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

Author

Listed:
  • G.R. Pasha

    () (Bahauddin Zakariya University, Multan, Pakistan.)

  • Tahira Qasim

    () (Bahauddin Zakariya University, Multan, Pakistan.)

  • Muhammad Aslam

    () (Bahauddin Zakariya University, Multan, Pakistan.)

Abstract

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.

Suggested Citation

  • G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
  • Handle: RePEc:lje:journl:v:12:y:2007:i:2:p:115-149
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    References listed on IDEAS

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    Cited by:

    1. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.

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