Results 81 to 90 of about 4,760 (217)
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
doaj +1 more source
Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models [PDF]
We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory ...
Kaizoji, Taisei, Lux, Thomas
core
Labor market forecasting in unprecedented times: A machine learning approach
Bulletin of Economic Research, Volume 76, Issue 4, Page 893-915, October 2024.Abstract The COVID‐19 pandemic ushered in unprecedented social and economic conditions, alongside unexpected policy responses, challenging the effectiveness of traditional labor market forecasting approaches. This article presents a novel approach that integrates macroeconomic variables, traditional labor market metrics, and Google search data to ...
Johanna M. Orozco‐Castañeda +2 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
Wen-Jen Tsay, Wolfgang Härdle
core
Local Whittle estimation with (quasi‐)analytic wavelets
Journal of Time Series Analysis, Volume 45, Issue 3, Page 421-443, May 2024.In the general setting of long‐memory multivariate time series, the long‐memory characteristics are defined by two components. The long‐memory parameters describe the autocorrelation of each time series. And the long‐run covariance measures the coupling between time series, with general phase parameters.
Sophie Achard, Irène Gannaz
wiley +1 more source
The Use of Weather Variables in the Modeling of Demand for Electricity in One of the Regions in the Southern Poland [PDF]
The main objective of the paper is the verification of usefulness of the ARFIMA-FIGARCH class models in the description of tendencies in the energy consumption in a selected region of the southern Poland taking into consideration weather variables ...
Aneta Wlodarczyk, Marcin Zawada
core
Network traffic prediction based on ARFIMA model
CoRR, 2013 ARFIMA is a time series forecasting model, which is an improved ARMA model, the ARFIMA model proposed in this article is demonstrated and deduced in detail. combined with network traffic of CERNET backbone and the ARFIMA model,the result shows that,compare to the ARMA model, the prediction efficiency and accuracy has increased significantly, and not ...
Dingding Zhou +2 more
openaire +2 more sources
International Journal of Finance &Economics, Volume 29, Issue 2, Page 1581-1608, April 2024.Abstract This article contributes to our understanding of the macro‐financial linkages in the high‐frequency domain during the recent health crisis. Building on the extant literature that mainly uses monthly or quarterly macro proxies, we examine the daily economic impact on intra‐daily financial volatility by applying the macro‐augmented HEAVY model ...
Guglielmo Maria Caporale +2 more
wiley +1 more source
IET Intelligent Transport Systems, Volume 18, Issue 4, Page 691-708, April 2024.Based on multi‐source data, this study couples the travel characteristics identifying by introducing a concept of service dependency degree and a Bayesian optimization–long short time memory–convolutional neural network method to conduct the multi‐task online car‐hailing demand prediction. This method is applied to the main scenic spots in Beijing, and
Zile Liu +3 more
wiley +1 more source
پژوهشهای ریاضی, 2020 Introduction The data obtained from observing a phenomenon over time is very common. One of the most popular models in time series and signal processing is the Autoregressive moving average model (ARMA).
Mahmod Afshari +2 more
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