English

Linear Multifractional Stable Motion: wavelet estimation of $H(\cdot)$ and $\al$ parameters

Statistics Theory 2013-04-11 v1 Statistics Theory

Abstract

Linear Fractional Stable Motion (LFSM) of Hurst parameter HH and of stability parameter \al\al, is one of the most classical extensions of the well-known Gaussian Fractional Brownian Motion (FBM), to the setting of heavy-tailed stable distributions \cite{SamTaq,EmMa}. In order to overcome some limitations of its areas of application, coming from stationarity of its increments as well as constancy over time of its self-similarity exponent, Stoev and Taqqu introduced in \cite{stoev2004stochastic} an extension of LFSM, called Linear Multifractional Stable Motion (LMSM), in which the Hurst parameter becomes a function H()H(\cdot) depending on the time variable tt. Similarly to LFSM, the tail heaviness of the marginal distributions of LMSM is determined by \al\al; also, under some conditions, its self-similarity is governed by H()H(\cdot) and its path roughness is closely related to H()1/\alH(\cdot)-1/\al. Namely, it was shown in \cite{stoev2004stochastic} that H(t0)H(t_0) is the self-similarity exponent of LMSM at a time t00t_0\neq 0; moreover, very recently, it was established in \cite{hamonier2012lmsm}, that the quantities mintIH(t)1/\al\min_{t\in I} H(t)-1/\al, and H(t0)1/\alH(t_0)-1/\al, are respectively the uniform H\"older exponent of LMSM on a compact interval II, and its local H\"older exponent at t0t_0. The main goal of our article, is to construct, using wavelet coefficients of LMSM, strongly consistent (i.e. almost surely convergent) statistical estimators of mintIH(t)\min_{t\in I} H(t), H(t0)H(t_0), and \al\al; our estimation results, are obtained when \al(1,2)\al\in (1,2), and, H()H(\cdot) is a H\"older function smooth enough, with values in a compact subinterval [H,Hˉ][\underline{H},\bar{H}] of (1/\al,1)(1/\al,1).

Keywords

Cite

@article{arxiv.1304.2995,
  title  = {Linear Multifractional Stable Motion: wavelet estimation of $H(\cdot)$ and $\al$ parameters},
  author = {Antoine Ayache and Julien Hamonier},
  journal= {arXiv preprint arXiv:1304.2995},
  year   = {2013}
}
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