Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock
volatility forecasting provides business insight into the stock market, making it valuable …

Volatility prediction plays a vital role in financial data. The time series movements of stock
prices are commonly characterized as highly nonlinear and volatile. This study is aimed at …

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 …

Estimating and predicting volatility in time series is of great importance in different areas
where it is required to quantify risk based on variability and uncertainty. This work proposes …

Volatility is a measure of fluctuation in financial asset returns, practical measurement of risk,
and a key variable for calculating options prices. Accurate prediction of volatility is crucial to …

This study investigates the advantage of combining the forecasting abilities of multiple
generalized autoregressive conditional heteroscedasticity (GARCH)‐type models, such as …

Accurate forecasting of stock market volatility is an important issue in portfolio risk
management. In this paper, an ensemble system for stock market volatility is presented. It is …

Volatility forecasting is an important task for those associated with the financial markets, and
has occupied the attention of academics and practitioners over the last two decades. This …

This paper endeavors to enhance the prediction of volatility in financial markets by
developing a novel hybrid model that integrates generalized autoregressive conditional …

This research provides a time series forecasting model that is hybrid which combines Long
Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) …