Valuing Capacity Investment Decisions: Binomial vs. Markov Models

In this work, we present a model to value capacity investment decisions based on real options. In the problem considered we incorporate partial reversibility by letting the firm reverse its capital investment at a cost, both fully or partially. The standard RO approach considers the stochastic variable to be normally distributed and then approximated by a binomial distribution, resulting in a binomial lattice. In this work, we investigate the use of a sparse Markov chain, which is derived from demand data previously collected. The main advantages of this approach are: i) the Markov chain does not assume any type of distribution for the stochastic variable, ii) the probability of a variation is not constant, actually it depends on the current value, and iii) it generalizes current literature using binomial distributions since this type of distribution can be modelled by a Markov chain.