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Risks 2018, 6(3), 64; https://doi.org/10.3390/risks6030064

Sampling the Multivariate Standard Normal Distribution under a Weighted Sum Constraint

Louvain Finance Center & CORE, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Received: 30 May 2018 / Revised: 21 June 2018 / Accepted: 22 June 2018 / Published: 25 June 2018
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Abstract

Statistical modeling techniques—and factor models in particular—are extensively used in practice, especially in the insurance and finance industry, where many risks have to be accounted for. In risk management applications, it might be important to analyze the situation when fixing the value of a weighted sum of factors, for example to a given quantile. In this work, we derive the (n1)-dimensional distribution corresponding to a n-dimensional i.i.d. standard Normal vector Z=(Z1,Z2,,Zn) subject to the weighted sum constraint wZ=c, where w=(w1,w2,,wn) and wi0. This law is proven to be a Normal distribution, whose mean vector μ and covariance matrix Σ are explicitly derived as a function of (w,c). The derivation of the density relies on the analytical inversion of a very specific positive definite matrix. We show that it does not correspond to naive sampling techniques one could think of. This result is then used to design algorithms for sampling Z under constraint that wZ=c or wZc and is illustrated on two applications dealing with Value-at-Risk and Expected Shortfall. View Full-Text
Keywords: conditional distribution; conditional sampling; multi-factor models conditional distribution; conditional sampling; multi-factor models
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Vrins, F. Sampling the Multivariate Standard Normal Distribution under a Weighted Sum Constraint. Risks 2018, 6, 64.

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