Volatility Estimation in the Dhaka Stock Exchange (DSE) returns by Garch Models

Abstract

This study aimed at understanding the volatility of Dhaka Stock Exchange (DSE). The daily and monthly average DSE General Index (DGEN), from the period January 1, 2002 to July 31, 2013 has been used. The study has been made by using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to estimate the presence of volatility. Though volatility is a common phenomenon in the capital market, the study recommends careful monitoring of volatility by the concerned authority if necessary. It is also recommended that activities of corporate insiders should be properly checked and information should become available for all of the interested investors and to ensure adequate supply of stock through active participation of the government and giant national and multinational companies and so forth.

Return and Volatility Spillover between Financial Market Participants of Dhaka Stock Exchange Using Asymmetric GARCH Methods

“…Other researchers, such as, [4] and [5] used GARCH framework to identify the best model to forecast volatility in Dhaka Stock Exchange. Mollik and Bepari [6] used autocorrelation structure and GARCH framework in the return series of DSE general and DSE 20 index and didn't find any asymmetric or differential impact of bad news and good news on the conditional variance of DSE return series.…”

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

“…Reference [3] and [31] observed Generalized Autoregressive Conditional Heteroskedasticity (GARCH) properties in the daily and monthly DSE returns where [2,3] and [14] used different GARCH framework to identify best model to forecast volatility. Different lag orders of ARCH and GARCH was used by [26] on four listed companies of DSE where GARCH (1,1) is found to be the best volatility model.…”

“…Reference [15] compared stock market volatility of CSE and DSE using closing price of four companies such as Aftab Automobiles, Bata Shoe Company, Beximco Pharmaceutical and Southeast Bank and DGEN from May, 2000 to April, 2014 using GARCH models where CSE is found more volatile. Reference [16] also used different GARCH specification and identified GARCH (1,3) as the best forecasting model based on Akaike Information Criteria (AIC) and Schwartz Information Criteria (SIC).…”

Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market

“…During the last decade or so, several studies have investigated a number of pertinent issues regarding the functioning of DSE, Bangladesh. Examples of such issues include non-normality, volatility clustering, presence of autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) effects in stock returns (Aziz & Uddin, 2014; Basher et al, 2007; Bose & Rahman, 2015; Siddikee & Begum, 2016), role of trading volume in reducing stock return volatility (Bose & Rahman, 2015), role of regulators and return volatility (Rahman & Golam Moazzem, 2011), impact of lock-in and circuit breaker measures in curbing return volatility (Basher et al, 2007), impact of circuit breaker measure in halting trade (Chowdhury & Masuduzzaman, 2010), effect of dividend policy on stock prices (Masum, 2014), day-of the week effects in stock returns (Bose & Rahman, 2015; Rahman, 2009), herding behavior among traders and seasonal influence (Ahsan & Sarkar, 2013; Bepari & Mollik, 2009), relationship between financial leverage and stock returns at firm level (Abdullah et al, 2015), stock market reaction to dividend announcement (Hossain et al, 2006; Rahman et al, 2012), market and informational inefficiency (Afzal & Hossain, 2011; Arefin & Rahman, 2011; Joarder et al, 2014; Mobarek et al, 2008), disparity in trading behavior of investors with respect to gender, age and education level (Arifuzzaman et al, 2012), prohibition of short-selling and market inefficiency (Sochi & Swidler, 2018), among others.…”