This study aims to introduce an ideal model for forecasting crude oil price volatility. For this purpose, the ‘predictability’ hypothesis was tested using the variance ratio test, BDS test and the chaos analysis. Structural analyses were also carried out to identify possible nonlinear...

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and student-t innovations’ distributions. For these analyses, we consider...

This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed...

This research investigates the presence of structural breaks in the indices of the Egyptian stock market using the Bai-Perron strcutural breaks test. The indices used are the EGX 30, the EGX 70, the EGX 100, and the EGX 20. The presence of long memory is then investigated using the GPH test and...

This study explores the volatility models and evaluates the quality of one-step ahead forecasts of volatility constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH performs relatively best in term of MSPE, followed by...

This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics...

The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model) and a regressive (Auto Regressive Fractionally Integrated Moving Average model which...

The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and...

The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and...

Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural...