Monte Carlo Approximate Tensor Moment Simulations
An algorithm to generate samples with approximate first-, second-, and third-order moments is presented extending the Cholesky matrix decomposition to a Cholesky tensor decomposition of an arbitrary order. The tensor decomposition of the first-, second-, and third-order objective moments generates a non-linear system of equations. The algorithm solves these equations by numerical methods. The results show that the optimisation algorithm delivers samples with an approximate error of 0.1%--4% between the components of the objective and the sample moments. An application for sensitivity analysis of portfolio risk assessment with Value-at-Risk VaR) is provided. A comparison with previous methods available in the literature suggests that methodology proposed reduces the error of the objective moments in the generated samples
Published on | 29th September 2014 |
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Authors | Juan C. Arismendi, ICMA Centre, University of Reading and University of Brasilia and Herbert Kimura, University of Brasilia |
Series Reference | 2014-8 |
External link | http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2491639 |