Information Transmission Among Equity Markets: A Comparison Between ARDL and GARCH Model

This study compares the performance of autoregressive conditional heteroscedastic (ARCH) model and autoregressive distributed lag (ARDL) model in term of relationship detection. The daily, weekly, and monthly data are used from 2005 to 2019 to explore the dynamic linkages among KSE 100, S&P 500, Nasdaq 100, Dowjones 30, and DFMG indices. The results indicate that the ARDL and ARCH model have same power to detect the relationship among financial series. The results show that due high volatility in daily and weekly data the ARDL model is failed to capture ARCH effect. In case of monthly data, the performance of ARDL model is as good as GARCH model. It concluded that on monthly basis or less frequency data the ARDL model can be used as an alternative method to GARCH model for financial time series modeling

Tracing Return and Volatility Spillover Effect between Exchange Rate and Pakistan Stock Exchange Index

The volatility spillover is broadly measured as the transmission of variability from one financial market to other markets. This study explores the spillover effect between the newly emerged index of the Pakistan stock exchange (PSX) and exchange rate by using the newly proposed alternative methodology by Ghouse et al. (2019) and GARCH model. Furthermore, the index under study is more concise in its composition than other readily used indices. The study finds shreds of evidence for the bidirectional spillover effect between PSX and exchange rate, which will be helpful for central policy makers and markets players in designing effective policy frameworks. Keywords: ARDL; GARCH; spillover effec

Volatility Modelling and Dynamic Linkages between Pakistani and Leading Foreign Stock Markets: A Multivariate GARCH Analysis

It is essential for financial institutions and academicians to understand volatility spillover and financial market returns. However, previous studies examined the effects of direct spillover only and ignored those of the newly emerging stock markets. Therefore, this study attempts to estimate the time-varying volatility of Pakistani and leading foreign stock markets. It also tries to explore the direct and indirect volatility spillover effect between Pakistani and eight leading foreign stock markets. Daily data were used from nine international equity markets (KSE 100, NIKKEI 225, HIS, S&P 500, NASDAQ 100, DOW JONES, GADXI, FTSE 350 and DFMGI) for the period between 2005 and 2016. The univariate GARCH and GJR models were employed for analysing volatility, and the multivariate GARCH Diagonal BEKK model was used to explore direct and indirect volatility spillover effects. In order to analyse the volatility spillover effect during and after the global financial crisis period, the data were categorised into two periods: between 2005 and 2009 and between 2010 and 2016. The Chow break-point test was also employed to identify structural breaks in return series due to global financial crises. Direct and indirect spillover effects were found between KSE100, S&P 500, NASDAQ 100, DOW JONES and DFMGI. Keywords: Volatility, Spillover, Equity Market, Financial Crisis and GARC

Information Transmission Among Equity Markets: A Comparison Between ARDL and GARCH Model

This study compares the performance of autoregressive conditional heteroscedastic (ARCH) model and autoregressive distributed lag (ARDL) model in term of relationship detection. The daily, weekly, and monthly data are used from 2005 to 2019 to explore the dynamic linkages among KSE 100, S&P 500, Nasdaq 100, Dowjones 30, and DFMG indices. The results indicate that the ARDL and ARCH model have same power to detect the relationship among financial series. The results show that due high volatility in daily and weekly data the ARDL model is failed to capture ARCH effect. In case of monthly data, the performance of ARDL model is as good as GARCH model. It concluded that on monthly basis or less frequency data the ARDL model can be used as an alternative method to GARCH model for financial time series modeling

Determinants of Low Birth Weight a Cross Sectional Study: In Case of Pakistan

This study investigates the impact of different independent factors on birth weight of infant. The Demographic and Health Survey of Pakistan (PDHS) 2014 data are used for empirical analysis. Binomial Logit Regression is employed for analysis. The analysis revealed the significant relationship of birth weight with mother’s education; Mother’s working status, wealth index of family, gender of child, Place of residence, age of mother at first birth with birth weight of infant. The analysis also revealed that birth-interval, birth order and institutional place of delivery reduce the birth weight children. The male children are more likely to be suffering of low birth weight as compare to female children. As far as mother’s education level, her employment and wealth status increases the risk of low birth weight decreases. It has important policy implications that at least mother’s education should be part of the education policy of Pakistan. The proper medical facilities should be provided at rural areas to decrease the risk of low birth weight and child mortality as well. From the policy perspective the education on birth order and birth interval should be arranged for awareness of parents. For the long-run the socioeconomic status of the household expressed by wealth index is needed

