Identifying tails, bounds and end-points of random variables

Caers, Jef; Maes, Marc A.

doi:10.1016/s0167-4730(97)00036-2

Cited by 37 publications

(25 citation statements)

References 14 publications

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“…In such cases, the standard non-parametric bootstrap method is not considered suitable since the non-parametric distribution model cannot provide a good approximation of the tail distribution. One effective way to overcome this problem has been proposed by Caers and Maes [36] in which the empirical tail is replaced by a parametric tail estimated from fitting a Generalized Pareto Distribution (GPD) to the extreme values over a given threshold. The tail model, however, is sensitive to the selected threshold and requires iterative optimization.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…The tail model, however, is sensitive to the selected threshold and requires iterative optimization. For this purpose, the procedure proposed by Caers and Maes [36] is followed in which the error in extreme quantile estimation is minimized. The formulation of the semi-parametric smoothed bootstrap method is briefly presented in Appendix B.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…In order to obtain the optimum threshold 2 p the methodology recommended by Caers and Maes [36] is applied.…”

Section: Discussionmentioning

confidence: 99%

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Probability distributions of wave run-up on a TLP model

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Section: Data Analysis and Resultsmentioning

confidence: 99%

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Probability distributions of wave run-up on a TLP model

Izadparast

Niedzwecki

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Marine Structures

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“…We may view this as a result of scarcity of data in tails compared with that around the median. Extracting information from tails requires, on average, slightly larger windows in comparison to the region near the median (Caers and Maes, 1998).…”

Section: Optimal Bandwidthmentioning

confidence: 99%

Exploring Nonlinearities in Financial Systemic Risk

Wolski

2013

SSRN Journal

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We propose a new methodology of assessing the effects of individual institution's risk on the others and on the system as a whole. We build upon the Conditional Value-at-Risk approach, however, we introduce the explicit Granger causal linkages and we account for possible nonlinearities in the financial time series. Conditional Value-at-Risk-Nonlinear Granger Causality, or CoVaR-NGraCo as we call it, has regular asymptotic properties which makes it particularly appealing for practical applications. We test our approach empirically and assess the contribution of the euro area financial companies to the overall systemic risk. We find that only a few financial institutions pose a serious ex ante threat to the systemic risk, whereas, given that the system is already in trouble, there are more institutions which hamper its recovery. Moreover, we discover non-negligible nonlinear structures in the systemic risk profile of the euro zone.JEL Codes: C1, G01, G20, G32

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“…Extreme value analysis often involves extrapolation to values beyond the largest or smallest observed value in order to assign probabilities to extreme events. Expert judgments (Slijkhuis et al 1999) and boot-strapping techniques (Caers and Maes 1998) are used to reduce the uncertainty of the tail-based estimates; however, boot-strapping techniques still require sufficiently long data records and a careful analysis of the influence of data sampling uncertainties (Van Noortwijk and Van Gelder 1998).…”

Section: Improved Precisionmentioning

confidence: 99%

Should Probabilistic Design Replace Safety Factors?

Doorn

Hansson

2010

Philos. Technol.

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Safety is a concern in almost all branches of engineering. Whereas safety was traditionally introduced by applying safety factors or margins to the calculated maximum load, this approach is increasingly replaced with probabilistic risk assessment (PRA) as a tool for dimensioning safety measures. In this paper, the two approaches are compared in terms of what they aim at and what they can, in fact, achieve. The outcome of this comparison suggests that the two approaches should be seen as complementary rather than mutually exclusive. PRA is particularly useful for priority setting and for the effect evaluation of safety measures; however, in most applications, uncertainties prevent PRA from providing an objective probability of failure or value of damage. Safety factors are indispensible for dealing with dangers that cannot be assigned meaningful probabilities.

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Identifying tails, bounds and end-points of random variables

Cited by 37 publications

References 14 publications

Probability distributions of wave run-up on a TLP model

Probability distributions of wave run-up on a TLP model

Exploring Nonlinearities in Financial Systemic Risk

Should Probabilistic Design Replace Safety Factors?

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