Results 231 to 240 of about 662,489 (267)

Value at Risk and Extreme Values

IFAC Proceedings Volumes, 1998
Abstract this paper gives a general exposition of the subject of Value at Risk (VaR), which is now considered as a standard measure of market risks. It is defined as the maximal loss of the portfolio for a given probability over a given period. This measure is sensitive to the tails of the distribution of returns; extreme value theory is used here to
openaire   +1 more source

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 ...
openaire   +1 more source

Testing Extreme Value Conditions

Extremes, 2002
A modification of the Cramér-von Mises statistics for testing the tail behaviour of i.i.d. sample CDF \(F\) is considered. Its version for nonnegative tail index \(\gamma\) is of the form \[ T_{k,n}=\int \left( {1\over \hat\gamma} (\log X_{n-[kt],n}-\log X_{n-k,n})+\log t \right)^2 t^2\, dt, \] where \(X_{i,n}\) is the \(i\)th order statistics, \(\hat ...
Dietrich, D, de Haan, Laurens, Hüsler, J   +2 more
openaire   +2 more sources

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
openaire   +3 more sources

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.
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +2 more sources

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 ...
openaire   +1 more source

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
openaire   +2 more sources

K-record values and the extreme-value index

Journal of Statistical Planning and Inference, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source