Multivariate statistical simulation
Multivariate statistical simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Generating multivariate mixture of normal distributions using a modified Cholesky decomposition
Proceedings of the 38th conference on Winter simulation
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The mixture of normal distributions provides a useful extension of the normal distribution for modeling of daily changes in market variables with fatter-than-normal tails and skewness. An efficient analytical Monte Carlo method is proposed for generating daily changes using a multivariate mixture of normal distributions with arbitrary covariance matrix. The main purpose of this method is to transform (linearly) a multivariate normal with an input covariance matrix into the desired multivariate mixture of normal distributions. This input covariance matrix can be derived analytically. Any linear combination of mixtures of normal distributions can be shown to be a mixture of normal distributions.