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 – 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.
Highlights
Forecasting of the stock exchange index is a motivating and tricky issue for both for investors and academics
The variance equation describes that the RESID(-1)^2, RESID(-1)^2*(RESID(-1)
The outcomes of the GARCH model on the selected stock indices returns demonstrate that the RESID(-1)^2 term is statistically significant which imply that the volatility of risk is influenced by past square residual terms
Summary
Forecasting of the stock exchange index is a motivating and tricky issue for both for investors and academics. The prices of financial securities, which are traded in the financial markets as well as interest rate and foreign exchange rates, are horizontal to constant inconsistency. For this type of changeability, their returns over the various periods of time are notably volatile and complicated to forecast. It is broadly consented that though returns of financial securities prices are more or less unpredictable on daily as well as monthly basis, return volatility is forecastable, phenomenon along with vital inference for financial economics and risk management (Torben et al 2009). Index trading vehicles give an effectual way for the investors for hedging against prospective market risks as well as they generate new return making opportunities for market arbitragers and speculators.
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