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Portfolio optimization with a copula-based extension of conditional value-at-risk

✍ Scribed by Krzemienowski, Adam; Szymczyk, Sylwia


Book ID
125355390
Publisher
Springer US
Year
2014
Tongue
English
Weight
299 KB
Volume
237
Category
Article
ISSN
0254-5330

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## ABSTRACT In this paper, we investigate the performance of a class of M‐estimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of value‐at‐risk. The class of estimators includes the least absolute deviation (LAD), Huber's, Cauchy and B‐estimator, as well