The main objective of this study is to assess the macroeconomic determinants of stock price variability in Pakistan. The quarterly data on macroeconomic variables (Gross Domestic Product, Foreign Direct Investment, Interest Rates, Exports, Money Supply and Unemployment Rate) and KSE-100 Index as proxy of stock price variation for the period of 1992:01 to 2012:04 is taken for the empirical investigation. Johansen co-integration test and VECM is used for this purpose. The analysis of this study specifies that the foreign direct investment, interest rates, export and unemployment rate have significant and negative impact on KSE-100 index, while money supply has found to be a significant and positive determinant of stock prices. On the other hand gross domestic product have a positive but insignificant impact on stock prices in Pakistan.

<abstract><p>In this paper, we propose a novel family of distributions called the <italic>odd Muth-G</italic> distributions by using Transformed-Transformer methodology and study their essential properties. The distinctive feature of the proposed family is that it can provide numerous special models with significant applications in reliability analysis. The density of the new model is expressible in terms of linear combinations of generalized exponentials, a useful feature to extract most properties of the proposed family. Some of the structural properties are derived in the form of explicit expressions such as quantile function, moments, probability weighted moments and entropy. The model parameters are estimated following the method of maximum likelihood principle. Weibull is selected as a baseline to propose an odd Muth-Weibull distribution with some useful properties. In order to confirm that our results converge with minimized mean squared error and biases, a simulation study has been performed. Additionally, a plan acceptance sampling design is proposed in which the lifetime of an item follows an odd Muth-Weibull model by taking median lifetime as a quality parameter. Two real-life data applications are presented to establish practical usefulness of the proposed model with conclusive evidence that the model has enough flexibility to fit a wide panel of lifetime data sets.</p></abstract>

In this paper, a new modified Kumaraswamy distribution is proposed, and some of its basic properties are presented, such as the mathematical expressions for the moments, probability weighted moments, order statistics, quantile function, reliability, and entropy measures. The parameter estimation is done via the maximum likelihood estimation method. In order to show the usefulness of the proposed model, some well-established actuarial measures such as value-at-risk, expected-shortfall, tail-value-at-risk, tail-variance, and tail-variance-premium are obtained. A simulation study is carried out to assess the performance of maximum likelihood estimates. The empirical analysis is carried out to show that our proposed model is better in performance as compared to other competitive models related to the extended Kumaraswamy model. Thus, insurance claim data and engineering related real-life data sets are considered to prove this claim.