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Fractional Gaussian Noise: Spectral Density and Estimation Methods
Journal of Time Series Analysis, Volume 46, Issue 6, Page 1146-1174, November 2025.The fractional Brownian motion (fBm) process, governed by a fractional parameter H∈(0,1)$$ H\in \left(0,1\right) $$, is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn.
Shuping Shi, Jun Yu, Chen Zhang
wiley +1 more source
Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2015 We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors.
Perron, Pierre, Varneskov, Rasmus T.
core
S&P 500 microstructure noise components: empirical inferences from futures and ETF prices
Journal of Time Series Analysis, Volume 46, Issue 6, Page 1032-1063, November 2025.By studying the differences between futures prices and exchange‐traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by positive bid‐ask spreads and discrete price scales.
Stephen J. Taylor
wiley +1 more source
Forecasting cryptocurrency prices time series using machine learning approach [PDF]
SHS Web of Conferences, 2019 This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and ...
Derbentsev Vasily +3 more
doaj +1 more source
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA [PDF]
, 2016 Long memory plays an important role in many fields by determining the behaviour and predictability of systems; for instance, climate, hydrology, finance, networks and DNA sequencing.
Franzke, Christian +3 more
core +2 more sources
Local powers of least‐squares‐based test for panel fractional Ornstein–Uhlenbeck process
Journal of Time Series Analysis, Volume 46, Issue 5, Page 997-1023, September 2025.In recent years, significant advancements have been made in the field of identifying financial asset price bubbles, particularly through the development of time‐series unit‐root tests featuring fractionally integrated errors and panel unit‐root tests.
Katsuto Tanaka, Weilin Xiao, Jun Yu
wiley +1 more source
Вестник Пермского университета: Серия Экономика, 2019 The task of forecasting the dynamics of changes in the rates of financial instruments is relevant, since its solution would reduce risks and increase the profitability of operations in financial markets.
Pyotr Mikhailovich Simonov +1 more
doaj +1 more source
Forecasting Digital Asset Return: An Application of Machine Learning Model
International Journal of Finance &Economics, Volume 30, Issue 3, Page 3169-3186, July 2025.ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti +4 more
wiley +1 more source
An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series [PDF]
, 2004 This paper addresses the notion that many fractional I(d) processes may fall into the ?empty box? category, as discussed in Granger (1999). We present ex ante forecasting evidence based on an updated version of the absolute returns series examined by ...
Bhardwaj, Geetesh, Swanson, Norman R.
core
Local Whittle estimation in time‐varying long memory series
Journal of Time Series Analysis, Volume 46, Issue 4, Page 647-673, July 2025.The memory parameter is usually assumed to be constant in traditional long memory time series. We relax this restriction by considering the memory a time‐varying function that depends on a finite number of parameters. A time‐varying Local Whittle estimator of these parameters, and hence of the memory function, is proposed.
Josu Arteche, Luis F. Martins
wiley +1 more source