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A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework … control problems as analytical solutions are not tractable in general. This paper generalizes the LSMC algorithm proposed in … Shen and Weng (2017) to solve a wide class of stochastic optimal control models. Our algorithm has three pillars: a …
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-dimensional discrete-time stochastic control problems under general constraints. The method employs quasi-random grids and radial basis … Finance applications: a) dynamic portfolio choice with labor income and financial constraints, a continuous control problem; b …) dynamic portfolio choice with capital gain taxation, a high-dimensional singular control problem …
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In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...
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This paper is concerned with near-optimal control of manufacturing systems consisting of two unreliable machines in …
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Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...
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