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 …

One of the most used methods to forecast price volatility is the generalized autoregressive
conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using …

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 …

This study compares the forecast performance of volatilities between two types of hybrid
ANN and GARCH-type models. The findings show that EGARCH-ANN model performs …

GARCH family models are widely used in forecasting volatile time series data. In the current
study, the ability of GARCH (1, 1) model is improved in forecasting Malaysian gold, known …

Forecasting volatility is an essential step in many financial decision makings. GARCH family
of models has been extensively used in finance and economics, particularly for estimating …

In this paper the volatility forecasts of crude oil commodity price returns are analysed.
Various GARCH family models are used to forecast the volatility and the output in terms of …

Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been
successfully used for financial forecasting. This paper deals with the application of SVM in …

This paper builds on previous research and seeks to determine whether improvements can
be achieved in the forecasting of oil price volatility by using a hybrid model and …

In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast
is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico …