The prediction of stock prices is a complex task due to the influence of various factors, high
noise, and nonlinearity. This paper focuses on addressing the challenges of low prediction …

In order to improve the forecasting accuracy of the volatilities of the markets, we propose the
hybrid models based on artificial neural networks with multi-hidden layers in this paper …

This research introduces a more reliable model for predicting market volatility. The model
incorporates Transformer and Multi-transformer layers with the GARCH and LSTM models …

In this paper, we incorporate a GARCH model into an artificial neural network (ANN) for
financial volatility modeling and estimate the parameters in Tensorflow. Our goal was to …

Stock prices are highly nonlinear and random. Traditional time-series methods such as
ARIMA and GARCH models are normally used. These models are effective only when the …

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model
for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear …

This paper examines the volatility risk in the KSA stock market (Tadawul), with a specific
focus on predicting volatility using the logarithm of the standard deviation of stock market …

This study develops a hybrid model that combines Autoregressive Integrated Moving
Average (ARIMA), Exponential GARCH (EGARCH) and Artificial Neural Network (ANN) to …

Volatility forecasting in the financial markets, along with the development of financial
models, is important in the areas of risk management and asset pricing, among others …

The prediction of stock price return volatilities is important for financial companies and
investors to help to measure and managing market risk and to support financial decision …