The purpose of this paper is to examine the unit root properties of eleven Pakistani macroeconomic series using annual data.

Abstract. The objective of this research is to measure and examine volatilities between important emerging and developed stock markets and to ascertain a relationship between volatilities and stock returns. This research paper also analyses the Mean reversion phenomenon in emerging and developed stock markets. For this purpose, seven emerging markets and five developed markets were considered. Descriptive statistics showed that the emerging markets have higher returns with the higher risk-return trade-off. In contrast, developed markets have low annual returns with a low risk-return trade-off. Correlation analysis indicated the significant positive correlation among the developed markets, but emerging and developed markets have shown relatively insignificant correlation. Results of ARCH and GARCH revealed that the value of likelihood statistics ratio is large, that entails the GARCH (1,1) model is a lucrative depiction of daily return pattern, that effectively and efficiently capturing the orderly reliance of volatility. The findings of the study showed that the estimate 'β' coefficients given in conditional variance equation are significantly higher than the 'α' , this state of affair entails that bigger market surprises tempt comparatively small revision in future volatility. Lastly, the diligence of the conditional variance estimated by α + β is significant and proximate to integrated GARCH (1,1) model, thus, this indicates, the existing evidence is also pertinent in order to forecast the future volatility. The results signified that the sum of GARCH (1,1) coefficients for all the equity returns' is less than 1 that is an important condition for mean reversion, as the sum gets closer to 1, hence the Mean reversion process gets slower for all the emerging and developed stock markets.

The objective of this research was to forecast the tax revenue of Pakistan for the fiscal year 2016-17 using three different time series techniques and also to analyse the impact of indirect taxes on the working class. The study further analysed the efficiency of three different time series models such as the Autoregressive model (A.R. with seasonal dummies), Autoregressive Integrated Moving Average model (A.R.I.M.A.), and the Vector Autoregression (V.A.R.) model. In any economy, tax analysis and forecasting of revenues is of paramount importance to ensure the economic and fiscal policies. This study is important to identify significant variables affecting tax revenue specifically in Pakistan. The data used for this paper was from July 1985 to December 2016 (monthly) and focused on forecasting for 2017. For the forecasting of total tax revenue, we used components of tax revenues such as direct tax, sales tax, federal excise duty and customs duties. The results of this study revealed that among these models the A.R.I.M.A. model gives better-forecasted values for the total tax revenues of Pakistan. The results further demonstrated that major tax revenue is generated by indirect taxes, which cause more inflation that directly hits the working class of Pakistan.

This research is an attempt to framework the applied strides to evaluate the long run relationship among commonly used inflation proxies induces such as, wholesale price index (WPI) and consumer price index (CPI), and crude oil price (COP) with KSE100 index returns. In this research we used monthly data for the time period from July 1995 to June 2016, and thus, in this way total 252 observations have been considered. Time series have been made stationary by applying ADF and PP tests at first difference. Johansen multivariate conintegration approach was used to test the long-term association amongst the considered macroeconomic variables. The results indicated that CPI and COP significantly affect KSE100 index returns that indicated CPI along with COP have foreseen power to impact KSE100 index. In contrary, the results of WPI and COP do not have long run relationship with KSE100 index in case of Pakistani economy. Results of variance decomposition exhibited that the index of LKSE100 was realistically rarer exogenous in connection to distinctive factors, as around 92.31% of its variation was explained due to its own specific shocks. It is concluded that CPI and COP can impact the KSE100 index returns. It is confirmed by the results of impulse response function that there is a positive and long run relationship between KSE100 returns and consumer price index (proxy of inflation) and international crude oil prices.

This paper analyzes Pakistan's monetary policy transmission mechanism by considering these channels: Interest rate Channel, Credit Channel, and Risk channel. In this study, an innovative channel, Risk Channel, is introduced to measure its impact on the monetary policy transmission mechanism by covering the annual time data from 1995 to 2020for Pakistan. This paper aims to examine the long-run and the short-run relationship between foreign debt, bank capital, and monetary policy transmission mechanism. To fulfill this objective, we intended to use Autoregressive Distributed Lag (ARDL) model to investigate long-run and short-run relations. As per the result, the risk channel represents that it is not following the cointegration benchmark. The coefficient is negative, but the probability is more significant than 0.05, which is statistically insignificant; therefore, there is no long-run relationship between the model variables. The interest rate channel represents that it ensures the benchmark of cointegration as the coefficient is negative, but the probability is less than 0.05, which is statistically significant; therefore, there is a long-run relationship between variables for the model. The credit channel represents that it ensures the cointegration benchmark as the coefficient is negative and statistically significant at 90%; therefore, there is a long-run relationship between variables for the model. However, the study concluded that risk channel has short-term relation and interest rate& credit channels have short-term and long-term relationships.