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• W2799352017 abstract "The sparse multivariate method of simulated quantiles (S‐MMSQ) is applied to solve a portfolio optimization problem under value‐at‐risk constraints where the joint returns follow a multivariate skew‐elliptical stable distribution. The S‐MMSQ is a simulation‐based method that is particularly useful for making parametric inference in some pathological situations where the maximum likelihood estimator is difficult to compute. The method estimates parameters by minimizing the distance between quantile‐based statistics evaluated on true and synthetic data, simulated from the postulated model, penalized by adding the smoothly clipped absolute deviation ℓ 1 ‐penalty in order to achieve sparsity. The S‐MMSQ aims to efficiently handle the problem of estimating large‐dimensional distributions with intractable likelihood, such as the stable distributions that have been widely applied in finance to model financial returns." @default.
• W2799352017 created "2018-05-17" @default.
• W2799352017 creator A5000455158 @default.
• W2799352017 creator A5009148451 @default.
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• W2799352017 date "2018-04-15" @default.
• W2799352017 modified "2023-09-25" @default.
• W2799352017 title "The sparse method of simulated quantiles: An application to portfolio optimization" @default.
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• W2799352017 doi "https://doi.org/10.1111/stan.12141" @default.
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