A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment

Abstract

This work proposes an exercise-dependent real options model for the valuation and optimal harvest timing of a forestry investment in eucalyptus. Investment in eucalyptus is complex, as trees allow for two cuts without re-plantation, and have a specific time and growth window in which they are suitable for industrial processing into paper pulp. Thus, path dependency in the cutting options is observed, since the moment of exercise of the first option determines the time interval in which the second option may be exercised. Therefore, the value of the second option depends on the history of the state variables rather than on its final value. In addition, the options to abandon the project, or convert land to another use, are also considered. The option value is estimated by solving a stochastic dynamic programming model. Results are reported for a case study in the Portuguese eucalyptus forest which show that price uncertainty postpones the optimal cutting decisions. Moreover, optimal harvesting policies deviate from present practice of forest managers and allow for considerable gains.