IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/24222.html

Liquidity Regimes and Optimal Dynamic Asset Allocation

Author

Listed:
  • Pierre Collin-Dufresne
  • Kent D. Daniel
  • Mehmet Saǧlam

Abstract

We solve a portfolio choice problem when expected returns, volatilities and trading-costs follow a regime-switching model. The optimal policy trades towards an aim portfolio given by a weighted-average of the conditional mean-variance portfolios in all future states. The trading speed is higher in more persistent, riskier and higher-liquidity states. It can be optimal to overweight low Sharpe-ratio assets such as Treasury bonds because they remain liquid even in crisis states. We illustrate our methodology by constructing an optimal US equity market timing portfolio based on an estimated regime-switching model and on trading costs estimated using a large-order institutional trading dataset.

Suggested Citation

  • Pierre Collin-Dufresne & Kent D. Daniel & Mehmet Saǧlam, 2018. "Liquidity Regimes and Optimal Dynamic Asset Allocation," NBER Working Papers 24222, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24222
    Note: AP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w24222.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yan, Tingjin & Han, Jinhui & Ma, Guiyuan & Siu, Chi Chung, 2023. "Dynamic asset-liability management with frictions," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 57-83.
    2. Ma, Guiyuan & Siu, Chi Chung & Zhu, Song-Ping, 2022. "Portfolio choice with return predictability and small trading frictions," Economic Modelling, Elsevier, vol. 111(C).
    3. Patrick Chan & Ronnie Sircar & Iosif Zimbidis, 2025. "Optimal Trading under Instantaneous and Persistent Price Impact, Predictable Returns and Multiscale Stochastic Volatility," Papers 2507.17162, arXiv.org.
    4. Bryan Kelly & Semyon Malamud & Lasse Heje Pedersen, 2023. "Principal Portfolios," Journal of Finance, American Finance Association, vol. 78(1), pages 347-387, February.
    5. Xinyang Li, 2025. "Tail risk and Flight-to-Safety," Journal of Asset Management, Palgrave Macmillan, vol. 26(4), pages 386-410, July.
    6. Yakubu Suleiman Baguda & Hani Moaiteq AlJahdali & Altyeb Altaher Taha, 2025. "Dynamic Portfolio Return Classification Using Price-Aware Logistic Regression," Mathematics, MDPI, vol. 13(11), pages 1-31, June.
    7. Acharya, Viral V. & Pedersen, Lasse Heje, 2019. "Economics with Market Liquidity Risk," Critical Finance Review, now publishers, vol. 8(1-2), pages 111-125, December.
    8. Yu, Xing & Shen, Xilin & Li, Yanyan & Gong, Xue, 2023. "Selective hedging strategies for crude oil futures based on market state expectations," Global Finance Journal, Elsevier, vol. 57(C).
    9. Hematizadeh, Roksana & Tajaddini, Reza & Hallahan, Terrence, 2022. "Dynamic asset allocation strategy using a state-dependent Markov model: Applications to international equity markets," Journal of International Money and Finance, Elsevier, vol. 128(C).
    10. Alain Bensoussan & Guiyuan Ma & Chi Chung Siu & Sheung Chi Phillip Yam, 2022. "Dynamic mean–variance problem with frictions," Finance and Stochastics, Springer, vol. 26(2), pages 267-300, April.
    11. Carré, Sylvain & Collin-Dufresne, Pierre & Gabriel, Franck, 2022. "Insider trading with penalties," Journal of Economic Theory, Elsevier, vol. 203(C).
    12. Yu, Xing & Li, Yanyan & Zhao, Qian, 2024. "Research on optimization strategy of futures hedging dependent on market state," Applied Energy, Elsevier, vol. 373(C).
    13. Jia Yue & Ming-Hui Wang & Nan-Jing Huang, 2022. "Global Optimal Consumption–Portfolio Rules with Myopic Preferences and Loss Aversion," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1427-1455, December.
    14. Alessandro Micheli & Johannes Muhle-Karbe & Eyal Neuman, 2021. "Closed-Loop Nash Competition for Liquidity," Papers 2112.02961, arXiv.org, revised Jun 2023.
    15. Johannes Muhle-Karbe & Xiaofei Shi & Chen Yang, 2020. "An Equilibrium Model for the Cross-Section of Liquidity Premia," Papers 2011.13625, arXiv.org.
    16. Lukas Gonon & Johannes Muhle‐Karbe & Xiaofei Shi, 2021. "Asset pricing with general transaction costs: Theory and numerics," Mathematical Finance, Wiley Blackwell, vol. 31(2), pages 595-648, April.
    17. Roy Cerqueti & Carmine da Fermo & Marco Nicolosi, 2024. "Probabilities of transitions among endogenous regimes in asset returns and Environmental, Social and Governance scores," Post-Print hal-05114157, HAL.
    18. Jian'an Zhang, 2025. "FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management," Papers 2510.02986, arXiv.org.
    19. Anastasis Kratsios & Xiaofei Shi & Qiang Sun & Zhanhao Zhang, 2025. "Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement," Papers 2504.04300, arXiv.org.
    20. Alessandro Micheli & Johannes Muhle‐Karbe & Eyal Neuman, 2023. "Closed‐loop Nash competition for liquidity," Mathematical Finance, Wiley Blackwell, vol. 33(4), pages 1082-1118, October.

    More about this item

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:nbr:nberwo:24222. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.