Monte Carlo Approximate Tensor Moment Simulations

Juan Arismendi Zambrano () and Herbert Kimura

ICMA Centre Discussion Papers in Finance from Henley Business School, University of Reading

Abstract: 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

Keywords: Monte Carlo Simulation; Higher-order Moments; Exact Moments Simulation; Stress-testing (search for similar items in EconPapers)
JEL-codes: C14 C15 G32 (search for similar items in EconPapers)
Date: 2014-08
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ore and nep-rmg
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:rdg:icmadp:icma-dp2014-08

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