I'm interested, but not exclusively, in the following topics:
Machine learning, artificial intelligence, and complexity in financial markets,
Long-memory and multifractal processes, change-point detection,
Wavelet signal processing of scaling processes.
I'm currently working on modern machine learning and AI algorithms. Among other things, I'm developing deep learning architectures for anomaly detection,
with Jean-Pierre Brun, and
Casey King,
Yale University. My second strand of research focuses on the
study of the scaling, multifractal, properties of very large data sets of irregularly spaced financial transaction data by means of wavelets.
Besides that, I'm a frequent reviewer for
Mathematical Reviews (MathSciNet®), with over 100 reviews and counting. The full list of my reviews is available on my
MathSciNet author profile .
Mathematical Reviews is edited by the American Mathematical Society, of which
I'm a member.
My Erdös number is 3; the path is:
Paul Erdös \( \longrightarrow\) Endre Csáki \( \longrightarrow\) Lajos Horváth
\( \longrightarrow\) Gilles Teyssière
Publications
D. Surgailis, G. Teyssière and M. Vaičiulis.
The increment ratio statistic.
Journal of Multivariate Analysis (2008) vol
99, 510-541.
MR2396977,
(Supplementary Material )
M. Lavielle and G. Teyssière.
Adaptive detection of multiple change-points in asset price
volatility.
In
Long-Memory in Economics.
G. Teyssière et al. editors, 129-156, Springer (2007).
MR2265058,
G. Teyssière and P. Abry.
Wavelet analysis of nonlinear long-range dependent
processes. Applications to financial time series.
In
Long-Memory in Economics.
G. Teyssière et al. editors, 173-238, Springer (2007).
MR2265060,
D. Kateb, A. Seghier and G. Teyssière.
Prediction, orthogonal polynomials and Toeplitz matrices:
a fast and reliable approximation to the Durbin-Levinson algorithm,
In
Long-Memory in Economics.
G. Teyssière et al. editors, 239-261, Springer (2007).
MR2265061
M. Lavielle and G. Teyssière.
Detection of multiple change-points in multivariate time
series.
Lithuanian Mathematical Journal (2006) vol
46, 287-306.
MR2285348,
M. Lavielle and G. Teyssière.
Détection de
ruptures multiples dans des séries temporelles multivariées
Lietuvos Matematikos Rinkinys (2006) vol
46, 351-376,
P. Doukhan, G. Teyssière and P. Winant.
A LARCH\((\infty)\) vector valued process.
In Dependence in Probability and Statistics.Lecture Notes in Statistics,, vol 187, 245-258, Springer (2006).
MR2283258.
A. Kirman and G. Teyssière.
Testing for bubbles and change-points.
Journal of Economic Dynamics and Control
(2005) vol 29, 765-799.
MR2129522,
L. Horváth, P. Kokoszka and G. Teyssière.
Bootstrap misspecification tests for ARCH based on the
empirical process of squared residuals.
Journal of Statistical Computation and
Simulation (2004) vol 74, 469-485.
MR2073226,
P. Kokoszka, G. Teyssière and A. Zhang.
Confidence intervals for the autocorrelations
of the squares of GARCH sequences.
In Computational Science - ICCS 2004. Lecture Notes in Computer
Science. M. Bubak et al. editors,
vol 3039, 827-834, Springer (2004).
MR2233424,
Volume for the Workshop on Computational Methods in Finance and Insurance, Kraków, Poland, June 2004.
Slides
L. Giraitis, P. Kokoszka, R. Leipus and G. Teyssière.
On the power of \(R/S\)-type tests under contiguous and semi
long-memory alternatives.
Acta Applicandae Mathematicae (2003) vol 78, 285-299.
MR2024032,
(Special Issue for the 8th Vilnius Conference
on Probability Theory and Mathematical Statistics), Lithuania.
L. Giraitis, P. Kokoszka, R. Leipus and G. Teyssière.
Rescaled variance and related tests for
long memory in volatility and levels.
Journal of Econometrics (2003) vol 112,
265-294.
MR1951145,
See also L. Giraitis, P. Kokoszka, R. Leipus and G. Teyssière,
Corrigendum to "Rescaled variance and
related tests for long memory in volatility and levels",
Journal of Econometrics (2005)
vol 126, 571-572.
MR2155635,
A. Kirman and G. Teyssière.
Bubbles and Long Range Dependence in Asset Prices
Volatilities.
In Equilibrium, Markets and Dynamics.
C.H. Hommes, R. Ramer and C. Withagen editors, 307-327, Springer (2002).
DOI.
G. Teyssière and A. Kirman.
Microeconomic models for long-memory in the volatility of financial time series.
SNDE (2002) vol 5, 281-302.
DOI.
L. Horváth, P. Kokoszka and G. Teyssière.
Empirical process of the squared residuals
of an ARCH sequence.
The Annals of Statistics (2001) vol 29, 445-469.
MR1863965,
L. Giraitis, P. Kokoszka, R. Leipus and G. Teyssière.
Semiparametric estimation of the intensity
of long-memory in conditional heteroskedasticity.
Statistical Inference for Stochastic
Processes (2000) vol 3, 113-128.
(Special Issue on Limit Theorems and Long-Range Dependence).MR1819290,
G. Teyssière. Multivariate long-memory ARCH
modelling for high frequency foreign exchange rates.
In Proceedings of the Second High Frequency Data in Finance (HFDF-II) Conference,
Olsen &
Associates, Zurich, (1998).
In 1996, I pioneered the class of double long memory processes:
Double Long-Memory Financial Time Series (1996) Univ. of London, with the ARFIMA-FIGARCH. This was my first paper in time series analysis.
Books
Long-Memory in Economics
G. Teyssière and A. Kirman editors, Springer (2007).
MR2263582 ISBN (Hardcover): 978-3540226949
ISBN (Paperback): 978-3642061547
ISBN (eBook): 978-3540346258
Dependence in Probability and Statistics,
Lecture Notes in Statistics, Vol 200.
G. Lang, D. Surgailis and G. Teyssière editors, Springer (2010).
MR2741808 ISBN (Paperback): 978-3642141034
ISBN (eBook): 978-3642141041