What drives cryptocurrency returns? A sparse statistical jump model approach
Cortese, Federico P. LU ; Kolm, Petter N. LU and Lindström, Erik LU
(2023)
In Digital Finance
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/cc92fb8e-fbdd-41d7-86b5-f69601f7ee10
- author
- Cortese, Federico P.
LU
; Kolm, Petter N.
LU
and Lindström, Erik
LU
- organization
- publishing date
- 2023-05-20
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Digital Finance
- publisher
- Springer
- ISSN
- 2524-6984
- DOI
- 10.1007/s42521-023-00085-x
- language
- English
- LU publication?
- yes
- id
- cc92fb8e-fbdd-41d7-86b5-f69601f7ee10
- date added to LUP
- 2023-06-14 16:41:55
- date last changed
- 2023-09-14 15:05:20
@article{cc92fb8e-fbdd-41d7-86b5-f69601f7ee10,
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.}},
author = {{Cortese, Federico P. and Kolm, Petter N. and Lindström, Erik}},
issn = {{2524-6984}},
language = {{eng}},
month = {{05}},
publisher = {{Springer}},
series = {{Digital Finance}},
title = {{What drives cryptocurrency returns? A sparse statistical jump model approach}},
url = {{http://dx.doi.org/10.1007/s42521-023-00085-x}},
doi = {{10.1007/s42521-023-00085-x}},
year = {{2023}},
}