Subject: class proposal by yannis
hi vince ,
yannis of the weather desk is planning to develop relationship with prof
rene carmona in doing weather analysis . to start this off , they are planning
to pay prof carmona to give a training class as outlined below , and they
want to know if research is willing to send people and bear part of the costs .
we can talk more at 4 : 00 pm , but while there is no doubt that getting people
from outside to present new ideas is always important and interesting , i
think research group members can easily give most of these talks .
apparently , people are interested in these topics and are willing to pay to
listen . my thought is that if the intent is to develop relationships , that
is fine , but the research group should also be given the opportunity to
provide more training and get more visibility . i have already communicated
this to joe and to yannis .
vasant
- - - - - - - - - - - - - - - - - - - - - - forwarded by vasant shanbhogue / hou / ect on 03 / 05 / 2001
10 : 40 am - - - - - - - - - - - - - - - - - - - - - - - - - - -
from : yannis tzamouranis / enron @ enronxgate on 03 / 05 / 2001 10 : 01 am
to : vasant shanbhogue / hou / ect @ ect
cc :
subject :
here is a tentative course description .
day 1 : extreme value distriutions and copulas
1 . heavy tail distributions : exploratory data analysis and detection .
extreme value distributions and generalized pareto distributions .
estimation and simulation . practical examples .
2 . notions of dependence and copulas . estimation and simulation .
experiments with the program evanece .
day 2 : principal component analysis and modern regression
1 . principal component analysis and applications to the yield curve and
the detection of contagion in financial markets .
2 . nonlinear regression and the construction of yield curves .
3 . nonparametric regression ( kernel and projection pursuit methods ) and
alternatives to the black - scholes formula to option pricing .
day 3 : time series analysis
examples of temperature time series will be used to introduce and
illustrate the following concepts and techniques :
1 . removing trends and seasonal components , and stationarity .
2 . fitting the classical autoregressive and moving average models .
3 . discretization of stochastic differential equations
4 . multivariate time series
day 4 : nonlinear systems and filtering
1 . arch , garch and stochastic volatility models
2 . linear state space models and the classical kalman filter
3 . nonlinear systems and particle filtering .
4 . applications