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Expected returns with leverage constraints and target returns

Author

Listed:
  • Leon (Liang) Xin

    (JP Morgan Chase)

  • Shanshan Ding

    (JP Morgan Chase)

Abstract

Classic mean–variance optimization is very sensitive to expected returns. An alternative and more robust approach is to calculate the implied returns given the current portfolio allocation and risk profile. Portfolio managers can then do a reality check on the implied returns and find opportunities for better allocations. The most common implied return calculation assumes normal distribution and unlimited leverage, and use volatility as risk measure and covariance matrix as model input. However, practitioners usually have leverage constraints, often use non-parametric risk models, and care about portfolio tail risk. This paper presents a new approach to calculate expected returns with leverage constraints. This approach is flexible enough to alleviate normal distribution assumption, connect with non-parametric risk models, and use tail risk measures, such as conditional VaR.

Suggested Citation

  • Leon (Liang) Xin & Shanshan Ding, 2021. "Expected returns with leverage constraints and target returns," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 200-208, May.
    • Handle: RePEc:pal:assmgt:v:22:y:2021:i:3:d:10.1057_s41260-020-00199-6
    DOI: 10.1057/s41260-020-00199-6
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    References listed on IDEAS

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    1. S Satchell & A Scowcroft, 2000. "A demystification of the Black–Litterman model: Managing quantitative and traditional portfolio construction," Journal of Asset Management, Palgrave Macmillan, vol. 1(2), pages 138-150, September.
    2. Ulf Herold, 2005. "Computing implied returns in a meaningful way," Journal of Asset Management, Palgrave Macmillan, vol. 6(1), pages 53-64, June.
    3. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    4. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    5. Sharpe, William F., 1974. "Imputing Expected Security Returns from Portfolio Composition," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(3), pages 463-472, June.
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