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Forecasting Volatility of Stock Indices with ARCH Model

Author

Listed:
  • Md. Zahangir Alam
  • Md. Noman Siddikee
  • Md. Masukujjaman
Abstract
The main motive of this study is to investigate the use of ARCH model for forecasting volatility of the DSE20 and DSE general indices by using the daily data. GARCH, EGARCH, PARCH, and TARCH models are used as benchmark models for the study purpose. This study covers from December 1, 2001 to August 14, 2008 and from August 18, 2008 to September 10, 2011 as in-sample and out-of-sample set sets respectively. The study finds the past volatility of both the DSE20 and DSE general indices returns series are significantly, influenced current volatility. Based on in-sample statistical performance, both the ARCH and PARCH models are considered as the best performing model jointly for DSE20 index returns, whereas for DSE general index returns series, ARCH model outperforms other models. According to the out ¨C of- sample statistical performance, all models except GARCH and TARCH models are regarded as the best model jointly for DSE20 index returns series, while for DSE general index returns series, no model is nominated as the best model individually. Based on the in-sample trading performance, all models except GARCH are considered as the best model jointly for DSE20 index returns series, while ARCH model is selected as the best model for DSE general index returns series. A per outputs of out-of-sample trading performance, the EGARCH model is the best performing model for DSE20 index returns series, whereas the GARCH and ARCH models are considered as the best performing model jointly for DSE general index returns series.

Suggested Citation

  • Md. Zahangir Alam & Md. Noman Siddikee & Md. Masukujjaman, 2013. "Forecasting Volatility of Stock Indices with ARCH Model," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(2), pages 126-143, April.
    • Handle: RePEc:jfr:ijfr11:v:4:y:2013:i:2:p:126-143
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    References listed on IDEAS

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    2. Fatima Syed & Naimat U. Khan, 2017. "Islamic Calendar Anomalies: Evidence from Pakistan," Business & Economic Review, Institute of Management Sciences, Peshawar, Pakistan, vol. 9(3), pages 104-122, September.

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