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What drives cryptoc...
What drives cryptocurrency returns? A sparse statistical jump model approach
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- Cortese, Federico P. (author)
- University of Milano-Bicocca
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- Kolm, Petter N. (author)
- New York University
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- Lindström, Erik (author)
- Lund University,Lunds universitet,Finansiell matematik,Forskargrupper vid Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Financial Mathematics Group,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2023
- 2023
- English.
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In: Digital Finance. - 2524-6984.
- Related links:
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http://dx.doi.org/10... (free)
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Abstract
Subject headings
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- 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.
Subject headings
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Publication and Content Type
- art (subject category)
- ref (subject category)
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