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 research employs the gradient descent learning (FIR. DM) approach as a learning
process in a nonlinear spectral model of maximum overlapping discrete wavelet transform …

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock
exchange (Tadawul) data patterns using the daily stock price indices data with 2026 …

Volatility in stock markets has been extensively studied in the applied finance literature. In
this paper, Artificial Neural Network models based on various back propagation algorithms …

We enhance the precision of predicting daily stock market price volatility using the maximum
overlapping discrete wavelet transform (MODWT) spectral model and two learning …

In this study, we proposed a new model to improve the accuracy of forecasting the stock
market volatility pattern. The hypothesized model was validated empirically using a data set …

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

Accurate forecasting of volatility from financial time series is paramount in financial decision
making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile …

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 examined the monthly volatility of Naira/Dollar exchange rates in Nigeria
between the periods of January, 1995 to January, 2016. Forecasting volatility remains to be …