Reports on Risk Management Findings from University of Waterloo Provide New Insights (Grouped Normal Variance Mixtures)

2020 OCT 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Researchers detail new data in . According to news reporting from Waterloo, Canada, by NewsRx journalists, research stated, “Grouped normal variance mixtures are a class of multivariate distributions that generalize classical normal variance mixtures such as the multivariate * * t* * distribution, by allowing different groups to have different (comonotone) mixing distributions.”

The news correspondents obtained a quote from the research from University of Waterloo: “This allows one to better model risk factors where components within a group are of similar type, but where different groups have components of quite different type. This paper provides an encompassing body of algorithms to address the computational challenges when working with this class of distributions. In particular, the distribution function and copula are estimated efficiently using randomized quasi-Monte Carlo (RQMC) algorithms. We propose to estimate the log-density function, which is in general not available in closed form, using an adaptive RQMC scheme. This, in turn, gives rise to a likelihood-based fitting procedure to jointly estimate the parameters of a grouped normal mixture copula jointly. We also provide mathematical expressions and methods to compute Kendall’s tau, Spearman’s rho and the tail dependence coefficient l.”

According to the news reporters, the research concluded: “All algorithms presented are available in the R package nvmix (version 0.0.5).”

For more information on this research see: Grouped Normal Variance Mixtures. Risks, 2020,8(103):103. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks8040103.

Our news editors report that more information may be obtained by contacting Erik Hintz, Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. Additional authors for this research include Marius Hofert, Christiane Lemieux.

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