IDEAS home Printed from https://ideas.repec.org/p/pri/econom/2019-1.html

Managing a Crypto-Currency Portfolio Via Minmax Drawdown Control

Author

Listed:
  • Sylvain Chassang

    (New York University)

Abstract

Crypto-currencies and other innovative asset classes present a fundamental challenge for quantitative asset-allocation. Because the track record of innovative assets is by definition short, it is difficult to form reliable estimates of expected returns and covariance matrices needed as inputs for standard portfolio optimization. Even if such estimates are available, they may be useless to investors if the behavior of underlying assets changes over time. Building on the MinMax Drawdown Control framework of Chassang (2018), this paper proposes a conceptually attractive and empirically successful approach to build benchmark portfolios of crypto-currencies and other innovative assets.

Suggested Citation

  • Sylvain Chassang, 2019. "Managing a Crypto-Currency Portfolio Via Minmax Drawdown Control," Working Papers 2019-1, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2019-1
    as

    Download full text from publisher

    File URL: https://www.sylvainchassang.org/assets/papers/crypto_portfolio_management.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    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. Yuhui Jin, 2024. "GraphCNNpred: A stock market indices prediction using a Graph based deep learning system," Papers 2407.03760, arXiv.org, revised Jul 2024.
    2. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    3. Tae-Hwy Lee & Ekaterina Seregina, 2024. "Optimal Portfolio Using Factor Graphical Lasso," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 670-695.
    4. Weilong Liu & Yanchu Liu, 2025. "Covariance Matrix Estimation for Positively Correlated Assets," Papers 2507.01545, arXiv.org.
    5. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joëlle, 2019. "A comprehensive appraisal of style-integration methods," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 134-150.
    6. Bruder, Benjamin & Hereil, Pierre & Roncalli, Thierry, 2011. "Managing sovereign credit risk in bond portfolios," MPRA Paper 36673, University Library of Munich, Germany.
    7. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    8. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    9. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    10. Gambacciani, Marco & Paolella, Marc S., 2017. "Robust normal mixtures for financial portfolio allocation," Econometrics and Statistics, Elsevier, vol. 3(C), pages 91-111.
    11. Ding, Wenliang & Shu, Lianjie & Gu, Xinhua, 2023. "A robust Glasso approach to portfolio selection in high dimensions," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 22-37.
    12. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    13. Eckhard Platen & Renata Rendek, 2017. "Market Efficiency and Growth Optimal Portfolio," Papers 1706.06832, arXiv.org.
    14. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    15. Sarunas Raudys & Aistis Raudys & Zidrina Pabarskaite, 2018. "Dynamically Controlled Length of Training Data for Sustainable Portfolio Selection," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    16. Cesarone, Francesco & Puerto, Justo, 2025. "Flexible enhanced indexation models through stochastic dominance and ordered weighted average optimization," European Journal of Operational Research, Elsevier, vol. 323(2), pages 657-670.
    17. Liusha Yang & Romain Couillet & Matthew R. McKay, 2015. "A Robust Statistics Approach to Minimum Variance Portfolio Optimization," Papers 1503.08013, arXiv.org.
    18. Benjamin Hippert & André Uhde & Sascha Tobias Wengerek, 2019. "Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe," Review of Derivatives Research, Springer, vol. 22(2), pages 203-259, July.
    19. Kaltsounis, Anastasios & Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1404-1412.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    Statistics

    Access and download statistics

    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:pri:econom:2019-1. 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: Bobray Bordelon (email available below). General contact details of provider: https://edirc.repec.org/data/deprius.html .

    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.