| 1. |
- Cortese, Federico, et al.
(författare)
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GENERALIZED INFORMATION CRITERIA FOR SPARSE STATISTICAL JUMP MODELS
- 2023
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Ingår i: Symposium i anvendt statistik 2023. - 9788798937036 ; , s. 68-78
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Bokkapitel (refereegranskat)abstract
- We extend the generalized information criteria for high-dimensional penalizedmodels to sparse statistical jump models, a new class of statistically robust and computationally efficient alternatives to hidden Markov models. In a simulation study, we demonstrate that the new generalized information criteria selects the correct hyperparameters with high probability. Finally, providing an empirical application, we infer the key features that drive the return dynamics of the largest cryptocurrencies. We find that a four-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, bull-neutral, bear-neutral, and bear market regimes, respectively.
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| 2. |
- Cortese, Federico, et al.
(författare)
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What drives cryptocurrency returns? A sparse statistical jump model approach
- 2023
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Konferensbidrag (refereegranskat)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.
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| 3. |
- Cortese, Federico, et al.
(författare)
-
What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach
- 2023
-
Konferensbidrag (refereegranskat)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 publicattention are key drivers of crypto market dynamics.
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