Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series

TL;DR

This paper introduces a novel moving estimator approach for adaptive, nonstationary time series analysis, focusing on alpha-Stable distributions and Hurst exponent estimation, with applications to financial data like DJIA.

Contribution

It proposes a bias-avoiding, local parameter estimation method using exponential weighting, applied to alpha-Stable distributions and market stability assessment.

Findings

Adaptive estimation of alpha parameter for market stability.

Continuous evaluation of distribution tail behavior.

Application to DJIA data demonstrates real-time tracking.

Abstract

Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln(\rho_\theta (x_\tau))$ local log-likelihood with exponentially weakening weights of the old values. In practice such moving estimates can be found by EMA (exponential moving average) of some parameters, like $m_p=E[|x-\mu|^p]$ absolute central moments, updated by $m_{p,t+1} = m_{p,t} + \eta (|x_t-\mu_t|^p-m_{p,t})$. We will focus here on its applications for alpha-Stable distribution, which also influences Hurst exponent, hence can be used for its adaptive estimation. Its application will be shown on financial data as DJIA time series - beside standard estimation of evolution of center $\mu$ and scale parameter $\sigma$, there is also estimated evolution of $\alpha$ parameter allowing to continuously evaluate market stability - tails having $\rho(x) \sim 1/|x|^{\alpha+1}$ behavior, controlling probability of potentially dangerous extreme events.