Results 51 to 60 of about 6,621,438 (152)
Stock market volatility simulation with the LSTM neural network
Вестник Пермского университета: Серия ЭкономикаIntroduction. Stock market volatility simulation and forecast are relevant issues which could contribute into lower risks and higher revenues of the market transactions.
Dmitry Aleksandrovich Patlasov +1 more
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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
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
Identification in ARFIMA models [PDF]
, 2016 RESUMEN: Desde la introducción de los modelos fraccionalmente integrados ARFIMA para series de tiempo con memoria larga, ha surgido un gran interés en el estudio de sus propiedades y áreas de aplicación.
Castaño Vélez, Elkin Argemiro
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A comparative analysis of alternative univariate time series models in forecasting Turkish inflation
Journal of Business Economics and Management, 2012 This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period ...
A. Nazif Çatık, Mehmet Karaçuka
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Forecasting West Texas Intermediate Crude Oil Price: Stochastic Differential Approach [PDF]
مدلسازی اقتصادسنجی, 2018 Uncertainty in oil markets has led economic researchers to the use of stochastic processes. The purpose of this paper, is the use of stochastic differential models to predict the crude oil price of West Texas Intermediate (WTI) and compare the ...
ramin khochiani, younes nademi
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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
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
A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter [PDF]
We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the
Wolfgang Härdle, Wen-Jen Tsay
core
Is there Long Memory in Stock Markets, or Does it Depend on the Model, Period or Frequency?
Ensayos Revista de Economía, 2017 This paper analyses the existence of long memory in the major stock markets in the world, and if this is the case, whether it’s due to the type of econometric models used, the period of study or the frequency of data (intraday, daily, weekly, etc.)?
Héctor F. Salazar-Núñez +2 more
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