Search

Outliers in time-series data are crucial in model estimation and forecasting. Understanding the importance of false detection of outliers in various autoregressive processes, the present study aims to evaluate test statistics' performance by utilizing multiple models through robust estimation of errors. In this study, numerous data-generating techniques have been employed via simulation at different values of the estimates, location, and size of an outlier, sample sizes, and classical cutoff to access the power of the test statistics for false detection of outlier type. The findings of the simulation reveal that the location and size of outliers, parameter values, and, to some extent, the size of the series influence the behavior of test statistics in detecting the type of outliers. Overall, the estimation method of residual standard deviation influences the sampling behavior of the test statistics. Outliers also affect the forecasting performance in the model. All results are also validated empirically. [ABSTRACT FROM AUTHOR]

This study evaluates the relevancy of economic globalization, financial development, and clean energy, in strengthening the environmental sustainability of the next 11 economies over a time period pertaining to 1995–2018. In order to achieve the objective of this study, the advanced panel estimation techniques of unit root testing, and the cointegration analysis have been applied due to the presence of the cross-sectional-dependence and heterogeneity of the slope parameters in the panel data. The long-run output coefficients have been estimated through the Cross-Sectional Autoregressive Distributive Lag Model (CS-ARDL). Moreover, the causality test for a heterogeneous panel has also been employed in order to determine the causal relationships among the variables that are under study. Our empirical findings of these tests indicate that financial development and economic globalization tend to contribute to the deterioration of environmental quality, but clean energy is productive for its improvement. The bi-directional causal relationship is recognized to exist between CO2 emission and all the variables. Based on these findings, the study recommends adopting economic growth policies that are aligned with the defined environmental regulations, thus promoting the use of more clean energy resources. These include resources such as renewable energy and incorporating the environmental welfare goals into financial development plans in N11 economies. [ABSTRACT FROM AUTHOR]

By aiming to understand the importance of the ongoing effects of COVID-19 on several economies, this study attempts to highlight the impact of COVID-19 confirmed cases and deaths occurring in the most affected countries of the world such as China and the USA, especially in the context of their respective currencies. For this purpose, the daily data from January 22, 2020, till May 7, 2021, has been taken into account, and then has been analyzed to examine the linear relationship between the currency and proxies of the COVID-19 pandemic. Moreover, the (autoregressive distributed lag) ARDL model has been employed in order to fulfil the research objective with numerous statistical diagnostic measures, so as to validate the model. The results indicate that the exchange rate is affected negatively due to the effect of COVID-19 cases and deaths in particular countries. Moreover, the effect of the pandemic is not limited to the short run only, but also in the long run, as it is hurting the financial backbone of China and the USA. [ABSTRACT FROM AUTHOR]

The novel Covid-19 virus has changed the dynamics of 'flight to safety' investment for various economies. Thus, the hedging ability of the stocks must be revisited in the scenario of this pandemic. For this purpose, specifically understanding the importance of a semi-strong form of an efficient market hypothesis is important. This was to observe the speed at which the markets react to the news announcement, and how rapidly they absorb new information to regain thier position in the market. Hence, this study conducts an event study analysis on Pakistan's emerging market to detect the financial and non-financial stock price reactions towards the lockdown announcement, following the spread of Covid-19 in Pakistan. The daily data on the KSE-100 index for thirty different industries, comprising of ninety firms, spanning from December 12, 2019, till June 7, 2020, was collected and analyzed. The abnormal returns were recorded to be at around 21 [−10. +10] and 41 days [−20, +20] event window, around the day of the lockdown announcement. These abnormal returns were obtained through the market model regression. The data collected implied that most of the industries were stable and behaved well before the event day, while the affected sectors recovered fairly quickly. Therefore, it has been affirmed that Pakistan's equity portfolios are informationally efficient, and can benefit the investors during a pandemic. [ABSTRACT FROM AUTHOR]

The environmental problem has become a worldwide issue that must be frequently examined. Thus, this study investigates the relation of tourism, natural resources, globalization, and foreign direct investment on the ecological footprint of the USA, the UK, Pakistan, China, and India by using annual data from 1995 to 2020. The study applied Zivot and Andrews (1992) test to demonstrate the stationarity properties of the series, and an autoregressive distributed lag (ARDL) approach was employed to estimate the long-run and short-run dynamics. The long-run findings disclose that tourism has a significantly positive effect on China and Pakistan, but a significantly negative impact on India. The outcomes further suggest that foreign direct investment increases environmental degradation in India and China in both long and short periods, but contributes to improving the UK's ecosystem. Besides, globalization in China tends to save its environment. Natural resources in the long run also harm the ecosystems of Pakistan, China, and the USA. The error correction term is negatively significant for all countries. The reliability of the model is investigated through diagnostic tests. The selected model is stable as the critical value of CUSUM and CUSUMSQ lie within the 5% significance level. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the bank specific determinants related to the performance of public and private sector banks in Pakistan. Using strongly balanced panel yearly data from 2010 to 2017, Pooled OLS, fixed effect, Random effect and Random Effect Mundlak Transformation (REMT) have been utilized to provide the empirical evidences in credit risk management in Pakistan. The identification of suitable explanatory variable that explains the banking profitability wisely is made possible by using the panel data techniques. In this study, impact of bank specific variables are: Return On Assets, Capital Ratio, Credit Risk, Credit deposit ratio, Liquidity Ratio, Interest expended to interest earned, bank size and ownership on the profitability of banks in Pakistan has been assessed using four different panel data techniques. Out of the four estimation strategies Random Effect with Mundlak (1978) transformation raises the overall variation of the baseline model to 63% that is explained by banking profitability. Ignoring the time-invariant characteristics in the model, credit deposit ratio and interest expanded to interest earned possess negative relationship with return on assets of banks. Size of the bank is positive and significant when with-in and between banks information is augmented in Radom effect method of estimation. However the size of banks may not affect the banking profitability by allowing correlation between unobservable heterogeneity using Random Effect with Mundlak (1978) transformation. [ABSTRACT FROM AUTHOR]