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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jin Wang Chunlei Liu |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Math. & Comput. Sci., Valdosta State Univ. (Jin Wang; Chunlei Liu) |
| Abstract | Mixture of normals is a more general and flexible distribution for modeling of daily changes in market variables with fat tails and skewness. An efficient analytical Monte Carlo method was proposed by Wang and Taaffe for generating daily changes using a multivariate mixture of normal distributions with arbitrary covariance matrix. However the usual Cholesky decomposition will fail if the covariance matrix is not positive definite. In practice, the covariance matrix is unknown and has to be estimated. The estimated covariance may be not positive definite. We propose a modified Cholesky decomposition for semi-definite matrices and also suggest an optimal semi-definite approximation for indefinite matrices |
| Starting Page | 342 |
| Ending Page | 347 |
| File Size | 120607 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424405009 |
| DOI | 10.1109/WSC.2006.323100 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gaussian distribution Covariance matrix Matrix decomposition Symmetric matrices Computational modeling Mathematics Computer science Probability distribution Tail Fitting |
| Content Type | Text |
| Resource Type | Article |
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