What drives cryptocurrency returns? A sparse statistical jump model approach
Cortese, Federico LU ; Kolm, Petter Nils LU and Lindström, Erik LU
(2023)
6th International Conference on Econometrics and Statistics
- Abstract
- The statistical sparse jump model, a recently developed, robust and interpretable regime-switching model, is used to analyze the factors driving the return dynamics of the largest cryptocurrencies. This method simultaneously incorporates feature selection, parameter estimation, and state classification. A wide range of candidate features is considered, including cryptocurrency, sentiment, and financial market-based time series that are known to influence cryptocurrency returns. The empirical analysis demonstrates that a three-state model provides a good representation of the cryptocurrency return dynamics. The latent states are interpreted as a bull, neutral, and bear market regimes, respectively. Through the data-driven feature selection... (More)
- The statistical sparse jump model, a recently developed, robust and interpretable regime-switching model, is used to analyze the factors driving the return dynamics of the largest cryptocurrencies. This method simultaneously incorporates feature selection, parameter estimation, and state classification. A wide range of candidate features is considered, including cryptocurrency, sentiment, and financial market-based time series that are known to influence cryptocurrency returns. The empirical analysis demonstrates that a three-state model provides a good representation of the cryptocurrency return dynamics. The latent states are interpreted as a bull, neutral, and bear market regimes, respectively. Through the data-driven feature selection approach, the significant factors are identified, and insignificant ones are excluded. The results indicate that within the candidate features, the first moments of returns, features indicating trends and reversal signals, market activity, and public attention are key drivers of crypto market dynamics. (Less)
- Abstract (Swedish)
- The statistical sparse jump model, a recently developed, robust and interpretable regime-switching model, is used to analyze the factors driving the return dynamics of the largest cryptocurrencies. This method simultaneously incorporates feature selection, parameter estimation, and state classification. We consider a wide range of candidate features, including cryptocurrency, sentiment, and financial market-based time series that are known to influence cryptocurrency returns. Our empirical analysis demonstrates that a three-state model provides a good representation of the cryptocurrency return dynamics. We interpret the latent states as bull, neutral, and bear market regimes, respectively. Through our data-driven feature selection... (More)
- The statistical sparse jump model, a recently developed, robust and interpretable regime-switching model, is used to analyze the factors driving the return dynamics of the largest cryptocurrencies. This method simultaneously incorporates feature selection, parameter estimation, and state classification. We consider a wide range of candidate features, including cryptocurrency, sentiment, and financial market-based time series that are known to influence cryptocurrency returns. Our empirical analysis demonstrates that a three-state model provides a good representation of the cryptocurrency return dynamics. We interpret the latent states as bull, neutral, and bear market regimes, respectively. Through our data-driven feature selection approach, we are able to identify the significant factors and exclude insignificant ones. Our results indicate that within the candidate features, the first moments of returns, features indicating 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/d7853f9f-1ddf-4210-88ff-1f15d72c3daf
- author
- Cortese, Federico
LU
; Kolm, Petter Nils
LU
and Lindström, Erik
LU
- organization
- publishing date
- 2023-08-03
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- 6th International Conference on Econometrics and Statistics
- conference location
- Tokyo, Japan
- conference dates
- 2023-08-01 - 2023-08-03
- language
- English
- LU publication?
- yes
- id
- d7853f9f-1ddf-4210-88ff-1f15d72c3daf
- date added to LUP
- 2023-06-17 21:47:39
- date last changed
- 2025-01-27 15:11:03
@misc{d7853f9f-1ddf-4210-88ff-1f15d72c3daf,
abstract = {{The statistical sparse jump model, a recently developed, robust and interpretable regime-switching model, is used to analyze the factors driving the return dynamics of the largest cryptocurrencies. This method simultaneously incorporates feature selection, parameter estimation, and state classification. A wide range of candidate features is considered, including cryptocurrency, sentiment, and financial market-based time series that are known to influence cryptocurrency returns. The empirical analysis demonstrates that a three-state model provides a good representation of the cryptocurrency return dynamics. The latent states are interpreted as a bull, neutral, and bear market regimes, respectively. Through the data-driven feature selection approach, the significant factors are identified, and insignificant ones are excluded. The results indicate that within the candidate features, the first moments of returns, features indicating trends and reversal signals, market activity, and public attention are key drivers of crypto market dynamics.}},
author = {{Cortese, Federico and Kolm, Petter Nils and Lindström, Erik}},
language = {{eng}},
month = {{08}},
title = {{What drives cryptocurrency returns? A sparse statistical jump model approach}},
year = {{2023}},
}