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Random orthogonal matrix simulation
journal contribution
posted on 2023-06-08, 12:23 authored by Walter Ledermann, Carol AlexanderCarol Alexander, Daniel LedermannThis paper introduces a method for simulating multivariate samples that have exact means, covariances, skewness and kurtosis. We introduce a new class of rectangular orthogonal matrix which is fundamental to the methodology and we call these matrices L matrices. They may be deterministic, parametric or data specific in nature. The target moments determine the L matrix then infinitely many random samples with the same exact moments may be generated by multiplying the L matrix by arbitrary random orthogonal matrices. This methodology is thus termed “ROM simulation”. Considering certain elementary types of random orthogonal matrices we demonstrate that they generate samples with different characteristics. ROM simulation has applications to many problems that are resolved using standard Monte Carlo methods. But no parametric assumptions are required (unless parametric L matrices are used) so there is no sampling error caused by the discrete approximation of a continuous distribution, which is a major source of error in standard Monte Carlo simulations. For illustration, we apply ROM simulation to determine the value-at-risk of a stock portfolio.
History
Publication status
- Published
Journal
Linear Algebra and its ApplicationsISSN
0024-3795Publisher
ElsevierExternal DOI
Issue
6Volume
434Page range
1444-1467Department affiliated with
- Business and Management Publications
Notes
Simulation; Orthogonal matrix; Random matrix; Ledermann matrix; L matrices; Multivariate moments; Volatility clustering; Value-at-riskFull text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-09-11Usage metrics
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