LinkedIn AMS MathSciNet Author Profile
Research & Publications

Recens Nuntium - Recent News

Research Interests & Academic Service

author profile & Google Scholar profile

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 applications to AML analysis, 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; path: Paul Erdös \( \longrightarrow\) Endre Csáki \( \longrightarrow\) Lajos Horváth \( \longrightarrow\) Gilles Teyssière


  1. D. Surgailis, G. Teyssière and M. Vaičiulis. The increment ratio statistic.
    Journal of Multivariate Analysis (2008) vol 99, 510-541. MR2396977, (Supplementary Material )
  2. 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,
  3. 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,
  4. 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
  5. M. Lavielle and G. Teyssière. Detection of multiple change-points in multivariate time series.
    Lithuanian Mathematical Journal (2006) vol 46, 287-306. MR2285348,
  6. M. Lavielle and G. Teyssière. Détection de ruptures multiples dans des séries temporelles multivariées
    Lietuvos Matematikos Rinkinys (2006) vol 46, 351-376,
  7. 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.
  8. A. Kirman and G. Teyssière. Testing for bubbles and change-points.
    Journal of Economic Dynamics and Control (2005) vol 29, 765-799. MR2129522,
  9. 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,
  10. 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
  11. 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.
  12. G. Teyssière. Interaction models for common long-range dependence in asset price volatilities.
    Invited chapter in Processes with Long Range Correlations: Theory and Applications. Lecture Notes in Physics. G. Rangarajan and M. Ding editors, vol 621, 251-269, Springer (2003). DOI, Invited lecture to the International Conference on Long-Range Dependent Stochastic Processes and their Applications, Bangalore, India, January 2002.
  13. 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,
  14. 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.
  15. G. Teyssière and A. Kirman. Microeconomic models for long-memory in the volatility of financial time series.
    SNDE (2002) vol 5, 281-302. DOI.
  16. 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,
  17. 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,
  18. 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).


Long-Memory in Economics
G. Teyssière and A. Kirman editors, Springer (2007).
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).
ISBN (Paperback): 978-3642141034
ISBN (eBook): 978-3642141041


Updated May 5, 2024.