An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume

Abstract

In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatility series were estimated using the Exponential Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. The simulation results show that trading volume helps improving the forecasting accuracy of BPNN in Morocco but not in Saudi Arabia. As a result, volume represents valuable information flow to be used in the modeling and prediction of volatility in Morocco. In addition, it is found that BPNN overpredicts volatility during high volatile periods. This finding is important in financial applications such as asset allocation and derivatives pricing.

Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

“…Meanwhile, in the ANN-BP network, there are linear functions and sigmoid functions only. The combination of activation functions in the hidden layer and the output layer has been widely performed such as the tansig function on the hidden layer and purelin on the output layer [15,16], and logsig function on every layer [17][18][19].…”

Emerging Markets Reward Risk

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