Open AccessEstimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models
Author(s) -
G. R. Pasha,
Tahira Bano Qasim,
Muhammad Aslam
Publication year - 2007
Publication title -
the lahore journal of economics
Language(s) - English
Resource type - Journals
eISSN - 1811-5446
pISSN - 1811-5438
DOI - 10.35536/lje.2007.v12.i2.a6
Subject(s) - autoregressive conditional heteroskedasticity , volatility (finance) , econometrics , economics , stock exchange , finance
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.