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Determinants of cryptocurrency returns: A LASSO quantile regression approach

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  • Ciner, Cetin
  • Lucey, Brian
  • Yarovaya, Larisa

Abstract

We consider a relatively large set of predictors and investigate the determinants of cryptocurrency returns at different quantiles. Our analysis exclusively focuses on the highly volatile period of COVID-19. The innovation in the paper stems from the fact that we employ the LASSO penalty in a quantile regression framework to select informative variables. We find that US government bond indices and small company stock returns, a new predictor introduce in this study, significantly impact the tail behavior of the cryptocurrency returns.

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  • Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2022. "Determinants of cryptocurrency returns: A LASSO quantile regression approach," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322002380
    DOI: 10.1016/j.frl.2022.102990
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    References listed on IDEAS

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    1. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Gilchrist, Simon & Wei, Bin & Yue, Vivian Z. & Zakrajšek, Egon, 2024. "The Fed takes on corporate credit risk: An analysis of the efficacy of the SMCCF," Journal of Monetary Economics, Elsevier, vol. 146(C).
    4. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
    5. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    6. Nguyen, Linh Hoang & Chevapatrakul, Thanaset & Yao, Kai, 2020. "Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 333-355.
    7. van Dijk, Mathijs A., 2011. "Is size dead? A review of the size effect in equity returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3263-3274.
    8. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    9. Ren, Boru & Lucey, Brian, 2022. "A clean, green haven?—Examining the relationship between clean energy, clean and dirty cryptocurrencies," Energy Economics, Elsevier, vol. 109(C).
    10. Ciner, Cetin, 2021. "Stock return predictability in the time of COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
    11. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    12. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
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    Cited by:

    1. Etienne Harb & Charbel Bassil & Talie Kassamany & Roland Baz, 2024. "Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 951-981, March.
    2. Theodore Panagiotidis & Georgios Papapanagiotou, 2024. "A note on the determinants of NFTs returns," Discussion Paper Series 2024_02, Department of Economics, University of Macedonia, revised Feb 2024.
    3. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2024. "A Bayesian approach for the determinants of bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Adel Benhamed & Ahlem Selma Messai & Ghassen El Montasser, 2023. "On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    5. Poddar, Abhishek & Misra, Arun Kumar & Mishra, Ajay Kumar, 2023. "Return connectedness and volatility dynamics of the cryptocurrency network," Finance Research Letters, Elsevier, vol. 58(PB).
    6. Kawakami, Tabito, 2023. "Quantile prediction for Bitcoin returns using financial assets’ realized measures," Finance Research Letters, Elsevier, vol. 55(PA).
    7. Lu, Xunfa & Huang, Nan & Mo, Jianlei, 2024. "Time-varying causalities from the COVID-19 media coverage to the dynamic spillovers among the cryptocurrency, the clean energy, and the crude oil," Energy Economics, Elsevier, vol. 132(C).
    8. Ciner, Cetin & Kosedag, Arman & Lucey, Brian, 2023. "Predictors of clean energy stock returns: An analysis with best subset regressions," Finance Research Letters, Elsevier, vol. 55(PA).
    9. Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.

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