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A principal component-guided sparse regression approach for the determination of bitcoin returns

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Listed:
  • Thanasis Stengos

    (Department of Economics and Finance, University of Guelph, Guelph ON Canada)

  • Theodore Panagiotidis

    (Department of Economics, University of Macedonia)

  • Orestis Vravosinos

    (Department of Economics, New York University)

Abstract

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2,872 daily observations). The principal component-guided sparse regression is employed, introduced by Tay et al. (2018). We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017-2018 period.

Suggested Citation

  • Thanasis Stengos & Theodore Panagiotidis & Orestis Vravosinos, 2020. "A principal component-guided sparse regression approach for the determination of bitcoin returns," Working Papers 2001, University of Guelph, Department of Economics and Finance.
    • Handle: RePEc:gue:guelph:2020-01
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    References listed on IDEAS

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    1. Schilling, Linda & Uhlig, Harald, 2019. "Some simple bitcoin economics," Journal of Monetary Economics, Elsevier, vol. 106(C), pages 16-26.
    2. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    3. Peter C. B. Phillips & Shu-Ping Shi & Jun Yu, 2011. "Testing for Multiple Bubbles," Working Papers CoFie-03-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    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. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    6. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    7. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Limit Theory Of Real‐Time Detectors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1079-1134, November.
    8. Kalaba, Robert & Rasakhoo, Nima & Tesfatsion, Leigh, 1989. "A FORTRAN program for time-varying linear regression via flexible least squares," Computational Statistics & Data Analysis, Elsevier, vol. 7(3), pages 291-309, February.
    9. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    10. Kalaba, Robert E. & Tesfatsion, Leigh S., 1989. "Time-Varying Linear Regression Via Flexible Least Squares," Staff General Research Papers Archive 11196, Iowa State University, Department of Economics.
    11. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    12. Łukasz Goczek & Ivan Skliarov, 2019. "What drives the Bitcoin price? A factor augmented error correction mechanism investigation," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6393-6410, December.
    13. Caspi, Itamar, 2013. "Rtadf: Testing for Bubbles with EViews," MPRA Paper 58791, University Library of Munich, Germany, revised 06 Sep 2014.
    14. Jin, Jingyu & Yu, Jiang & Hu, Yang & Shang, Yue, 2019. "Which one is more informative in determining price movements of hedging assets? Evidence from Bitcoin, gold and crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    15. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
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    Cited by:

    1. Emon Kalyan Chowdhury & Mohammad Nayeem Abdullah, 2024. "Gauging Demand for Cryptocurrency over the Economic Policy Uncertainty and Stock Market Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 37-55, July.
    2. Thanasis Stengos, 2021. "Recent Developments in Cryptocurrency Markets: Co-Movements, Spillovers and Forecasting," JRFM, MDPI, vol. 14(3), pages 1-3, February.
    3. Pınar Kaya Soylu & Mustafa Okur & Özgür Çatıkkaş & Z. Ayca Altintig, 2020. "Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple," JRFM, MDPI, vol. 13(6), pages 1-21, May.
    4. Nikolaos A. Kyriazis, 2020. "Is Bitcoin Similar to Gold? An Integrated Overview of Empirical Findings," JRFM, MDPI, vol. 13(5), pages 1-19, May.
    5. Mokni, Khaled & Youssef, Manel & Ajmi, Ahdi Noomen, 2022. "COVID-19 pandemic and economic policy uncertainty: The first test on the hedging and safe haven properties of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 60(C).
    6. 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.
    7. Umar, Muhammad & Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Shao, Xue-Feng, 2021. "Bitcoin: A safe haven asset and a winner amid political and economic uncertainties in the US?," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. Pinar Deniz & Thanasis Stengos, 2020. "Cryptocurrency Returns before and after the Introduction of Bitcoin Futures," JRFM, MDPI, vol. 13(6), pages 1-21, June.
    9. Zvonko Merkaš & Vlasta Roška, 2021. "The Impact of Unsystematic Factors on Bitcoin Value," JRFM, MDPI, vol. 14(11), pages 1-17, November.
    10. Mokni, Khaled & Ajmi, Ahdi Noomen & Bouri, Elie & Vo, Xuan Vinh, 2020. "Economic policy uncertainty and the Bitcoin-US stock nexus," Journal of Multinational Financial Management, Elsevier, vol. 57.
    11. Serda Selin Ozturk, 2020. "Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns," JRFM, MDPI, vol. 13(11), pages 1-14, November.

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    More about this item

    Keywords

    bitcoin; cryptocurrency; bubble; sparse regression; LASSO; PC-LASSO; principal component; flexible least squares;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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    This paper has been announced in the following NEP Reports:

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