scholarly journals The policy iteration method for the optimal stopping of a markov chain with an application

1976 ◽  
pp. 22-36
Author(s):  
K. M. Hee
Keyword(s):  
Markov Chain ◽  
Optimal Stopping ◽  
Iteration Method ◽  
Policy Iteration

Motion planning of a quadrotor robot game using a simulation-based projected policy iteration method

2019 ◽  
Vol 20 (4) ◽  
pp. 525-537
Author(s):  
Li-dong Zhang ◽  
Ban Wang ◽  
Zhi-xiang Liu ◽  
You-min Zhang ◽  
Jian-liang Ai
Keyword(s):  
Motion Planning ◽  
Iteration Method ◽  
Policy Iteration ◽  
Simulation Based ◽  
Quadrotor Robot

Object delineation in noisy images by a modified policy-iteration method

1992 ◽  
Vol 14 (9) ◽  
pp. 952-958 ◽  
Author(s):  
A.C.M. Dumay ◽  
M.N.A.J. Claessens ◽  
C. Roos ◽  
J.J. Gerbrands ◽  
J.H.C. Reiber
Keyword(s):  
Iteration Method ◽  
Policy Iteration ◽  
Noisy Images ◽  
Object Delineation

American Option Valuation under Continuous-Time Markov Chains

2015 ◽  
Vol 47 (2) ◽  
pp. 378-401 ◽  
Author(s):  
B. Eriksson ◽  
M. R. Pistorius
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Optimal Stopping ◽  
Continuous Time ◽  
Markov Models ◽  
American Options ◽  
American Option ◽  
Discrete State ◽  
Continuous Time Markov Chains ◽  
Exponential Lévy Models

This paper is concerned with the solution of the optimal stopping problem associated to the value of American options driven by continuous-time Markov chains. The value-function of an American option in this setting is characterised as the unique solution (in a distributional sense) of a system of variational inequalities. Furthermore, with continuous and smooth fit principles not applicable in this discrete state-space setting, a novel explicit characterisation is provided of the optimal stopping boundary in terms of the generator of the underlying Markov chain. Subsequently, an algorithm is presented for the valuation of American options under Markov chain models. By application to a suitably chosen sequence of Markov chains, the algorithm provides an approximate valuation of an American option under a class of Markov models that includes diffusion models, exponential Lévy models, and stochastic differential equations driven by Lévy processes. Numerical experiments for a range of different models suggest that the approximation algorithm is flexible and accurate. A proof of convergence is also provided.

American Option Valuation under Continuous-Time Markov Chains

2015 ◽  
Vol 47 (02) ◽  
pp. 378-401
Author(s):  
B. Eriksson ◽  
M. R. Pistorius
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Optimal Stopping ◽  
Continuous Time ◽  
Markov Models ◽  
American Options ◽  
American Option ◽  
Discrete State ◽  
Continuous Time Markov Chains ◽  
Exponential Lévy Models

This paper is concerned with the solution of the optimal stopping problem associated to the value of American options driven by continuous-time Markov chains. The value-function of an American option in this setting is characterised as the unique solution (in a distributional sense) of a system of variational inequalities. Furthermore, with continuous and smooth fit principles not applicable in this discrete state-space setting, a novel explicit characterisation is provided of the optimal stopping boundary in terms of the generator of the underlying Markov chain. Subsequently, an algorithm is presented for the valuation of American options under Markov chain models. By application to a suitably chosen sequence of Markov chains, the algorithm provides an approximate valuation of an American option under a class of Markov models that includes diffusion models, exponential Lévy models, and stochastic differential equations driven by Lévy processes. Numerical experiments for a range of different models suggest that the approximation algorithm is flexible and accurate. A proof of convergence is also provided.

On Optimal Stopping of Random Sequences Modulated by Markov Chain

2010 ◽  
Vol 54 (3) ◽  
pp. 534-542 ◽  
Author(s):  
E. L. Presman ◽  
I. M. Sonin
Keyword(s):  
Markov Chain ◽  
Optimal Stopping ◽  
Random Sequences
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