Forecasting volatility in GARCH models with additive outliers

In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns. The exponential GARCH and threshold GARCH models can be used to capture asymmetric volatility, called leverage effect. The aim of this research is to determine the best model between exponential GARCH and threshold GARCH models, and to know the results of forecasting volatility the LQ-45 stock index using the best model. The research showed that the best model to predicting volatility is EGARCH(2,1), because it has the smallest AIC value compared to other models. Then forecasting volatility of the LQ-45 stock index using EGARCH(2,1) showed that volatility increase from the first period until fourteenth period, this means that it has high volatility.

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Additive outliers, GARCH and forecasting volatility

International Journal of Forecasting ◽

10.1016/s0169-2070(98)00053-3 ◽

1999 ◽

Vol 15 (1) ◽

pp. 1-9 ◽

Cited By ~ 124

Author(s):

Philip Hans Franses ◽

Hendrik Ghijsels

Keyword(s):

Additive Outliers ◽

Forecasting Volatility

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Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

THE LAHORE JOURNAL OF ECONOMICS ◽

10.35536/lje.2007.v12.i2.a6 ◽

2007 ◽

Vol 12 (2) ◽

pp. 115-149

Author(s):

G.R. Pasha ◽

Tahira Qasim ◽

Muhammad Aslam

Keyword(s):

Time Series ◽

Stock Exchange ◽

Financial Time Series ◽

Garch Models ◽

Financial Time ◽

Daily Data ◽

Asymmetric Garch ◽

Gjr Model ◽

Forecasting Volatility ◽

Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

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The Forecasting Ability of GARCH Models for the 2003–07 Crisis: Evidence from S&P500 Index Volatility

Lahore Journal of Business ◽

10.35536/ljb.2012.v1.i1.a3 ◽

2012 ◽

Vol 1 (1) ◽

pp. 37-58

Author(s):

Mahreen Mahmud

Keyword(s):

Financial Crisis ◽

Realized Volatility ◽

Stock Index ◽

Garch Models ◽

Crisis Period ◽

Forecasting Volatility ◽

Stock Index Returns

This article studies the ability of the GARCH family of models to accurately forecast the volatility of S&P500 stock index returns across the financial crisis that affected markets in 2003–07. We find the GJR-GARCH (1,1) model to be superior in its ability to forecast the volatility of the initial crisis period (2003– 06) compared to its realized volatility, which acts as a proxy for the actual. This model is then extended to make forecasts for the crisis period. We conclude that the model’s ability to forecast volatility across the crisis is not substantially affected, thus supporting the use of the GARCH family of models in forecasting volatility.

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