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
“…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%
“…. , U rc ) have distributions which are convolutions of α− symmetric distributions (see Fang et al 1990, and the review in Ibragimov 2005, 2009b, and Ibragimov and Walden 2007.…”
Section: Extensions: Multiple Additive and Multiplicative Common Shocksmentioning
confidence: 99%
“…3). Recently, Ibragimov and Walden (2007) showed that, with a value at risk approach with bounded risks concentrated on a sufficiently large interval, diversification may be suboptimal up to a certain number of risks and then become optimal. Ibragimov et al (2009) demonstrate how this analysis can be used to explain low levels of reinsurance among insurance providers in markets for catastrophe reinsurance.…”
Section: Introductionmentioning
confidence: 99%
“…(see Ibragimov 2005, 2009band Ibragimov and Walden 2007, models (1.2) and (1.3) provide a natural framework for modeling risks subject to (additive) common shocks R i and C j (such as political or macroeconomic ones, see the discussion in Andrews 2003Andrews , 2005. The common shocks R i affect all risks Y i j , j = 1, .…”
Portfolio analysis, Value at risk, Power laws, Heavy-tailedness, Diversification, Dependence, Common shocks, Factor models, Riskiness, Majorization, Random effects, Linear estimators, Efficiency, G11, C13,
“…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%
“…. , U rc ) have distributions which are convolutions of α− symmetric distributions (see Fang et al 1990, and the review in Ibragimov 2005, 2009b, and Ibragimov and Walden 2007.…”
Section: Extensions: Multiple Additive and Multiplicative Common Shocksmentioning
confidence: 99%
“…3). Recently, Ibragimov and Walden (2007) showed that, with a value at risk approach with bounded risks concentrated on a sufficiently large interval, diversification may be suboptimal up to a certain number of risks and then become optimal. Ibragimov et al (2009) demonstrate how this analysis can be used to explain low levels of reinsurance among insurance providers in markets for catastrophe reinsurance.…”
Section: Introductionmentioning
confidence: 99%
“…(see Ibragimov 2005, 2009band Ibragimov and Walden 2007, models (1.2) and (1.3) provide a natural framework for modeling risks subject to (additive) common shocks R i and C j (such as political or macroeconomic ones, see the discussion in Andrews 2003Andrews , 2005. The common shocks R i affect all risks Y i j , j = 1, .…”
Portfolio analysis, Value at risk, Power laws, Heavy-tailedness, Diversification, Dependence, Common shocks, Factor models, Riskiness, Majorization, Random effects, Linear estimators, Efficiency, G11, C13,
Choosing a proper risk measure is an important regulatory issue, as exemplified in governmental regulations such as Basel II accord [1] and its recent revision [2], which use Value-at-Risk (VaR) with scenario analysis as the risk measure for setting capital requirement for market risk. The main motivation of this article is to investigate whether VaR with scenario analysis are good risk measures for external regulation. By using the notion of comonotonic random variables studied in decision theory literatures such as Refs 3-8, we shall propose a new class of risk measures satisfying a new set of axioms. The new class of risk measures includes VaR with scenario analysis, in particular the current and recently revised Basel II risk measures [1,2] as special cases, and therefore provides a theoretical basis for using VaR with scenario analysis as robust risk measures for the purpose of external, regulatory risk measurement.Broadly speaking, a risk measure attempts to assign a single numerical value to a random financial loss. Obviously, it can be problematic to use one number to summarize the whole statistical distribution of the financial loss. Therefore, one shall avoid doing this if it is at all possible. However, in many cases there is no other choice.
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