Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models
HY Kim, CH Won - Expert Systems with Applications, 2018 - Elsevier
Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk
management, and hedging strategies. Therefore, accurate prediction of volatility is critical …
management, and hedging strategies. Therefore, accurate prediction of volatility is critical …
ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module
Forecasting a financial asset's price is important as one can lower the risk of investment
decision-making with accurate forecasts. Recently, the deep neural network is popularly …
decision-making with accurate forecasts. Recently, the deep neural network is popularly …
Gold volatility prediction using a CNN-LSTM approach
A Vidal, W Kristjanpoller - Expert Systems with Applications, 2020 - Elsevier
Prediction of volatility for different types of financial assets is one of the tasks of greater
mathematical complexity in time series prediction, mainly due to its noisy, non-stationary and …
mathematical complexity in time series prediction, mainly due to its noisy, non-stationary and …
A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction
Y Hu, J Ni, L Wen - Physica A: Statistical Mechanics and its Applications, 2020 - Elsevier
Forecasting the copper price volatility is an important yet challenging task. Given the
nonlinear and time-varying characteristics of numerous factors affecting the copper price, we …
nonlinear and time-varying characteristics of numerous factors affecting the copper price, we …
Forecasting stock price volatility: New evidence from the GARCH-MIDAS model
L Wang, F Ma, J Liu, L Yang - International Journal of Forecasting, 2020 - Elsevier
This paper introduces a combination of asymmetry and extreme volatility effects in order to
build superior extensions of the GARCH-MIDAS model for modeling and forecasting the …
build superior extensions of the GARCH-MIDAS model for modeling and forecasting the …
Forecasting oil price realized volatility using information channels from other asset classes
S Degiannakis, G Filis - Journal of International Money and Finance, 2017 - Elsevier
Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the
information flow, we claim that cross-market volatility transmission effects are synonymous to …
information flow, we claim that cross-market volatility transmission effects are synonymous to …
A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis
W Kristjanpoller, MC Minutolo - Expert Systems with Applications, 2018 - Elsevier
Measurement, prediction, and modeling of currency price volatility constitutes an important
area of research at both the national and corporate level. Countries attempt to understand …
area of research at both the national and corporate level. Countries attempt to understand …
A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting
W Zhang, Q Chen, J Yan, S Zhang, J Xu - Energy, 2021 - Elsevier
Accurate load forecasting is challenging due to the significant uncertainty of load demand.
Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning …
Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning …
Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation
SW Lee, HY Kim - expert systems with applications, 2020 - Elsevier
Forecasting stock market indexes is an important issue for market participants, because
even a small improvement in forecast accuracy may lead to better trading decisions than …
even a small improvement in forecast accuracy may lead to better trading decisions than …
Forecasting volatility of oil price using an artificial neural network-GARCH model
W Kristjanpoller, MC Minutolo - Expert Systems with Applications, 2016 - Elsevier
This paper builds on previous research and seeks to determine whether improvements can
be achieved in the forecasting of oil price volatility by using a hybrid model and …
be achieved in the forecasting of oil price volatility by using a hybrid model and …