Forecasting Financial Assets Volatility Using Integrated GARCH-Type Models: International Evidence

Samir Mabrouk^1,

¹Faculty of Management and Economic Sciences of Sousse, Sousse University, Tunisia

Cite this paper:
Samir Mabrouk. Forecasting Financial Assets Volatility Using Integrated GARCH-Type Models: International Evidence. Journal of Finance and Economics. 2016; 4(2):54-62. doi: 10.12691/jfe-4-2-3

Abstract

In this article we compare the forecasting ability of two symmetric integrated GARCH models (FIGARCH & HYGARCH) with an asymmetric model (FIAPARCH) based on a skewed Student distribution. Each model is used for forecasting the daily conditional variance of 10 financial assets, for a sample period of about 18 years. This exercise is done for seven stock indexes (Dow Jones, NASDAQ, S&P500, DAX30, FTSE100, CAC40 and Nikkei 225) and three exchange rates vis-a-vis the US dollar (the GBP- USD, YEN-USD and Euro-USD). Results indicate that the skewed Student AR (1) FIAPARCH (1.d.1) relatively outperforms the other models in out-of-sample forecasts for one, five and fifteen day forecast horizons. Results indicate also, no difference for the AR (1) FIGARCH (1.d.1) and AR (1) HYGARCH (1.d.1) models since they have the same forecasting ability.

Keywords:
forecasting volatility skewed Student distribution Long-range memory

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