Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
A rejection technique for sampling from log-concave multivariate distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Random variate generation by numerical inversion when only the density is known
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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It has been observed by E. Eberlein and U. Keller that the hyperbolic distribution fits logarithmic rates of returns of a stock much better than the normal distribution. We give a method for sampling from the hyperbolic distribution by the inversion method, which is suited for simulation using low discrepancy point sets.Instead of directly inverting the cumulative distribution function (CDF) we provide an approximation of the inverse function which is simple to obtain by standard numerical methods and which is fast to compute.