Anticipative alpha-stable linear processes for time series analysis : conditional dynamics and estimation

Research output: PhD ThesisPhD Thesis - Research external, graduation external

Abstract

In the framework of linear time series analysis, we study a class of so-called anticipative strictly stationary processes potentially depending on all the terms of an independent and identically distributed alpha-stable errors sequence.Focusing first on autoregressive (AR) processes, it is shown that higher order conditional moments than marginal ones exist provided the characteristic polynomials admits at least one root inside the unit circle. The forms of the first and second order moments are obtained in special cases.The least squares method is shown to provide a consistent estimator of an all-pass causal representation of the process, the validity of which can be tested by a portmanteau-type test. A method based on extreme residuals clustering is proposed to determine the original AR representation.The anticipative stable AR(1) is studied in details in the framework of bivariate alpha-stable random vectors and the functional forms of its first four conditional moments are obtained under any admissible parameterisation.It is shown that during extreme events, these moments become equivalent to those of a two-point distribution charging two polarly-opposite future paths: exponential growth or collapse.Parallel results are obtained for the continuous time counterpart of the AR(1), the anticipative stable Ornstein-Uhlenbeck process.For infinite alpha-stable moving averages, the conditional distribution of future paths given the observed past trajectory during extreme events is derived on the basis of a new representation of stable random vectors on unit cylinders relative to semi-norms.Contrary to the case of norms, such representation yield a multivariate regularly varying tails property appropriate for prediction purposes, but not all stable vectors admit such a representation.A characterisation is provided and it is shown that finite length paths of a stable moving average admit such representation provided the process is "anticipative enough".Processes resulting from the linear combination of stable moving averages are encompassed, and the conditional distribution has a natural interpretation in terms of pattern identification.
Original languageEnglish
QualificationPhD
Awarding Institution
  • Université Paris-Saclay
  • ENSAE ParisTech
Supervisors/Advisors
  • Zakoian, Jean Michel, Supervisor, External person
Thesis sponsors
Award date4 Dec 2018
Publication statusPublished - 11 Jan 2019
Externally publishedYes

Keywords

  • Time series
  • Noncausal process
  • Anticipative process
  • Multivariate stable distribution
  • Conditional distribution
  • Prediction

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