The stock market is considered one of the most complicated financial systems, comprising
several components or inventories, whose prices vary substantially over time. Bursaries …

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

GARCH family models are the most widely-used methods for forecasting price volatility.
Given that this approach usually has extremely high forecast errors, continuous studies have …

Volatility is an important element for various financial instruments owing to its ability to
measure the risk and reward value of a given financial asset. Owing to its importance …

Volatility clustering is a crucial property that has a substantial impact on stock market
patterns. Nonetheless, developing robust models for accurately predicting future stock price …

Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence
used extensively for pricing and discriminating between different financial investments. As a …

This article explores a method that integrates deep learning with classical econometric
models to address the challenge of predicting volatility risk in financial markets. In view of …

Volatility plays a crucial role in financial markets and accurate prediction of the stock price
indices is of high interest. In multivariate time series, Dynamic Conditional Correlation (DCC) …

In this article, we analyze volatility forecasts associated with the price of gold, silver, and
copper, three of the most important metals in the world market. First, a group of GARCH …

For simulating intricate goalfunctions, neural networks are a technology that is employed in
artificial intelligence. The usage of artificial neural networks is becoming more …