G10; Regime switching; Unsupervised learning;
English
20-mag-2023
2023
5
3-4
483
518
open
Cortese, F., Kolm, P., Lindström, E. (2023). What drives cryptocurrency returns? A sparse statistical jump model approach. DIGITAL FINANCE, 5(3-4), 483-518 [10.1007/s42521-023-00085-x].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/416797
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