Results 91 to 100 of about 7,382 (224)

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

Comparing the accuracy of the model Meta heuristic and Econometric in forecasting of financial time series with long-term memory (Case Study, Stock Index of Cement Industry in Iran) [PDF]

تحقیقات مالی, 2011
Data with high frequency have a particular type of none stationary that is called fractional none stationary. This property causes the emergence of long-term memory in financial time series with high frequency. The existence of long-term memory in cement
Farnaz Barzinpour, Seyed Babak Ebrahimi, Seyed Mohammad Hasheminejad, Hamed Nasr Esfahani   +3 more
doaj  

Macro‐financial linkages in the high‐frequency domain: Economic fundamentals and the Covid‐induced uncertainty channel in US and UK financial markets

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, Menelaos Karanasos, Stavroula Yfanti   +2 more
wiley   +1 more source

"Realized Volatility Risk" [PDF]

In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in ...
David E. Allen, Marcel Scharth, Michael McAleer   +2 more
core   +3 more sources

Coupling travel characteristics identifying and deep learning for demand forecasting on car‐hailing tourists: A case study of Beijing, China

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, Xiaobing Liu, Yun Wang, Xuedong Yan   +3 more
wiley   +1 more source

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 1, January/February 2024.
A review of recent research on the application of deep learning models to price forecast of financial time series, with information on model architectures, applications, advantages and disadvantages, and directions for future research. Abstract Accurately predicting the prices of financial time series is essential and challenging for the financial ...
Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim   +2 more
wiley   +1 more source

MODEL PERGERAKAN HARGA MINYAK MENTAH BRENT MENGGUNAKAN PENDEKATAN TIME SERIES DENGAN EFEK LONG MEMORY

Jurnal Lebesgue
Oil price movements are highly volatile and tend to be influenced over extended periods, often displaying long memory effect. This study utilizes the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, a long memory model, to analyze ...
Eza Syafri Ramadhani, Dodi Devianto, Ferra Yanuar   +2 more
doaj   +1 more source

Aquila Optimizer‐Based Hybrid Predictive Model for Traffic Congestion in an IoT‐Enabled Smart City

International Journal of Intelligent Systems, Volume 2024, Issue 1, 2024.
Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi‐LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time‐series data prediction statistical model ARIMA (Autoregressive Integrated Moving ...
Ayushi Chahal, Preeti Gulia, Nasib Singh Gill, Nishat Sultana, Alexander Hošovský   +4 more
wiley   +1 more source

Modelling and Forecasting Noisy Realized Volatility [PDF]

Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even
Asai, M., McAleer, M.J., Medeiros, M.
core   +4 more sources

ARFIMA Reference Forecasts for Worldwide CO2 Emissions and the Need for Large and Frontloaded Decarbonization Policies

, 2020
We provide reference forecasts for worldwide CO2 emissions from fuel fossil combustion and cement production based on an ARFIMA approach. Our projections suggest a time path for emissions that is inconsistent with the general IPCC decarbonization goals ...
J. Belbute, A. Pereira
semanticscholar   +1 more source