IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v50y2022ics154461232200438x.html
   My bibliography  Save this article

Analyzing diversification benefits of cryptocurrencies through backfill simulation

Author

Listed:
  • Kim, Jang Ho

Abstract

The cryptocurrency market provides an interesting diversification opportunity for asset allocation due to its fundamental differences compared to traditional asset classes. Even though the cryptocurrency market experienced a surge until 2017, the market has relatively stabilized since the bubble along with increased participation from institutional investors. Instead of only performing analysis for the recent years, which is short for testing asset allocation benefits, we focus on the post-bubble market condition but backfill cryptocurrency returns into the past in order to analyze a longer investment horizon. In our backfill simulation, investment in cryptocurrencies show higher return but mixed results in terms of portfolio efficiency for risk-based optimized portfolios. Even though risk-based models allocate a small portion in cryptocurrencies, its high volatility limits diversification benefits since even small allocations often lead to higher portfolio risk.

Suggested Citation

  • Kim, Jang Ho, 2022. "Analyzing diversification benefits of cryptocurrencies through backfill simulation," Finance Research Letters, Elsevier, vol. 50(C).
  • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s154461232200438x
    DOI: 10.1016/j.frl.2022.103238
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232200438X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2022.103238?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sifat, Imtiaz, 2021. "On cryptocurrencies as an independent asset class: Long-horizon and COVID-19 pandemic era decoupling from global sentiments," Finance Research Letters, Elsevier, vol. 43(C).
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. repec:dau:papers:123456789/4688 is not listed on IDEAS
    4. Stambaugh, Robert F., 1997. "Analyzing investments whose histories differ in length," Journal of Financial Economics, Elsevier, vol. 45(3), pages 285-331, September.
    5. David Yermack, 2013. "Is Bitcoin a Real Currency? An economic appraisal," NBER Working Papers 19747, National Bureau of Economic Research, Inc.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    7. Andros Gregoriou, 2019. "Cryptocurrencies and asset pricing," Applied Economics Letters, Taylor & Francis Journals, vol. 26(12), pages 995-998, July.
    8. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    9. Mourad Mroua & Slah Bahloul & Nader Naifar, 2022. "Should investors include bitcoin in their portfolio? New evidence from a bootstrap-based stochastic dominance approach," Applied Economics Letters, Taylor & Francis Journals, vol. 29(1), pages 53-62, January.
    10. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    11. Bianchi, Daniele & Babiak, Mykola, 2022. "On the performance of cryptocurrency funds," Journal of Banking & Finance, Elsevier, vol. 138(C).
    12. Liu, Weiyi, 2019. "Portfolio diversification across cryptocurrencies," Finance Research Letters, Elsevier, vol. 29(C), pages 200-205.
    13. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khaki, Audil & Prasad, Mason & Al-Mohamad, Somar & Bakry, Walid & Vo, Xuan Vinh, 2023. "Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Huang, Linxian, 2024. "The relationship between cryptocurrencies and convention financial market: Dynamic causality test and time-varying influence," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 811-826.
    3. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    4. Moreno, David & Antoli, Marcos & Quintana, David, 2022. "Benefits of investing in cryptocurrencies when liquidity is a factor," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    7. Dobrynskaya, Victoria, 2024. "Is downside risk priced in cryptocurrency market?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    8. Ngo, Vu Minh & Nguyen, Huan Huu & Van Nguyen, Phuc, 2023. "Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?," Research in International Business and Finance, Elsevier, vol. 65(C).
    9. Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
    10. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    11. Neveen Ahmed & Omar Farooq & Nidaa Hamed, 2023. "Relation Between Bitcoin and Its Forks: An Empirical Investigation," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 49(2), pages 249-261, April.
    12. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    13. Tim Schmitz & Ingo Hoffmann, 2020. "Re-evaluating cryptocurrencies' contribution to portfolio diversification -- A portfolio analysis with special focus on German investors," Papers 2006.06237, arXiv.org, revised Aug 2020.
    14. Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
    17. Arkorful, Gideon Bruce & Chen, Haiqiang & Gu, Ming & Liu, Xiaoqun, 2023. "What can we learn from the convenience yield of Bitcoin? Evidence from the COVID-19 crisis," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 141-153.
    18. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
    19. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
    20. Nakagawa, Kei & Sakemoto, Ryuta, 2022. "Cryptocurrency network factors and gold," Finance Research Letters, Elsevier, vol. 46(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:50:y:2022:i:c:s154461232200438x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.