Determinants of Low Birth Weight a Cross Sectional Study: In Case of Pakistan

This study investigates the impact of different independent factors on birth weight of infant. The Demographic and Health Survey of Pakistan (PDHS) 2014 data are used for empirical analysis. Binomial Logit Regression is employed for analysis. The analysis revealed the significant relationship of birth weight with mother’s education; Mother’s working status, wealth index of family, gender of child, Place of residence, age of mother at first birth with birth weight of infant. The analysis also revealed that birth-interval, birth order and institutional place of delivery reduce the birth weight children. The male children are more likely to be suffering of low birth weight as compare to female children. As far as mother’s education level, her employment and wealth status increases the risk of low birth weight decreases. It has important policy implications that at least mother’s education should be part of the education policy of Pakistan. The proper medical facilities should be provided at rural areas to decrease the risk of low birth weight and child mortality as well. From the policy perspective the education on birth order and birth interval should be arranged for awareness of parents. For the long-run the socioeconomic status of the household expressed by wealth index is needed

Time Varying Volatility Modeling of Pakistani and leading foreign stock markets

This study estimates the volatility of Pakistani and leading foreign stock markets. Daily data are used from nine international equity markets (KSE 100, NIKKEI 225, HIS, S&P 500, NASDAQ 100, DOW JONES, GADXI, FTSE 350 and DFMGI) for the period of Jan, 2005 to Nov, 2014. The whole data set is used for modeling of time varying volatility of stock markets. Univariate GARCH type models i.e. GARCH and GJR are employed for volatility modeling of Pakistani and leading foreign stock markets. The residual analysis also employed to check the validity of models. Our study brings important conclusions for financial institutions, portfolio managers, market players and academician to diagnose the nature and level of linkages between the financial markets

Time Varying Volatility Modeling of Pakistani and leading foreign stock markets

This study estimates the volatility of Pakistani and leading foreign stock markets. Daily data are used from nine international equity markets (KSE 100, NIKKEI 225, HIS, S&P 500, NASDAQ 100, DOW JONES, GADXI, FTSE 350 and DFMGI) for the period of Jan, 2005 to Nov, 2014. The whole data set is used for modeling of time varying volatility of stock markets. Univariate GARCH type models i.e. GARCH and GJR are employed for volatility modeling of Pakistani and leading foreign stock markets. The residual analysis also employed to check the validity of models. Our study brings important conclusions for financial institutions, portfolio managers, market players and academician to diagnose the nature and level of linkages between the financial markets

ARDL model as a remedy for spurious regression: problems, performance and prospectus

Spurious regression have performed a vital role in the construction of contemporary time series econometrics and have developed many tools employed in applied macroeconomics. The conventional Econometrics has limitations in the treatment of spurious regression in non-stationary time series. While reviewing a well-established study of Granger and Newbold (1974) we realized that the experiments constituted in this paper lacked Lag Dynamics thus leading to spurious regression. As a result of this paper, in conventional Econometrics, the Unit root and Cointegration analysis have become the only ways to circumvent the spurious regression. These procedures are also equally capricious because of some specification decisions like, choice of the deterministic part, structural breaks, autoregressive lag length choice and innovation process distribution. This study explores an alternative treatment for spurious regression. We concluded that it is the missing variable (lag values) that are the major cause of spurious regression therefore an alternative way to look at the problem of spurious regression takes us back to the missing variable which further leads to ARDL Model. The study mainly focus on Monte Carlo simulations. The results are providing justification, that ARDL model can be used as an alternative tool to avoid the spurious regression problem

ARDL model as a remedy for spurious regression: problems, performance and prospectus

Spurious regression have performed a vital role in the construction of contemporary time series econometrics and have developed many tools employed in applied macroeconomics. The conventional Econometrics has limitations in the treatment of spurious regression in non-stationary time series. While reviewing a well-established study of Granger and Newbold (1974) we realized that the experiments constituted in this paper lacked Lag Dynamics thus leading to spurious regression. As a result of this paper, in conventional Econometrics, the Unit root and Cointegration analysis have become the only ways to circumvent the spurious regression. These procedures are also equally capricious because of some specification decisions like, choice of the deterministic part, structural breaks, autoregressive lag length choice and innovation process distribution. This study explores an alternative treatment for spurious regression. We concluded that it is the missing variable (lag values) that are the major cause of spurious regression therefore an alternative way to look at the problem of spurious regression takes us back to the missing variable which further leads to ARDL Model. The study mainly focus on Monte Carlo simulations. The results are providing justification, that ARDL model can be used as an alternative tool to avoid the spurious regression problem