Pair trading strategies in the cryptoassets market: a cointegration framework with optimized thresholds using genetic algorithms

Lorette Danilo, Fayssal Jamhamed and Franck Martin ()
Additional contact information
Lorette Danilo: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Fayssal Jamhamed: Arkéa
Franck Martin: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

Abstract: The crypto-assets markets are notoriously volatile and risky. In this context, market-neutral type strategies, such as pair-trading, may be relevant. In this paper, we focus on the implementation of pair-trading strategies with a wide range of crypto-assets over periods between August 2021 and January 2024. To carry out this study, we combine econometric and machine learning techniques which differ from those used in existing literature on the subject. By using cointegration tests and error correction models, we identify a sample of 229 pairs suitable for pair-trading strategies. Using a genetic algorithm and pair clustering, we test four strategies using standard and optimized thresholds. The results highlight the existence of profitable cointegrating relationships, and, therefore, short-term market inefficiencies in the crypto-assets market. Indeed, though still risky, the best strategy identified in terms of risk-return trade-off, with a median maxdrawdown of 15.29%, delivers an average annual Sharpe ratio per pair of 0.69 over the out-of-sample period.

Keywords: C22; C61; G11; G12; G14; Short-term market inefficiencies; K-means; Genetic algorithm; Error-correction models; Cointegration; Pair-trading; Cryptoassets (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Published in Quantitative Finance, 2026, 26 (5), pp.799-821. ⟨10.1080/14697688.2026.2653663⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05654972

DOI: 10.1080/14697688.2026.2653663

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().