), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the … GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold … methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and …

The purpose of this paper is to provide an insight into the modelling and forecasting of unknown events or shocks that … econometric forecasting has been recently confirmed by the pandemic and other events that have affected the world economy and …-ante predictions which fill the gap in the ex-ante forecasting literature. The study of previous events is relevant for research …

systematic exploration and forecasting of sovereign default risks. Multivariate statistical and stochastic process …-based sovereign default risk forecasting has a 50-year developmental history. This article describes a continuous, non …

can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which … series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in … relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models …

quite successful in forecasting monthly changes in commodity prices, but that success diminished in the period following …

forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets …

Different forecasting behaviors affect investors' trading decisions and lead to qualitatively different asset price … forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price … forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and …