Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data

TL;DR

This paper develops a flexible, easy-to-implement semiparametric method for estimating the fractal index of time series, addressing measurement noise issues and providing practical tools for empirical analysis.

Contribution

It extends the fractal index estimator to non-Gaussian processes, introduces a noise-robust estimator, and offers a hypothesis test for noise presence.

Findings

Estimator works well on simulated data

Proposed noise-robust estimator reduces bias

Applied methods successfully to turbulence and financial data

Abstract

We study a well-known estimator of the fractal index of a stochastic process. Our framework is very general and encompasses many models of interest; we show how to extend the theory of the estimator to a large class of non-Gaussian processes. Particular focus is on clarity and ease of implementation of the estimator and the associated asymptotic results, making it easy for practitioners to apply the methods. We additionally show how measurement noise in the observations will bias the estimator, potentially resulting in the practitioner erroneously finding evidence of fractal characteristics in a time series. We propose a new estimator which is robust to such noise and construct a formal hypothesis test for the presence of noise in the observations. Finally, the methods are illustrated on two empirical data sets; one of turbulent velocity flows and one of financial prices.