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Constrained Stochastic Estimation Algorithms for a Class of Hybrid Stock Market Models

Author

Listed:
  • G. Yin

    (Wayne State University)

  • Q. Zhang

    (University of Georgia)

  • K. Yin

    (University of Minnesota)

Abstract

This paper is concerned with a class of hybrid stock market models, in which both the return rate and the volatility depend on a hidden, continuous-time Markov chain with a finite state space. One of the crucial issues is to estimate the generator of the underlying Markov chain. We develop a stochastic optimization procedure for this task, prove its convergence, and establish the rate of convergence. Numerical tests are carried out via simulation as well as using real market data. In addition, we demonstrate how to use the estimated generator in making stock liquidation decisions.

Suggested Citation

  • G. Yin & Q. Zhang & K. Yin, 2003. "Constrained Stochastic Estimation Algorithms for a Class of Hybrid Stock Market Models," Journal of Optimization Theory and Applications, Springer, vol. 118(1), pages 157-182, July.
    • Handle: RePEc:spr:joptap:v:118:y:2003:i:1:d:10.1023_a:1024795626350
    DOI: 10.1023/A:1024795626350
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    References listed on IDEAS

    as
    1. Q. Zhang & G. Yin, 2004. "Nearly-Optimal Asset Allocation in Hybrid Stock Investment Models," Journal of Optimization Theory and Applications, Springer, vol. 121(2), pages 419-444, May.
    2. 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.
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    Cited by:

    1. Yang, Aijun & Liu, Yue & Xiang, Ju & Yang, Hongqiang, 2016. "Optimal buying at the global minimum in a regime switching model," Mathematical Social Sciences, Elsevier, vol. 84(C), pages 50-55.

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