Dr. Md Abu Hasan

This paper examines the volatility of the Bangladesh stock market returns in response to the volatility of the macroeconomic variables employing monthly data of general index of Dhaka Stock Exchange (DSE) and four macroeconomic variables (Call Money Rate, Crude Oil Price, Exchange Rate and SENSEX of Bombay Stock Exchange) from January 2001 to December 2015. The results of GARCHS models reveal that the volatility of DSE return is significantly guided by the volatility of macroeconomic variables, such as, exchange rate and SENSEX. Specifically, volatility of the DSE is expected to 19% increase by 1% increase of exchange rate. Moreover, the volatility of the Bangladesh stock market returns is expected to dampen down by 2% with an increase in the volatility of Indian stock market of 1%. Thus, we can comment that adding exchange rate or stock returns of India in the GARCH model provides significant knowledge about the behaviour of the DSE volatility.

This paper examines the well-known stylized facts of stock returns volatiliy in the Dhaka Stock Exchange (DSE) utilizing daily all share price index return data for the period of 02 January 1993 to 27 January 2013. In addition, the study explores the adequate volatiliy model for DSE. The results of the estimated MA(1)-GARCH(1,1) model reveal that the stock returns of DSE capture volatility clustering and the volatility is moderately persistent. The estimated MA(1)-EGARCH(1,1) model displays that the effect of bad news on stock market volatility is greater than the effect induced by good news. Hence, it is concluded that return series of DSE shows evidence of three common stylized facts namely, volatility clustering, leptokurtosis and the leverage effect. In the end, this study discovers that MA(1)-GARCH(1,1) is the best model for modeling volatility of DSE stock returns in Bangladesh.

This paper investigates the nature of volatility characteristics of stock returns in the Bangladesh stock markets employing daily all share price index return data of Dhaka Stock Exchange (DSE) and Chittagong Stock Exchange (CSE) respectively. Furthermore, the study explores the adequate volatility model for the stock markets in Bangladesh. Results of the estimated MA(1)-GARCH(1,1) model for DSE and GARCH(1,1) model for CSE reveal that the stock markets of Bangladesh capture volatility clustering, while volatility is moderately persistent in DSE and highly persistent in CSE. Estimated MA(1)-EGARCH(1,1) model shows that effect of bad news on stock market volatility is greater than effect induced by good news in DSE, while EGARCH(1,1) model displays that volatility spill over mechanism is not asymmetric in CSE. Therefore, it is concluded that return series of DSE show evidence of three common events, namely volatility clustering, leptokurtosis and the leverage effect, while return series of CSE contains leptokurtosis, volatility clustering and long memory. Finally, this study explores that MA(1)-GARCH(1,1) is the best model for modeling volatility of Dhaka stock market returns, while GARCH models are inadequate for volatility modeling of CSE returns.

Measuring the efficiency of the stock market is an important research topic as there are various implications for investors. This paper investigates the weak form efficiency in the framework of the random walk hypothesis for the stock market in Bangladesh, employing both Non Parametric tests (Runs test and Phillips-Perron test) and Parametric tests (Autocorrelation test, Augmented Dickey-fuller test, and Variance Ratio test). The study uses daily return data for the three stock indices of Dhaka Stock Exchange such as DSI (

This paper examines the well-known stylized facts of stock returns volatiliy in the Dhaka Stock Exchange (DSE) utilizing daily all share price index return data for the period of 02 January 1993 to 27 January 2013. In addition, the study explores the adequate volatiliy model for DSE. The results of the estimated MA(1)-GARCH(1,1) model reveal that the stock returns of DSE capture volatility clustering and the volatility is moderately persistent. The estimated MA(1)-EGARCH(1,1) model displays that the effect of bad news on stock market volatility is greater than the effect induced by good news. Hence, it is concluded that return series of DSE shows evidence of three common stylized facts namely, volatility clustering, leptokurtosis and the leverage effect. In the end, this study discovers that MA(1)-GARCH(1,1) is the best model for modeling volatility of DSE stock returns in Bangladesh.

This study investigates semi-strong form of the Efficient Market Hypothesis (EMH) based on macroeconomic variable version of the Arbitrage Pricing Theory (APT) using monthly data of All Share Price Index (DSI) of Dhaka Stock Exchange (DSE) and five macroeconomic variables namely, Industrial Production Index (IPI), Broad Money Supply (M2), Crude Oil Price (OP), Exchange Rate (ER) and Index of Bombay Stock Exchange (SENSEX) from January 2001 to December 2012. The Johansen and Juselius multivariate cointegration tests reveal that IPI and OP have significant negative long run relationship with DSI, while M2, ER and SENSEX have significant positive long run relationship with DSI. The results of VECM indicate that there is long run causality running from the explanatory variables to DSI. The study also reveals that individually IPI and SENSEX are the leading indicators with respect to stock prices in Bangladesh in the short run. Moreover, stock price index of DSE is a leading indicator with respect to IPI and ER in the short run. Therefore, Bangladeshi stock market is motivated by macroeconomic factors, while global stock markets have some influence on it. Hence, it may be concluded that DSE is not efficient in the semi-strong form of EMH.

