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What drives cryptoc...
What drives cryptocurrency returns? A sparse statistical jump model approach
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- Cortese, Federico (författare)
- University of Milano-Bicocca
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- Kolm, Petter Nils (författare)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Lindström, Erik (författare)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
- Relaterad länk:
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https://lup.lub.lu.s...
Abstract
Ämnesord
Stäng
- 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.
- 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.
Ämnesord
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
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