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Portfolio Optimization for Assets with Stochastic Yields and Stochastic Volatility

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  • Tao Pang

    (North Carolina State University)

  • Katherine Varga

    (CoBank)

Abstract

In this paper, we consider a stochastic portfolio optimization model for investment on a risky asset with stochastic yields and stochastic volatility. The problem is formulated as a stochastic control problem, and the goal is to choose the optimal investment and consumption controls to maximize the investor’s expected total discounted utility. The Hamilton–Jacobi–Bellman equation is derived by virtue of the dynamic programming principle, which is a second-order nonlinear equation. Using the subsolution–supersolution method, we establish the existence result of the classical solution of the equation. Finally, we verify that the solution is equal to the value function and derive and verify the optimal investment and consumption controls.

Suggested Citation

  • Tao Pang & Katherine Varga, 2019. "Portfolio Optimization for Assets with Stochastic Yields and Stochastic Volatility," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 691-729, August.
    • Handle: RePEc:spr:joptap:v:182:y:2019:i:2:d:10.1007_s10957-019-01513-y
    DOI: 10.1007/s10957-019-01513-y
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    References listed on IDEAS

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    1. Geske, Robert, 1978. "The Pricing of Options with Stochastic Dividend Yield," Journal of Finance, American Finance Association, vol. 33(2), pages 617-625, May.
    2. Tao Pang, 2006. "Stochastic Portfolio Optimization With Log Utility," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 869-887.
    3. R. S. Tunaru, 2018. "Dividend derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 63-81, January.
    4. Thaleia Zariphopoulou, 2001. "A solution approach to valuation with unhedgeable risks," Finance and Stochastics, Springer, vol. 5(1), pages 61-82.
    5. T. Pang, 2004. "Portfolio Optimization Models on Infinite-Time Horizon," Journal of Optimization Theory and Applications, Springer, vol. 122(3), pages 573-597, September.
    6. Jean-Pierre Fouque & Chuan-Hsiang Han, 2003. "Pricing Asian options with stochastic volatility," Quantitative Finance, Taylor & Francis Journals, vol. 3(5), pages 353-362.
    7. M. Goel & K. S. Kumar, 2009. "Risk-Sensitive Portfolio Optimization Problems with Fixed Income Securities," Journal of Optimization Theory and Applications, Springer, vol. 142(1), pages 67-84, July.
    8. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
    9. Lorella Fatone & Francesca Mariani & Maria Cristina Recchioni & Francesco Zirilli, 2009. "An explicitly solvable multi‐scale stochastic volatility model: Option pricing and calibration problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(9), pages 862-893, September.
    10. Jean-Pierre Fouque & Ronnie Sircar & Thaleia Zariphopoulou, 2017. "Portfolio Optimization And Stochastic Volatility Asymptotics," Mathematical Finance, Wiley Blackwell, vol. 27(3), pages 704-745, July.
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    Cited by:

    1. Frank Phillipson & Harshil Singh Bhatia, 2020. "Portfolio Optimisation Using the D-Wave Quantum Annealer," Papers 2012.01121, arXiv.org.

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