The tutorials will take place on Friday the 30th of November 2012 at Salón cultural CajAstur (c/San Francisco, 4 - entrance by c/Mendizabal). View the location by downloading the map of the center of Oviedo. The number of participants to the tutorials is limited and restricted only to those who attend the conference. For
further information please contact Ana Colubi.
TUTORIAL 1: 9:00-13:45 (coffee break at 11:00)
Dynamic models for volatility and heavy tails (Tutorial 1.pdf)
Andrew Harvey, Faculty of Economics, University of Cambridge, UK.
Email: Contact
This set of lectures will describe a class of nonlinear time series
models known as dynamic conditional score (DCS) models. The models are
designed to extract a dynamic signal from noisy observations. The
signal may be the level of a series or it may be a measure of
scale. Changing scale is of considerable importance in financial time
series where volatility clustering is an established stylized fact.
Generalized autoregressive conditional heteroscedasticity (GARCH)
models are widely used to extract the current variance of a
series. However, using variance (or rather standard deviation) as a
measure of scale may not be appropriate for non-Gaussian (conditional)
distributions. This is of some importance, since another established
feature of financial returns is that they are characterized by heavy
tails.
The dynamic equations in GARCH models are filters. Just as the
filters for linear Gaussian level models are linear combinations of
past observations, so GARCH filters, because of their Gaussian
origins, are usually linear com- binations of past squared
observations. The models described here replace the observations or
their squares by the score of the conditional distribution. When
modeling scale, an exponential link function is employed, as in expo-
nential GARCH (EGARCH), thereby ensuring that the filtered scale
remains positive. The unifying feature of the models in the proposed
class is that the as- ymptotic distribution of the maximum likelihood
estimators is established by a single theorem which delivers an
explicit analytic expression for the asymptotic covariance matrix of
the estimators. The conditions under which the asymptot- ics go
through are relatively straightforward to verify. There is no such
general theory for GARCH models.
Other properties of the proposed models may be found. These
include analytic expressions for moments, autocorrelation functions
and multi-step forecasts. The properties, particularly for the
volatility models, which employ an exponential link function, are more
general than is usually the case. For exam- ple, expressions for
unconditional moments, autocorrelations and the conditional moments of
multi-step predictive distributions can be found for absolute values
of the observations raised to any power. The generality of the
approach is further illustrated by consideration of dynamic models for
non-negative variables. Such models have been used for mod- eling
durations, range and realized volatility in finance. Again the use of
an exponential link function combined with a dynamic equation driven
by the con- ditional score gives a range of analytic results similar
to those obtained with the new class of EGARCH models.
Download here the material of the course Tutorial 1 - Material
TUTORIAL 2: 15:15-20:00 (coffee break at 17:00)
Functional data analysis.
Hans-Georg Müller, Department of Statistics, University of California, Davis, USA.
Email: Contact
The tutorial provides an introduction to this area, emphasizing
methods, some computational aspects
and various applications for the following and related topics:
Functional Principal Component Analysis as a central tool; Functional
data designs; Principal Analysis by Conditional Expectation (PACE);
Functional linear regression for scalar and functional responses;
Functional diagnostics; Functional response and dose-response models;
Functional methods for longitudinal data; Generalized
functional linear models; Functional polynomial models; Functional
additive models; Functional gradient models; Empirical dynamics and
dynamics learning. Download here the material of the course Tutorial 2 - Material
Programme Summary
