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What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

Cortese, Federico LU ; Kolm, Petter Nils LU and Lindström, Erik LU orcid (2023) 29th Nordic Conference in Mathematical Statistics
Abstract
We consider the statistical sparse jump model, a recently developed, robust and interpretable regime switching model, to identify features that drive the return dynamics of the largest cryptocurrencies. The approach simultaneously performs feature selection, parameter estimation, and state classification. Our large number of candidate features comprises cryptocurrency, sentiment, and financial market-based time series that previously have been identified in the emerging literature as influencing cryptocurrency returns, as well as new ones. Our empirical study indicates that a three-state model offers the most accurate description of the cryptocurrency returns dynamics. These states have straightforward market-based interpretations as they... (More)
We consider the statistical sparse jump model, a recently developed, robust and interpretable regime switching model, to identify features that drive the return dynamics of the largest cryptocurrencies. The approach simultaneously performs feature selection, parameter estimation, and state classification. Our large number of candidate features comprises cryptocurrency, sentiment, and financial market-based time series that previously have been identified in the emerging literature as influencing cryptocurrency returns, as well as new ones. Our empirical study indicates that a three-state model offers the most accurate description of the cryptocurrency returns dynamics. These states have straightforward 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. Our findings reveal that, among the set of candidate features, the first moments of returns, features that represent trends and reversal signals, market activity, and public
attention are key drivers of crypto market dynamics. (Less)
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organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
29th Nordic Conference in Mathematical Statistics
conference location
Gothenburg, Sweden
conference dates
2023-06-19 - 2023-06-22
language
English
LU publication?
yes
id
4540d6d8-7f98-4fe4-a956-c988cc9d7200
date added to LUP
2023-06-17 21:51:36
date last changed
2024-03-19 09:36:37
@misc{4540d6d8-7f98-4fe4-a956-c988cc9d7200,
  abstract     = {{We consider the statistical sparse jump model, a recently developed, robust and interpretable regime switching model, to identify features that drive the return dynamics of the largest cryptocurrencies. The approach simultaneously performs feature selection, parameter estimation, and state classification. Our large number of candidate features comprises cryptocurrency, sentiment, and financial market-based time series that previously have been identified in the emerging literature as influencing cryptocurrency returns, as well as new ones. Our empirical study indicates that a three-state model offers the most accurate description of the cryptocurrency returns dynamics. These states have straightforward 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. Our findings reveal that, among the set of candidate features, the first moments of returns, features that represent trends and reversal signals, market activity, and public<br/>attention are key drivers of crypto market dynamics.}},
  author       = {{Cortese, Federico and Kolm, Petter Nils and Lindström, Erik}},
  language     = {{eng}},
  month        = {{06}},
  title        = {{What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach}},
  year         = {{2023}},
}