In this paper we give conditions for the existence of bias optimal policies in a class of continuous-time controlled Markov chains with unbounded reward and transition rates. Several characterizations of bias optimality are proposed. We also introduce new sets of conditions ensuring uniform exponential ergodicity of continuous-time controlled Markov chains.
Given a Markov process, we are interested in the numerical computation of the moments of the exit time from a bounded domain. We use a moment approach which, together with appropriate semidefinite positivity moment conditions, yields a sequence of semidefinite programs (or SDP relaxations), depending on the number of moments considered, that provide a sequence of nonincreasing (resp. nondecreasing) upper (resp. lower) bounds. The results are compared to the linear Hausdorff moment conditions approach considered for the LP relaxations in Helmes et al. [Helmes, K., Rö hl, S., Stockbridge, R.H. Computing moments of the exit time distribution for Markov processes by linear programming. Oper. Res. 2001, 49, 516-530]. The SDP relaxations are shown to be more general and more precise than the LP relaxations.
We present a new methodology for the numerical pricing of a class of exotic derivatives such as Asian or barrier options when the underlying asset price dynamics are modeled by a geometric Brownian motion or a number of mean-reverting processes of interest. This methodology identifies derivative prices with infinite-dimensional linear programming problems involving the moments of appropriate measures, and then develops suitable finite-dimensional relaxations that take the form of semidefinite programs (SDP) indexed by the number of moments involved. By maximizing or minimizing appropriate criteria, monotone sequences of both upper and lower bounds are obtained. Numerical investigation shows that very good results are obtained with only a small number of moments. Theoretical convergence results are also established.
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