Modeling Volatility in Nigeria Foreign Exchange Market Using GARCH-type Models

Abstract

In this study, the performance of GARCH-type model is considered in modelling Nigeria foreign exchange returns. The datasets consists of the foreign exchange of Nigeria naira for the periods before recession and during recession. It is observed that volatility is higher during recession than when there was no recession. Model selection criteria based on Hannan-Quinn Information Criterion (HQIC) shows that Gaussian process is least considered model to capture the variability in foreign exchange rate returns in Nigeria, but student’s  and Generalized Error distribution are more suitable, therefore forecast performance was used to access each of the Asymmetric models. The empirical analysis shows that GARCH (1, 1) and gjrGARCH (1, 1) with Student’s  error distribution and iGARCH(1, 1), sGARCH(1,1), and csGARCH (1,1) are the best fitted models. Fifty days out-of-sample forecast shows that csGARCH (1, 1) based on Generalized Error distribution is the best predictive model based on Mean Square Error (MSE), and sGARCH based on Mean Absolute Error (MAE) and Directional Absolute Error (DAE). The study recommends that future study should consider alternative error distributions with a view to realizing a more robust volatility forecasting model that could guarantee sound policy choices.

Keywords: Volatility, foreign exchange, GARCH-type models, Error Distributions