In the 18th century, statisticians sometimes worked as consultants to gamblers. In order to answer questions like "If a fair coin is °ipped 100 times, what is the probability of getting 60 or more heads?", Abraham de Moivre discovered the so-called "normal curve". Independently, Pierre-Simon Laplace derived the central limit theorem, where the normal distribution acts as the limit for the distribution of the sample mean.
Nowadays, statisticians sometimes work as consultants for economists, to whom the normal distribution is far from a satisfactory model. For example, one may need to model large-impact ¯nancial events in order to to answer questions like "What is the probability of getting into a crisis period similar to the credit squeeze in 2007 in the coming 10 years?". At ¯rst glance, estimating the chances of events that rarely happen or even have never happened before sounds like a "mission impossible". The development of Extreme Value Theory (EVT) shows that it is in fact possible to achieve this goal.
On Extreme Value Statistics Chen Zhou (http://publishing.eur.nl/ir/repub/asset/14290/ThesisDef.pdf)
(http://research.stlouisfed.org/wp/1989/1989-006.pdf)
(http://creditlab.stanford.edu/Research.htm)
(http://soe.stanford.edu/research/layoutMSnE.php?sunetid=giesecke)
(http://www.gsb.stanford.edu/facseminars/pdfs/credit_20.pdf)
(http://www.stanford.edu/dept/MSandE/cgi-bin/people/faculty/giesecke/pdfs/glss.pdf)
(http://www-m4.ma.tum.de/Papers/Klueppelberg/EVTFinance061207.pdf)
