Results 231 to 240 of about 40,912 (303)
Some of the next articles are maybe not open access.
Finance Research Letters, 2020
This paper investigates the impact of relative economic policy uncertainty between China and the United States (the Sino-US EPU ratio) on the Chinese exchange rate volatility by employing a GARCH-MIDAS model.
Zhongbao Zhou, Ximei Zeng
exaly +2 more sources
This paper investigates the impact of relative economic policy uncertainty between China and the United States (the Sino-US EPU ratio) on the Chinese exchange rate volatility by employing a GARCH-MIDAS model.
Zhongbao Zhou, Ximei Zeng
exaly +2 more sources
International Review of Economics and Finance, 2019
GARCH-type models are frequently used to forecast crude oil price volatility, and whether we should consider multiple regimes for the GARCH-type models is of great significance for the forecasting work but does not have a final conclusion yet.
Yue-Jun Zhang, Ting Yao, Ling-Yun He
exaly +2 more sources
GARCH-type models are frequently used to forecast crude oil price volatility, and whether we should consider multiple regimes for the GARCH-type models is of great significance for the forecasting work but does not have a final conclusion yet.
Yue-Jun Zhang, Ting Yao, Ling-Yun He
exaly +2 more sources
A hybrid model for carbon price forecasting using GARCH and long short-term memory network
Applied Energy, 2021The reform of the EU ETS markets in 2017 has induced new carbon price forecasting challenges. This study proposes a novel decomposition-ensemble paradigm VMD-GARCH/LSTM-LSTM model to better adapt to the current fast-rising and volatile carbon price ...
Yumeng Huang +3 more
semanticscholar +1 more source
Multivariate Time Series Forecasting With GARCH Models on Graphs
IEEE Transactions on Signal and Information Processing over Networks, 2023Data that house topological information is manifested as relationships between multiple variables via a graph formulation. Various methods have been developed for analyzing time series on the nodes of graphs but research works on graph signals with ...
Junping Hong +3 more
semanticscholar +1 more source
Journal of Econometrics, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stefan Lundbergh, Timo Teräsvirta
openaire +2 more sources
Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach
Resources policy, 2022This study proposes a new hybrid model that combines LSTM and BiLSTM neural networks with GARCH type model forecasts using an ensemble approach to forecast volatility for one-day ahead 95% and 99% Value-at-Risk (VaR) estimates using the Parametric (PAR ...
K. Kakade, I. Jain, A. Mishra
semanticscholar +1 more source
The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach
Physica A: Statistical Mechanics and its Applications, 2021The purpose of this paper is to investigate the impact of economic policy uncertainty on stock volatility forecast. We apply the GARCH–MIDAS model which can directly incorporate low-frequency economic policy uncertainty index and high-frequency stock ...
Xiaoling Yu, Yirong Huang
semanticscholar +1 more source
On the Continuous Limit of GARCH [PDF]
SSRN Electronic Journal, 2005 GARCH processes constitute the major area of time series variance analysis, hence the limit of these processes is of considerable interest for continuous time volatility modelling. The continuous time limit of the GARCH(1,1) model is fundamental for limits of other GARCH processes, yet it has been the point of much debate between econometricians.
Carol Alexandra, Emese Lazar
openaire +1 more source
Stacking hybrid GARCH models for forecasting Bitcoin volatility
Expert systems with applications, 2021Machine learning techniques have been used frequently for volatility forecasting. However, previous studies have built these hybrid models in a form of a first-order GARCH(1,1) process by following general use for GARCH models.
Serkan Aras
semanticscholar +1 more source