939 research outputs found
Incorporating statistical model error into the calculation of acceptability prices of contingent claims
The determination of acceptability prices of contingent claims requires the
choice of a stochastic model for the underlying asset price dynamics. Given
this model, optimal bid and ask prices can be found by stochastic optimization.
However, the model for the underlying asset price process is typically based on
data and found by a statistical estimation procedure. We define a confidence
set of possible estimated models by a nonparametric neighborhood of a baseline
model. This neighborhood serves as ambiguity set for a multi-stage stochastic
optimization problem under model uncertainty. We obtain distributionally robust
solutions of the acceptability pricing problem and derive the dual problem
formulation. Moreover, we prove a general large deviations result for the
nested distance, which allows to relate the bid and ask prices under model
ambiguity to the quality of the observed data.Comment: 27 pages, 2 figure
Numerical Methods for Convex Multistage Stochastic Optimization
Optimization problems involving sequential decisions in a stochastic
environment were studied in Stochastic Programming (SP), Stochastic Optimal
Control (SOC) and Markov Decision Processes (MDP). In this paper we mainly
concentrate on SP and SOC modelling approaches. In these frameworks there are
natural situations when the considered problems are convex. Classical approach
to sequential optimization is based on dynamic programming. It has the problem
of the so-called ``Curse of Dimensionality", in that its computational
complexity increases exponentially with increase of dimension of state
variables. Recent progress in solving convex multistage stochastic problems is
based on cutting planes approximations of the cost-to-go (value) functions of
dynamic programming equations. Cutting planes type algorithms in dynamical
settings is one of the main topics of this paper. We also discuss Stochastic
Approximation type methods applied to multistage stochastic optimization
problems. From the computational complexity point of view, these two types of
methods seem to be complimentary to each other. Cutting plane type methods can
handle multistage problems with a large number of stages, but a relatively
smaller number of state (decision) variables. On the other hand, stochastic
approximation type methods can only deal with a small number of stages, but a
large number of decision variables
Guaranteed Bounds for General Nondiscrete Multistage Risk-Averse Stochastic Optimization Programs
In general, multistage stochastic optimization problems are formulated on the basis of continuous distributions describing the uncertainty. Such āinfiniteā problems are practically impossible to solve as they are formulated, and finite tree approximations of the underlying stochastic processes are used as proxies. In this paper, we demonstrate how one can find guaranteed bounds, i.e., finite tree models, for which the optimal values give upper and lower bounds for the optimal value of the original infinite problem. Typically, there is a gap between the two bounds. However, this gap can be made arbitrarily small by making the approximating trees bushier. We consider approximations in the first-order stochastic sense, in the convex-order sense, and based on subgradient approximations. Their use is shown in a multistage risk-averse production problem
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