874 research outputs found
Optimal Dynamic Procurement Policies for a Storable Commodity with L\'evy Prices and Convex Holding Costs
In this paper we study a continuous time stochastic inventory model for a
commodity traded in the spot market and whose supply purchase is affected by
price and demand uncertainty. A firm aims at meeting a random demand of the
commodity at a random time by maximizing total expected profits. We model the
firm's optimal procurement problem as a singular stochastic control problem in
which controls are nondecreasing processes and represent the cumulative
investment made by the firm in the spot market (a so-called stochastic
"monotone follower problem"). We assume a general exponential L\'evy process
for the commodity's spot price, rather than the commonly used geometric
Brownian motion, and general convex holding costs.
We obtain necessary and sufficient first order conditions for optimality and
we provide the optimal procurement policy in terms of a "base inventory"
process; that is, a minimal time-dependent desirable inventory level that the
firm's manager must reach at any time. In particular, in the case of linear
holding costs and exponentially distributed demand, we are also able to obtain
the explicit analytic form of the optimal policy and a probabilistic
representation of the optimal revenue. The paper is completed by some computer
drawings of the optimal inventory when spot prices are given by a geometric
Brownian motion and by an exponential jump-diffusion process. In the first case
we also make a numerical comparison between the value function and the revenue
associated to the classical static "newsvendor" strategy.Comment: 28 pages, 3 figures; improved presentation, added new results and
section
Optimal Harvesting under Resource Stock and Price Uncertainty
We analyze optimal harvesting policy under stochastic price and stock dynamics. We state a set of weak conditions under which the optimal policy can be characterized by a single exercise threshold and show that the value of optimal harvesting and depletion policies can be expressed as the separable form according to which only the current price and the expected per capita growth rate affect the threshold, while under risk neutrality volatility of price dynamics will have no effect. Uncertainty makes waiting valuable and the optimal threshold is higher when harvesting can be exercised only once than in the sequential case.optimal harvesting, stochastic price and stock dynamics, single and sequential harvesting opportunity
On Poisson constrained control of linear diffusions
The classical setting in optimal stopping and optimal control theory assumes that the agent controlling the system can operate continuously in time. In optimal stopping this setting is highly stylized for many applications, for example, in mathematical finance due to illiquid markets. In optimal stochastic control this setting often leads to optimal strategies being singular with respect to the Lebesgue measure, and thus the strategies are not feasible in practice. Hence, it is of importance to study these problems from such a perspective that their solutions are practically more implementable.
In this thesis we alter the classical setting by introducing an exogenous constraint, in the form of a signal process, for the control opportunities of the agent. In order to keep the problems more tractable, especially time-homogeneous and Markovian, the signal process is assumed to be a Poisson process with constant intensity. Consequently, the agent can only have influence on the system at discrete times. We call these control problems Poisson constrained control problems and study them when the dynamics are governed by linear diffusion processes.
Linear diffusions are particular enough to have a rich theory but still general enough to offer a class of interesting dynamics that are applicable in various situations. A key factor is also that many control problems with diffusions will lead to closed-form solutions. This thesis investigates to which extent the classical theory of diffusion can be applied in this class of control problems to form closed-form solutions
A General Approach to the Stochastic Rotation Problem with Amenity Valuation
This paper presents a new approach to study the optimal rotation policy with amenity valuation under uncertainty. We first postulate the stochastic forest value and assume plausibly that monetary value of amenities is a continuous and non-negative function of forest value thus presenting the trade-off between timber revenues and amenity values. Second, instead of using a dynamic programming approach, we derive a recursive representation of the total forest value and solve the optimal rotation threshold by applying ordinary non-linear programming techniques. Third, we characterize under certain set of conditions how the properties of both the expected cumulative value and the expected marginal cumulative value, accrued from amenity services, depend on the precise nature of the monetary valuation of amenities and what is the impact of volatility on these concepts. Finally, we illustrate our results explicitly in models based on logistic growth by focusing on the role of amenity valuation and volatility of forest value in the determination of Wicksellian and Faustmannian thresholds. Our theoretical and numerical findings emphasize the crucial importance of the nature of amenity valuation for the impact of higher volatility of forest value on the rotation thresholds.amenity valuation, optimal Faustmannian and Wicksellian rotation policy, stochatic impulse control
Non-intrusive and structure preserving multiscale integration of stiff ODEs, SDEs and Hamiltonian systems with hidden slow dynamics via flow averaging
We introduce a new class of integrators for stiff ODEs as well as SDEs. These
integrators are (i) {\it Multiscale}: they are based on flow averaging and so
do not fully resolve the fast variables and have a computational cost
determined by slow variables (ii) {\it Versatile}: the method is based on
averaging the flows of the given dynamical system (which may have hidden slow
and fast processes) instead of averaging the instantaneous drift of assumed
separated slow and fast processes. This bypasses the need for identifying
explicitly (or numerically) the slow or fast variables (iii) {\it
Nonintrusive}: A pre-existing numerical scheme resolving the microscopic time
scale can be used as a black box and easily turned into one of the integrators
in this paper by turning the large coefficients on over a microscopic timescale
and off during a mesoscopic timescale (iv) {\it Convergent over two scales}:
strongly over slow processes and in the sense of measures over fast ones. We
introduce the related notion of two-scale flow convergence and analyze the
convergence of these integrators under the induced topology (v) {\it Structure
preserving}: for stiff Hamiltonian systems (possibly on manifolds), they can be
made to be symplectic, time-reversible, and symmetry preserving (symmetries are
group actions that leave the system invariant) in all variables. They are
explicit and applicable to arbitrary stiff potentials (that need not be
quadratic). Their application to the Fermi-Pasta-Ulam problems shows accuracy
and stability over four orders of magnitude of time scales. For stiff Langevin
equations, they are symmetry preserving, time-reversible and Boltzmann-Gibbs
reversible, quasi-symplectic on all variables and conformally symplectic with
isotropic friction.Comment: 69 pages, 21 figure
Controlled diffusion processes
This article gives an overview of the developments in controlled diffusion
processes, emphasizing key results regarding existence of optimal controls and
their characterization via dynamic programming for a variety of cost criteria
and structural assumptions. Stochastic maximum principle and control under
partial observations (equivalently, control of nonlinear filters) are also
discussed. Several other related topics are briefly sketched.Comment: Published at http://dx.doi.org/10.1214/154957805100000131 in the
Probability Surveys (http://www.i-journals.org/ps/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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