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Li, Y. (2013) GARCH Models for Forecasting Volatilities of Three Major Stock Indicies Using Both Frequentist and Bayesian Approach. Masters Thesis, Ball State University, Muncie, IN.

has been cited by the following article:

• 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.