The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized … autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy … for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model …

forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation …

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 …

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 …

forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price … to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks. …

the current monetary policy framework and the challenges in transitioning towards FFIT. Given that forecasting is central … to FFIT, we develop a practical model-based forecasting and policy analysis system (FPAS) to support transition to FFIT …