G
Gilles Teyssière
Researcher at University of Paris
Publications - 28
Citations - 1438
Gilles Teyssière is an academic researcher from University of Paris. The author has contributed to research in topics: Volatility (finance) & Conditional variance. The author has an hindex of 18, co-authored 28 publications receiving 1370 citations. Previous affiliations of Gilles Teyssière include Charité & University of Gothenburg.
Papers
More filters
Journal ArticleDOI
Rescaled variance and related tests for long memory in volatility and levels
TL;DR: In this paper, a rescaled variance test based on V/S statistic was proposed for general fourth order stationary sequences, which is shown to have a simpler asymptotic distribution and to achieve a somewhat better balance of size and power than Lo's modified R/S test and the KPSS test of Kwiatkowski et al.
Journal ArticleDOI
Detection of multiple change-points in multivariate time series
Marc Lavielle,Gilles Teyssière +1 more
TL;DR: In this article, the authors consider the multiple change-point problem for multivariate time series, including strongly dependent processes, with an unknown number of change-points, and propose an adaptive method to detect changes in multivariate i.i.d., weakly and strongly dependent series.
Posted Content
Microeconomic models for long-memory in the volatility of financial time series
Alan Kirman,Gilles Teyssière +1 more
TL;DR: In this article, a class of microeconomic behavioral models with interacting agents, derived from Kirman (1991, 1993), can replicate the empirical long-memory properties of the two first conditional moments of financial time series.
BookDOI
Long memory in economics
Gilles Teyssière,Alan Kirman +1 more
TL;DR: In this article, a nonlinear structural model for volatility clustering in financial markets is proposed, which is based on the Durbin-Levinson algorithm, and the model is applied to financial time series.
Book ChapterDOI
Adaptive Detection of Multiple Change-Points in Asset Price Volatility
Marc Lavielle,Gilles Teyssière +1 more
TL;DR: In this article, an adaptive method for finding the segmentation, i.e., the sequence of change-points τ with the optimal level of resolution, was proposed for time series, including strongly dependent processes.