TITLE:
Implementation of the Estimating Functions Approach in Asset Returns Volatility Forecasting Using First Order Asymmetric GARCH Models
AUTHORS:
Timothy Ndonye Mutunga, Ali Salim Islam, Luke Akong’o Orawo
KEYWORDS:
Estimating Function, Asymmetric GARCH, Volatility, Mean Square Error, Mean Absolute Error
JOURNAL NAME:
Open Journal of Statistics,
Vol.5 No.5,
August
19,
2015
ABSTRACT: This paper implements the method of estimating functions (EF) in the modelling and forecasting of financial returns volatility. This estimation approach incorporates higher order moments which are common in most financial time series, into modelling, leading to a substantial gain of information and overall efficiency benefits. The two models considered in this paper provide a better in-sample-fit under the estimating functions approach relative to the traditional maximum likely-hood estimation (MLE) approach when fitted to empirical time series. On this ground, the EF approach is employed in the first order EGARCH and GJR-GARCH models to forecast the volatility of two market indices from the USA and Japanese stock markets. The loss functions, mean square error (MSE) and mean absolute error (MAE), have been utilized in evaluating the predictive ability of the EGARCH vis-à-vis the GJR-GARCH model.