This study explores the relative effectiveness of monetary and fiscal policies on economic growth in Bangladesh
for the period from fiscal year 1974 to2015 employing cointegration and Vector Error Correction Model (VECM). We use
nominal GDP as a proxy for economic growth, while broad money supply (M2) and reserve money (RM) as proxies for
monetary policy. Total government revenue (TR) and total government expenditure (TE) are used as proxies for fiscal policy.
The Johansen cointegration tests reveal that monetary policy (M2 and RM) has a greater long run positive impact on economic
growth over fiscal policy in Bangladesh. The results of VECM show that there is a weak long run causality running from
monetary and fiscal policies to economic growth. VECM also finds that GDP, M2 and TR play a part to adjust any
disequilibrium, while TR picks up the disequilibrium rapidly and guides the variables of the system back to equilibrium.
VECM Granger causality/block exogeneity Wald test results show that M2 is the leading indicator with respect to economic
growth in Bangladesh in the short run. Moreover, economic growth is a leading indicator with respect to fiscal policy in the
short run. Thus, we conclude that monetary policy is the more effective channel than fiscal policy to promote economic growth
in the short run and long run in Bangladesh

This paper examines the volatility of the Bangladesh stock market returns in response to the volatility of the macroeconomic variables employing monthly data of general index of Dhaka Stock Exchange (DSE) and four macroeconomic variables (Call Money Rate, Crude Oil Price, Exchange Rate and SENSEX of Bombay Stock Exchange) from January 2001 to December 2015. The results of GARCH-S models reveal that the volatility of DSE return is significantly guided by the volatility of macroeconomic variables, such as, exchange rate and SENSEX. Specifically, volatility of the DSE is expected to 19% increase by 1% increase of exchange rate. Moreover, the volatility of the Bangladesh stock market returns is expected to dampen down by 2% with an increase in the volatility of Indian stock market of 1%. Thus, we can comment that adding exchange rate or stock returns of India in the GARCH model provides significant knowledge about the behaviour of the DSE volatility.

This study investigates the relationship between foreign trade and economic growth using annual data of real GDP (proxied for economic growth), real exports and imports for the period of 1979 to 2014 in Bangladesh. The study operates the bounds testing cointegration procedure and ARDL-error correction model to examine the short-run and long-run relationships among the variables. The ARDL bounds test approach reveals that the long run cointegrations exist among the variables when GDP is dependent on export and import, and export is dependent on GDP and import. The long run coefficients of ARDL results show that export is positively and import is negatively related with GDP at 1% level of significance. Furthermore, GDP and import have a positive and highly significant long-run effect on export in Bangladesh. The short run dynamics along with the error correction term (ECT) results indicate that the coefficients of error correction terms of the two models are negative and highly significant which suggesting that the long run causalities are also directing from exports and imports to GDP, and GDP and imports to exports. The error correction terms imply that GDP requires about ten years to converge to equilibrium after being shocked, while export requires only about nine months. All of the short run coefficients of the models are significant and consistent with Original Research Article

This study investigates the weak form efficiency of Efficient Market Hypothesis (EMH) employing Autocorrelation test, Runs test and Unit Root tests, and the nature of volatility characteristics of stock returns applying GARCH family models in Bangladesh stock market using daily all share price index return data of Dhaka Stock Exchange (DSE) from 02 January 1993 to 27 January 2013. This study also examines the semi-strong form of the EMH of DSE based on macroeconomic variable version of the Arbitrage Pricing Theory (APT) applying Cointegration tests, Vector Error Correction Model (VECM) and Granger causality tests, and the volatility of the DSE returns in response to the volatility of the macroeconomic variables employing GARCH family models using monthly data from January 2001 to December 2012. In addition, the short run and long run relationships between macroeconomic variables and aggregate stock prices in Bangladesh have also been determined. Employing both nonparametric tests (Runs test and Phillips-Perron test) and parametric tests (Autocorrelation test and Augmented Dickey-fuller test), this study finds that the DSE of Bangladesh is not weak form efficient. Taking the outcome of VAR models into account, it is found that all selected macroeconomic variables do significantly explain the stock prices of the Bangladesh stock market. As a consequence, it may be concluded that the Bangladesh stock market is not efficient in the semi-strong form of EMH. Results of the estimated MA(1)-GARCH(1,1) and MA(1)-EGARCH(1,1) models reveal that stock market returns of Bangladesh exhibit leptokurtosis, volatility clustering and leverage effect. Results of six GARCH-S models indicate that the volatility of DSE return is significantly influenced by the volatility of macroeconomic variables, such as, exchange rate, broad money supply and stock returns of India. Highlights * The role of economic factors and past stock price patterns on the stock prices has been subjected to economic research all over the world. Although, study like efficiency and volatility of the stock market in Bangladesh in response of macroeconomic variables has basically been ignored. * This study takes an attempt to uncover the issue of efficiency and volatility of the stock market in Bangladesh by employing both univariate models using daily past share prices data and multivariate models employing monthly data of macroeconomic variables and stock index. * This research concludes that the stock market of Bangladesh is not efficient in weak and semi-strong form of EMH. Stock market returns of Bangladesh exhibit well-known stylized facts. The volatility of DSE return is significantly influenced by the volatility of macroeconomic variables.