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Optimal consumption—portfolio problem with CVaR constraints

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  • Zhang, Qingye
  • Gao, Yan

Abstract

The optimal portfolio selection is a fundamental issue in finance, and its two most important ingredients are risk and return. Merton's pioneering work in dynamic portfolio selection emphasized only the expected utility of the consumption and the terminal wealth. To make the optimal portfolio strategy be achievable, risk control over bankruptcy during the investment horizon is an indispensable ingredient. So, in this paper, we consider the consumption-portfolio problem coupled with a dynamic risk control. More specifically, different from the existing literature, we impose a dynamic relative CVaR constraint on it. By the stochastic dynamic programming techniques, we derive the corresponding Hamilton–Jacobi–Bellman (HJB) equation. Moreover, by the Lagrange multiplier method, the closed form solution is provided when the utility function is a logarithmic one. At last, an illustrative empirical study is given. The results show the distinct difference of the portfolio strategies with and without the CVaR constraints: the proportion invested in the risky assets is reduced over time with CVaR constraint instead of being constant without CVaR constraints. This can provide a good decision-making reference for the investors.

Suggested Citation

  • Zhang, Qingye & Gao, Yan, 2016. "Optimal consumption—portfolio problem with CVaR constraints," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 516-521.
  • Handle: RePEc:eee:chsofr:v:91:y:2016:i:c:p:516-521
    DOI: 10.1016/j.chaos.2016.07.015
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    References listed on IDEAS

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    1. Shangmei Zhao & Qing Lu & Liyan Han & Yong Liu & Fei Hu, 2015. "A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution," Annals of Operations Research, Springer, vol. 226(1), pages 727-739, March.
    2. Yiu, K. F. C., 2004. "Optimal portfolios under a value-at-risk constraint," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1317-1334, April.
    3. Valenti, Davide & Spagnolo, Bernardo & Bonanno, Giovanni, 2007. "Hitting time distributions in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 311-320.
    4. G. Bonanno & D. Valenti & B. Spagnolo, 2005. "Role of Noise in a Market Model with Stochastic Volatility," Papers cond-mat/0510154, arXiv.org, revised Oct 2006.
    5. Dybvig, Philip H. & Liu, Hong, 2010. "Lifetime consumption and investment: Retirement and constrained borrowing," Journal of Economic Theory, Elsevier, vol. 145(3), pages 885-907, May.
    6. Lisa Borland, 2002. "A theory of non-Gaussian option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 415-431.
    7. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    8. Basak, Suleyman & Shapiro, Alexander, 2001. "Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 371-405.
    9. Duan Li & Wan‐Lung Ng, 2000. "Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation," Mathematical Finance, Wiley Blackwell, vol. 10(3), pages 387-406, July.
    10. Paul A. Samuelson, 2011. "Lifetime Portfolio Selection by Dynamic Stochastic Programming," World Scientific Book Chapters, in: Leonard C MacLean & Edward O Thorp & William T Ziemba (ed.), THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 31, pages 465-472, World Scientific Publishing Co. Pte. Ltd..
    11. Lisa Borland, 2002. "A Theory of Non_Gaussian Option Pricing," Papers cond-mat/0205078, arXiv.org, revised Dec 2002.
    12. Zhao, Pan & Xiao, Qingxian, 2016. "Portfolio selection problem with liquidity constraints under non-extensive statistical mechanics," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 5-10.
    13. Traian A. Pirvu, 2007. "Portfolio optimization under the Value-at-Risk constraint," Quantitative Finance, Taylor & Francis Journals, vol. 7(2), pages 125-136.
    14. Domenico Cuoco & Hua He & Sergei Isaenko, 2008. "Optimal Dynamic Trading Strategies with Risk Limits," Operations Research, INFORMS, vol. 56(2), pages 358-368, April.
    15. Bernardo Spagnolo & Davide Valenti, 2008. "Volatility Effects on the Escape Time in Financial Market Models," Papers 0810.1625, arXiv.org.
    16. Gao, Jianjun & Xiong, Yan & Li, Duan, 2016. "Dynamic mean-risk portfolio selection with multiple risk measures in continuous-time," European Journal of Operational Research, Elsevier, vol. 249(2), pages 647-656.
    17. J. Lehoczky & S. Sethi & S. Shreve, 1983. "Optimal Consumption and Investment Policies Allowing Consumption Constraints and Bankruptcy," Mathematics of Operations Research, INFORMS, vol. 8(4), pages 613-636, November.
    18. G. Bonanno & D. Valenti & B. Spagnolo, 2006. "Role of noise in a market model with stochastic volatility," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 53(3), pages 405-409, October.
    19. 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.
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    1. Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.

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