Modelling Investment Opportunities

I will need to generate random opportunities for the entrepreneurs in my economy.  These opportunities will have to be funded, probably by involving other entrepreneurs, and so I need to specify an opportunity by its distributional characteristics and its initial cost.

Typically one would choose a log-normal distribution for the outcome of an investment opportunity, but I don’t like the implied scale invariance of log-normality.  I prefer to use the Gamma Distribution, which is sometimes used to model aggregate claims in insurance,  with the density given by

p(x; k, \theta) = \frac{1}{\Gamma(k)\theta^k} x^{k-1}e^{-\frac{x}{\theta}}

where k>0 is the shape and \theta>0 is the scale.  The expected value of a Gamma distributed random variable is k \theta while its variance is k \theta^2.

I need to have a consistent method of defining the initial cost of the opportunity and will base it on the CDF of the distribution, specifically I will define a parameter q\in(0,1) that will be used to specify the profitability of the economy and defines the cost, l, of an opportunity by

q = \int_0^l p(x; k, \theta)\; dx.

The attached plot shows the value of l for k \in (0.2,10.0) and \theta \in (0.2,3.0) with q=0.1.




One thought on “Modelling Investment Opportunities

  1. Pingback: Fair Interest Rates | REFS Project

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