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Value-at-risk and extreme returns [PDF]

Annales d'economie et de statistique, 1998
Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the non-parametric empirical distribution function.
Jón Daníelsson, Casper G. de Vries
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The Laplacian and Mean and Extreme Values

The American Mathematical Monthly, 2016
The Laplace operator is pervasive in many important mathematical models, and fundamental results such as the Mean Value Theorem for harmonic functions, and the Max- imum Principle for super-harmonic functions are well-known. Less well-known is how the Laplacian and its powers appear naturally in a series expansion of the mean value of a func- tion on a
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Approximations for Bivariate Extreme Values

Extremes, 2000
Let \((X,Y)\) denote a random vector with distribution function (d.f.) \(F\) and suppose that both \(X\) and \(Y\) are standardized to have \[ \text{Pr}(X\leq x)=\text{Pr}(Y\leq x)=\exp(-x^{-1}),\quad x>0. \] In recent years a number of statistical models have been proposed for extreme values of \((X,Y).\) The basis for these models is the assumption ...
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On selecting an extreme value distribution

Zeitschrift für Operations Research, 1988
In a recent paper Hernandez and Johnson (1984) have given a procedure based on Bayesian statistical inference for selecting an extreme-value distribution to “best” fit available data. In this note we give an alternative derivation of part of their results.
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Testing Extreme Value Models

Extremes, 2000
For the extreme value distribution (EVD) and the generalized Pareto distribution (GPD) with scale and location parameters asymptotically uniformly optimal tests for one-sided and two-sided hypotheses on the shape parameter are considered. Using the local asymptotic normality (LAN) property the author derives the asymptotic power of the tests under ...
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Predicting Rare Extreme Values

2006
Modelling extreme data is very important in several application domains, like for instance finance, meteorology, ecology, etc.. This paper addresses the problem of predicting extreme values of a continuous variable. The main distinguishing feature of our target applications resides on the fact that these values are rare.
Luís Torgo, Rita P. Ribeiro
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Value Based Extreme Programming

AGILE 2006 (AGILE'06), 2006
Agile methods, such as Scrum and extreme programming, are not known for carefully tracking to time and cost estimates. On most projects, schedule slips are common and cost increases are predictable. At the end of every iteration, some of our stories get dropped, usually due to reasons such as "the story took longer than what we expected", or "we didn't
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Estimation of Value at Risk by Extreme Value Methods

Extremes, 2000
The article is devoted to the estimation (prediction) of quantiles of financial assets returns distributions. Such quantiles are called Values at Risk (VaR). The author describes methods based on the Gaussian model, empirical quantiles, estimation of parameters of generalized extreme value and generalized Pareto distributions (GPD).
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Extreme Weather and Climate Change: Population Health and Health System Implications

Annual Review of Public Health, 2021
Katie Hayes, Colleen Reid, June Spector   +2 more
exaly  

Association of Extreme Heat and Cardiovascular Mortality in the United States: A County-Level Longitudinal Analysis From 2008 to 2017

Circulation, 2022
Sameed Ahmed Khatana, , Rachel Werner
exaly