/* Describe your code here */ // Sample two field sizes from a distribution a = truncate(1 to 1000, .1, 10000) // Lognormal, with extreme values cut off b = truncate(1 to 1000, .1, 10000) // Ratio between size of two randomly chosen fields ratio = max(a/b, b/a)
/* Describe your code here */ rho1 = uniform(0,1) rho2 = normal(0.5, 0.25) rho3 = 2*beta(1,3) rho = mx(rho1,rho2,rho3,[1,1,1]) // unweighted mixture of the above
/* Exploring Isoelastic Utility Functions (IUF) under semi-efficient market conditions */ // What if we are uncertain about Rho and model it as lognormally distributed? // This could be justifiable if we think that 100% efficient markets are impossible to achieve, and log(rho) is normally distributed rho_lognormal = 0.1 to 1 // vmf is value of marginal funding. It is the gradient of the IUF, x^-rho.
/* Exploring Isoelastic Utility Functions (IUF) under semi-efficient market conditions */ // CURRENTLY THIS IS MOSTLY A COPY OF THE EFFICIENT MARKET MODEL // What if we are uncertain about Rho and model it as lognormally distributed? // This could be justifiable if we think that 100% efficient markets are impossible to achieve, and log(rho) is normally distributed rho_lognormal = 1 - (0.1 to 1)
/* Describe your code here */ // For Isoelastic Utility Functions (IUFs), 'compounding' fields have increasing returns, so rho < 0. // Diminishing returns means rho > 1 // We want to know whether modelling rho as a distribution is comparable (in terms of central values) to // using a discounted point-estimate rho instead.