2006
DOI: 10.2139/ssrn.880081
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The Limits of Diversification when Losses May be Large

Abstract: Recent results in value at risk analysis show that, for extremely heavy-tailed risks with unbounded distribution support, diversification may increase value at risk, and that generally it is difficult to construct an appropriate risk measure for such distributions. We further analyze the limitations of diversification for heavy-tailed risks. We provide additional insight in two ways. First, we show that similar nondiversification results are valid for a large class of risks with bounded support, as long as the… Show more

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Cited by 51 publications
(86 citation statements)
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References 69 publications
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“…For instance, using in the proof the extensions of the results in Propositions 3.1 and 3.2 to the case of dependence discussed in Ibragimov (2005Ibragimov ( , 2009b and Ibragimov and Walden (2007) one obtains that all the results in the paper also hold in settings where the risks R i , C j and U i j in (1.2) and (1.3) are dependent among themselves or are bounded. These generalizations include models (1.2) and (1.3) in which the vectors of common shocks (R 1 , .…”
Section: Extensions: Multiple Additive and Multiplicative Common Shocksmentioning
confidence: 84%
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“…For instance, using in the proof the extensions of the results in Propositions 3.1 and 3.2 to the case of dependence discussed in Ibragimov (2005Ibragimov ( , 2009b and Ibragimov and Walden (2007) one obtains that all the results in the paper also hold in settings where the risks R i , C j and U i j in (1.2) and (1.3) are dependent among themselves or are bounded. These generalizations include models (1.2) and (1.3) in which the vectors of common shocks (R 1 , .…”
Section: Extensions: Multiple Additive and Multiplicative Common Shocksmentioning
confidence: 84%
“…As discussed in Ibragimov (2005Ibragimov ( , 2009b, and Ibragimov and Walden (2007), convolutions of α−symmetric distributions exhibit both heavy-tailedness in marginals and dependence among them. For instance, convolutions of models (7.4) with α < 1 have extremely heavy-tailed marginal distributions with infinite means.…”
Section: Extensions: Multiple Additive and Multiplicative Common Shocksmentioning
confidence: 91%
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