| dc.description.abstract | Extreme events have large impact throughout the span of engineering,
science and economics. This is because extreme events often lead to failure and losses
due to the nature unobservable of extra ordinary occurrences. In this context this
paper focuses on appropriate statistical methods relating to a combination of quantile
regression approach and extreme value theory to model the excesses. This plays a
vital role in risk management. Locally, nonparametric quantile regression is used, a
method that is flexible and best suited when one knows little about the functional
forms of the object being estimated. The conditions are derived in order to estimate
the extreme value distribution function. The threshold model of extreme values is
used to circumvent the lack of adequate observation problem at the tail of the
distribution function. The application of a selection of these techniques is
demonstrated on the volatile fuel market. The results indicate that the method used
can extract maximum possible reliable information from the data. The key attraction
of this method is that it offers a set of ready made approaches to the most difficult
problem of risk modeling | en_US |
