Forecasting Volatility of Stock Indices with ARCH Model

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

Islamic Calendar Anomalies: Evidence from Pakistan

“…Engle (1982) which was later modified into GARCH by Bollerslev (1986). Stock markets are associated with the element of uncertainty and due to such uncertain circumstances; investors prefer to forecast the performance of the market (Alam, Siddikee, & Masukujjaman, 2013;Akhtar & Khan, 2016 Iqbal and Dheeriya (1991) suggested that any abnormal returns in the market are because of the market risk and their distribution has a constant variance but Giaccoto and Ali (1982) contradicted such assumptions and argued that abnormal returns can be because of heteroscedasticity which is due to non-constant variance. Akgiray (1989) (Corhay & Rad, 1996).…”

Analysis of Investor Sentiment Impact in Indonesia Composite Stock Price Index Return Volatility

“…Descriptive statistical test was conducted on the data used in this study before classical assumptions tests such as stationarity, autocorrelation, multicolinearity, and heteroscedasticity test are done. Based on Alam et al (2013) descriptive statistical tests conducted are mean, median, maximum, minimum, and standard deviation.…”

Stock Market Volatility in Saudi Arabia: An Application of Univariate GARCH Model

“…Investors looking forward to reduce the uncertainty of their portfolios many reduce it by pulling down their exposure to stocks that are expected to face increased volatility of returns in the future or by adopting appropriate diversification strategy to hedge the risk of increased estimated future volatility. (Tse, 1991), Australia (Brailsford & Faff, 1996) and emerging markets like India (Karmakar, 2005), Jordon (Al-Raimony & El-Nader, 2012), Sudan (Ahmed & Suliman, 2011), Bangladesh (Alam et al, 2013), China (Fabozzi, 2004), Nigeria (Emenike, 2010), Egypt (Ezzat, 2012), etc. However, studies in the context of Saudi Arabia are almost non-existent.…”