In a recent publication Stadnitski (2012) presented an overview of methods to estimate fractal scaling in time series, outlined as an accessible tutorial1. The publication was set-up as a comparison between monofractal and ARFIMA methods, and promotes ARFIMA to distinguish between spurious and genuine 1/f noise, shedding light on “the problem that the log–log power spectrum of short-memory ARMA...

Abstract The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional (fGARCH) were applied to study volatility of Fractionally Integrated Moving Average (ARFIMA) model, which is primary objective this study. other goal paper expand on researchers’ previous work by examining long memory volatilities simultaneously, using ARFIMA-sGARCH hybrid comparing ...

2 1 1 =0 | | d t t t p p q q d d k k t () () ()(1) () = () (0) () () (1) (1) = () ())(+ 1) () () 0 5 1. Fractionally integrated timeseries and ARFIMA modelling 1 This presentation of ARFIMA modelling draws heavily from Baum and Wiggins (2000). The model of an autoregressive fractionally integrated moving average process of a timeseries of order , denoted by ARFIMA , with mean , may be written u...

Ce document est publié dans l'intention de rendre accessibles les résultats préliminaires de la recherche effectuée au CIRANO, afin de susciter des échanges et des suggestions. Les idées et les opinions émises sont sous l'unique responsabilité des auteurs, et ne représentent pas nécessairement les positions du CIRANO ou de ses partenaires. This paper presents preliminary research carried out at...

Autoregressive fractional integrated moving average modeling strategy was used to model the daily average temperature (DAT) series of Sokoto metropolis for the period of 01/01/2003 to 03/04/2007. The time plot suggests that there is persistence dependence in the series. The order of fractional integration was found to be 0.6238841. The correct model for the daily average temperature data (DAT) ...

This paper proposes an easy test for independence between two stationary autoregressive fractionally integrated moving average (ARFIMA) processes via AR approximations. We prove that an ARFIMA (p, d, q) process, φ(L)(1 − L)yt = θ(L)et, d ∈ (0, 0.5), where et is a white noise, can be approximated well by an autoregressive (AR) model and establish the theoretical foundation of Haugh’s (1976) stat...