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

G. Yin, Q. Zhang and K. Yin
Additional contact information
G. Yin: Wayne State University
Q. Zhang: University of Georgia
K. Yin: University of Minnesota

Journal of Optimization Theory and Applications, 2003, vol. 118, issue 1, No 9, 157-182

Abstract: 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.

Keywords: Stochastic optimization; constraints; hybrid models; Markov chains; geometric Brownian motions (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1023/A:1024795626350

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