Results 51 to 60 of about 2,441 (205)
Data & Policy, 2022 The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System ...
Tamir Libel
doaj +1 more source
International Journal of Finance &Economics, Volume 31, Issue 2, Page 2845-2862, April 2026.ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio.
Xinran Huang +3 more
wiley +1 more source
Discrete Dynamics in Nature and Society, Volume 2026, Issue 1, 2026.Today, the astonishing growth of digital currency has attracted many bold investors. This has caused digital currencies to be gradually introduced as a new asset class with its own criteria. However, the relationship between traditional assets and new assets is not yet deeply understood. This study’s objective is to investigate the dynamic relationship
Farzaneh Shams Tarnabi, Fabio Tramontana
wiley +1 more source
Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.In this paper, we model edge traffic with a conformable fractional partial differential equation that keeps memory in time and space. The solution represents a unit‐free attack pressure, built from a z‐scored edge series, a quiet period baseline, and a partially absorbing boundary that reflects scrubbing and rate limits.
Ahmad Alshanty +3 more
wiley +1 more source
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
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
Estimation of a stationary multivariate ARFIMA process [PDF]
, 2019 In this note, we consider an m-dimensional stationary multivariate long memory ARFIMA (AutoRegressive Fractionally Integrated Moving Average) process, which is defined as : A(L)D(L) (y1(t),...,ym(t))' = B(L) (∈1(t),..., ∈m(t))', where M' denotes the ...
Mbeke, Kévin Stanislas, Hili, Ouagnina
core +1 more source
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
ARFIMA and FIEGARCH processes with non-Gaussian innovations [PDF]
, 2022 O principal objetivo deste trabalho é analisar inovações não Gaussianas para séries temporais de processos que apresentam longa-dependência na esperança condicional ou na volatilidade.
Silva, Gabriel Fagundes da
core
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