An optimal extraction problem with price impact / Giorgio Ferrari and Torben Koch
VerfasserFerrari, Giorgio ; Koch, Torben
ErschienenBielefeld : Center for Mathematical Economics (IMW), December 2018
Elektronische Ressource
Umfang1 Online-Ressource (36 Seiten) : Diagramme
SerieCenter for Mathematical Economics Working papers ; 603
SchlagwörterUnternehmen / Preisbildung / Rohstoffpreis / Gewinnmaximierung / Hamilton-Jacobi-Differentialgleichung / Dynamische Optimierung
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An optimal extraction problem with price impact [0.61 mb]

A price-maker company extracts an exhaustible commodity from a reservoir, and sells it instantaneously in the spot market. In absence of any actions of the company, the commodity's spot price evolves either as a drifted Brownian motion or as an Ornstein- Uhlenbeck process. While extracting, the company affects the market price of the commodity, and its actions have an impact on the dynamics of the commodity's spot price. The company aims at maximizing the total expected profits from selling the commodity, net of the total expected proportional costs of extraction. We model this problem as a two-dimensional degenerate singular stochastic control problem with finite fuel. To determine its solution, we construct an explicit solution to the associated Hamilton-Jacobi-Bellman equation, and then verify its actual optimality through a verification theorem. On the one hand, when the (uncontrolled) price is a drifted Brownian motion, it is optimal to extract whenever the current price level is larger or equal than an endogenously determined constant threshold. On the other hand, when the (uncontrolled) price evolves as an Ornstein-Uhlenbeck process, we show that the optimal extraction rule is triggered by a curve depending on the current level of the reservoir. Such a curve is a strictly decreasing C1-function for which we are able to provide an explicit expression. Finally, our study is complemented by a theoretical and numerical analysis of the dependency of the optimal extraction strategy and value function on the model's parameters.

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