Maximum likelihood estimation for $\alpha$-stable autoregressive processes

TL;DR

This paper develops maximum likelihood estimators for $lpha$-stable autoregressive processes, providing their asymptotic distributions, bootstrap validation, and finite-sample performance, with applications to financial time series.

Contribution

It introduces MLE for $lpha$-stable AR processes, deriving their asymptotic properties and validating bootstrap methods for inference.

Findings

Estimators for AR parameters are $n^{1/lpha}$-consistent with intractable limiting distribution.

Stable noise parameters estimators are $n^{1/2}$-consistent and asymptotically normal.

Simulation studies and real data application demonstrate estimator performance.

Abstract

We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian $\alpha$-stable noise. A nondegenerate limiting distribution is given for maximum likelihood estimators of the parameters of the autoregressive model equation and the parameters of the stable noise distribution. The estimators for the autoregressive parameters are $n^{1/\alpha}$-consistent and converge in distribution to the maximizer of a random function. The form of this limiting distribution is intractable, but the shape of the distribution for these estimators can be examined using the bootstrap procedure. The bootstrap is asymptotically valid under general conditions. The estimators for the parameters of the stable noise distribution have the traditional $n^{1/2}$ rate of convergence and are asymptotically normal. The behavior of the estimators for finite samples is studied via simulation, and we use maximum likelihood estimation to fit a noncausal autoregressive model to the natural logarithms of volumes of Wal-Mart stock traded daily on the New York Stock Exchange.