Computational Risk Management

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 5180

Special Issue Editor

Department of Statistics and Actuarial Science, Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada
Interests: computational statistics; data science; copula modeling; quantitative risk management

Special Issue Information

Dear Colleagues,

Solutions to problems from quantitative risk management have recently become increasingly more computational in nature, more complex to design, more challenging to implement, more difficult to test and run, more demanding to deploy and maintain, and harder to integrate in software relevant for business practice. These are all aspects the research area computational risk management is concerned with. This Special Issue of Risks is dedicated to this new research area in the form of short articles highlighting some of its aspects.

Dr. Marius Hofert
Guest Editor

Manuscript Submission Information

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Keywords

  • Quantitative risk management
  • Computational aspects
  • Algorithms
  • Implementation
  • Challenges

Published Papers (2 papers)

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Research

26 pages, 1741 KiB  
Article
Grouped Normal Variance Mixtures
Risks 2020, 8(4), 103; https://doi.org/10.3390/risks8040103 - 07 Oct 2020
Cited by 6 | Viewed by 1755
Abstract
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. This allows one to better model risk factors where components within [...] Read more.
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. 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 λ. All algorithms presented are available in the R package nvmix (version ≥ 0.0.5). Full article
(This article belongs to the Special Issue Computational Risk Management)
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28 pages, 949 KiB  
Article
Implementing the Rearrangement Algorithm: An Example from Computational Risk Management
Risks 2020, 8(2), 47; https://doi.org/10.3390/risks8020047 - 14 May 2020
Cited by 5 | Viewed by 2910
Abstract
After a brief overview of aspects of computational risk management, the implementation of the rearrangement algorithm in R is considered as an example from computational risk management practice. This algorithm is used to compute the largest quantile (worst value-at-risk) of the sum of [...] Read more.
After a brief overview of aspects of computational risk management, the implementation of the rearrangement algorithm in R is considered as an example from computational risk management practice. This algorithm is used to compute the largest quantile (worst value-at-risk) of the sum of the components of a random vector with specified marginal distributions. It is demonstrated how a basic implementation of the rearrangement algorithm can gradually be improved to provide a fast and reliable computational solution to the problem of computing worst value-at-risk. Besides a running example, an example based on real-life data is considered. Bootstrap confidence intervals for the worst value-at-risk as well as a basic worst value-at-risk allocation principle are introduced. The paper concludes with selected lessons learned from this experience. Full article
(This article belongs to the Special Issue Computational Risk Management)
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