Multi-Period Asset Allocation: An Application of Discrete Stochastic Programming
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The issue of modeling farm financial decisions in a dynamic framework is addressed in
this paper. Discrete stochastic programming is used to model the farm portfolio over the
planning period. One of the main issues of discrete stochastic programming is representing the
uncertainty of the data. The development of financial scenario generation routines provides a
method to model the stochastic nature of the model. In this paper, two approaches are presented
for generating scenarios for a farm portfolio problem. The approaches are based on copulas and
optimization. The copula method provides an alternative to the multivariate normal assumption.
The optimization method generates a number of discrete outcomes which satisfy specified
statistical properties by solving a non-linear optimization model. The application of these
different scenario generation methods is then applied to the topic of geographical diversification.
The scenarios model the stochastic nature of crop returns and land prices in three separate
geographic regions. The results indicate that the optimal diversification strategy is sensitive to
both scenario generation method and initial acreage assumptions. The optimal diversification
results are presented using both scenario generation methods.