Extreme Value Theory


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Extreme Value Theory

In statistics, any way to estimate or measure the likelihood of an extremely unlikely event. That is, extreme value theory measures the probability that a data point that deviates significantly from the mean will occur. It is useful in insurance to measure the risk of catastrophic events, such as tornados and wildfires.
References in periodicals archive ?
Introduced by McNeil and Frey (2000), and applied to Nord Pool spot prices by Bystrom (2005), the Extreme Value Theory (EVT) approach has proven superior to the use of a standard AR-GARCH approach.
2004, "Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory," International Review of Financial Analysis 13, 133 - 152.
The results presented in this table reveal that the models that do not resort to extreme value theory, whether on the assumption of normal distribution or t-student distribution, generally showed difficulties in adapting to extreme changes in market.
The use of the extreme value theory leads to different conclusions from those listed above, about the multivariate GARCH models.
Another approach is to use the Extreme Value Theory to construct models which account for such thick tails as are empirically observed.
Last one is the Static EVT method in which returns are assumed to have fat-tailed distribution and extreme value theory is applied to the upper tail of the returns.
While the average strength of the wires was greater than 60 N (~6 lb), extreme value theory predicts that one wire in one hundred would break at 3 N (~3 lb), 1/2 the average strength.
The recent turmoil that has occurred in Asian financial markets provides interesting exploratory opportunities to use the extreme value theory to analyze these markets.
One such area is the modeling of extreme events, the probabilistic nature of which remains poorly understood, and for which recent developments in extreme value theory hold promise.
the concepts of backtesting, stress testing and extreme value theory in risk measurement
The first core reference on the latest developments in extreme value theory and its application in the finance and insurance industry
Key topics such as extreme value theory, volatility modelling, principle components, confidence intervals and fitting probability distributions to real data are covered in sufficient detail so that these methods can be integrated into your own risk management systems.
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