Name
Title | ||
|---|---|---|
Generating daily changes in market variables using a multivariate mixture of normal distributions |
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/WSC.2001.977286 | Winter Simulation Conference |
Keywords | Field | DocType |
normal distribution,market variables,fatter-than-normal tails,fatter-than-normal tail,efficient analytical monte carlo,covariance matrices,multivariate mixture of normal distributions,daily change generation,main purpose,generating daily change,daily change,market variable,arbitrary covariance matrix,monte carlo methods,multivariate normal,input covariance matrix,linear combination,skewness,analytical monte carlo method,multivariate mixture,modeling,modelling,covariance matrix,nonlinear equations,monte carlo method,mathematics,analysis of variance,finance,gaussian distribution,computer science,probability distribution | Elliptical distribution,Applied mathematics,Matrix normal distribution,Simulation,Multivariate normal distribution,Statistics,Wishart distribution,Complex normal distribution,Normal-Wishart distribution,Mathematics,Scatter matrix,Matrix t-distribution | Conference |
Volume | ISBN | Citations |
1 | 0-7803-7307-3 | 2 |
PageRank | References | Authors |
0.56 | 1 | 1 |