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A Dynamic Model of Private Asset Allocation

Author

Listed:
  • Hui Chen
  • Giovanni Gambarotta
  • Simon Scheidegger
  • Yu Xu

Abstract

We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors' portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges.

Suggested Citation

  • Hui Chen & Giovanni Gambarotta & Simon Scheidegger & Yu Xu, 2025. "A Dynamic Model of Private Asset Allocation," Papers 2503.01099, arXiv.org.
    • Handle: RePEc:arx:papers:2503.01099
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    References listed on IDEAS

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    1. Getmansky, Mila & Lo, Andrew W. & Makarov, Igor, 2004. "An econometric model of serial correlation and illiquidity in hedge fund returns," Journal of Financial Economics, Elsevier, vol. 74(3), pages 529-609, December.
    2. Nadauld, Taylor D. & Sensoy, Berk A. & Vorkink, Keith & Weisbach, Michael S., 2019. "The liquidity cost of private equity investments: Evidence from secondary market transactions," Journal of Financial Economics, Elsevier, vol. 132(3), pages 158-181.
    3. Geltner, David Michael, 1991. "Smoothing in Appraisal-Based Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 4(3), pages 327-345, September.
    4. Luca Gaegauf & Simon Scheidegger & Fabio Trojani, 2023. "A Comprehensive Machine Learning Framework for Dynamic Portfolio Choice With Transaction Costs," Swiss Finance Institute Research Paper Series 23-114, Swiss Finance Institute.
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