2015
DOI: 10.1080/03461238.2015.1119717
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Reduction of Value-at-Risk bounds via independence and variance information

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Cited by 18 publications
(7 citation statements)
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“…This paper continues the streamline of easily computable and practical bounds on VaR which has been initiated in [6] and further extended in [26]. Based on a novel extension of classical Hoe ding-Fréchet bounds, we provide an upper VaR bound for a joint risk portfolio with xed marginal distributions and positive dependence information assumed on a subset of the domain of its distribution function.…”
Section: Motivation and Preliminariesmentioning
confidence: 84%
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“…This paper continues the streamline of easily computable and practical bounds on VaR which has been initiated in [6] and further extended in [26]. Based on a novel extension of classical Hoe ding-Fréchet bounds, we provide an upper VaR bound for a joint risk portfolio with xed marginal distributions and positive dependence information assumed on a subset of the domain of its distribution function.…”
Section: Motivation and Preliminariesmentioning
confidence: 84%
“…E ects of this dependence information on the reduction of the VaR bounds are described in [6] and in [2]. Some higher order marginal information has been investigated in [13], [23], [14], and in [26]. The reduction of VaR bounds by inclusion of additional second or higher order moment information was described in [3] and in [4].…”
Section: Motivation and Preliminariesmentioning
confidence: 99%
“…Puccetti, Rüschendorf, and Manko (2016) considered the value-at-risk upper bounds in the case where positive dependence information is assumed in the tails or some central part of the distribution function. In Puccetti et al (2017), independence among (some) subgroups of the marginal components is assumed, a fact that leads to a considerable improvement in the value-at-risk bounds as compared with the case where only the marginals are known (see Theorems 4 and 5).…”
Section: Boundsmentioning
confidence: 99%
“…Bertsimas, Lauprete, and Samarov (2004) derived value-at-risk bounds when only the mean and a maximum variance of the portfolio loss can be trusted (see Theorem 7). Moreover, Puccetti et al (2017) derived bounds when information on the maximum variance of the portfolio loss is assumed in addition to the knowledge of the marginals and the independence among some subgroups of the marginals (see Theorem 8).…”
Section: Boundsmentioning
confidence: 99%
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