Intraday Volatility Spillovers among European Financial Markets during COVID-19

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Shadow Economy in Pakistan: Its Size and Interaction with Official Economy

Shadow economy encompasses wide array of activities that influence the official economy and government policies, either directly or indirectly. In this paper we estimate the shadow economy of Pakistan using currency demand approach with two econometric approached, i.e. one using Auto Regressive Distributed Lag (ARDL) model and two with Engel Granger two step approach. Additionally, we use a variant of currency demand approach where along with tax variable we include unemployment rate and intensity of government control as indicator variables of shadow economy, for the first time in case of Pakistan. The average shadow economy of Pakistan estimated from 1973-2015 as percentage of GDP is 26.41, 25.29, and 26.11 from Models 1, 2, and 3 respectively. Furthermore, we analyzed interaction between the official and shadow sector using ARDL model. Our results show a significantly increasing shadow economy in Pakistan with positive impact on the official sector in long run while negative impact in the short run. This again is a novelty in our paper where we observe short and long run impacts separately along with dynamic simulations to show Pakistan’s GDP per Capita in the absence of shadow economy

Shadow Economy in Pakistan: Its Size and Interaction with Official Economy

Shadow economy encompasses wide array of activities that influence the official economy and government policies, either directly or indirectly. In this paper we estimate the shadow economy of Pakistan using currency demand approach with two econometric approached, i.e. one using Auto Regressive Distributed Lag (ARDL) model and two with Engel Granger two step approach. Additionally, we use a variant of currency demand approach where along with tax variable we include unemployment rate and intensity of government control as indicator variables of shadow economy, for the first time in case of Pakistan. The average shadow economy of Pakistan estimated from 1973-2015 as percentage of GDP is 26.41, 25.29, and 26.11 from Models 1, 2, and 3 respectively. Furthermore, we analyzed interaction between the official and shadow sector using ARDL model. Our results show a significantly increasing shadow economy in Pakistan with positive impact on the official sector in long run while negative impact in the short run. This again is a novelty in our paper where we observe short and long run impacts separately along with dynamic simulations to show Pakistan’s GDP per Capita in the absence of shadow economy

Shadow Economy in Pakistan: Its Size and Interaction with Official Economy

Shadow economy encompasses wide array of activities that influence the official economy and government policies, either directly or indirectly. In this paper we estimate the shadow economy of Pakistan using currency demand approach with two econometric approached, i.e. one using Auto Regressive Distributed Lag (ARDL) model and two with Engel Granger two step approach. Additionally, we use a variant of currency demand approach where along with tax variable we include unemployment rate and intensity of government control as indicator variables of shadow economy, for the first time in case of Pakistan. The average shadow economy of Pakistan estimated from 1973-2015 as percentage of GDP is 26.41, 25.29, and 26.11 from Models 1, 2, and 3 respectively. Furthermore, we analyzed interaction between the official and shadow sector using ARDL model. Our results show a significantly increasing shadow economy in Pakistan with positive impact on the official sector in long run while negative impact in the short run. This again is a novelty in our paper where we observe short and long run impacts separately along with dynamic simulations to show Pakistan’s GDP per Capita in the absence of shadow economy

Shadow Economy in Pakistan: Its Size and Interaction with Official Economy

Shadow economy encompasses wide array of activities that influence the official economy and government policies, either directly or indirectly. In this paper we estimate the shadow economy of Pakistan using currency demand approach with two econometric approached, i.e. one using Auto Regressive Distributed Lag (ARDL) model and two with Engel Granger two step approach. Additionally, we use a variant of currency demand approach where along with tax variable we include unemployment rate and intensity of government control as indicator variables of shadow economy, for the first time in case of Pakistan. The average shadow economy of Pakistan estimated from 1973-2015 as percentage of GDP is 26.41, 25.29, and 26.11 from Models 1, 2, and 3 respectively. Furthermore, we analyzed interaction between the official and shadow sector using ARDL model. Our results show a significantly increasing shadow economy in Pakistan with positive impact on the official sector in long run while negative impact in the short run. This again is a novelty in our paper where we observe short and long run impacts separately along with dynamic simulations to show Pakistan’s GDP per Capita in the absence of shadow economy

Impact of Mobile Remittances on the Performance of Banks in Pakistan: A Panel Data Analysis

The research aimed to explore the effect of a new technological development, Mobile Remittance, on the profitability of Pakistani banks. For this purpose five bank’s data have been chosen over the period of 5 years, i.e., from 2009-2013. Return on Assets is used as the proxy for measuring bank profitability. The empirical result of this study show that mobile remittances, off-balance sheet activities and capital adequacy ratio have a positive coefficient which indicates that these variables play an important role to increase banks profitability. The credit risk has a negative coefficient that shows that increase in the ratio of credit risk decreases the bank's performance

Intraday Volatility Spillovers among European Financial Markets during COVID-19

During crises, stock market volatility generally rises sharply, and as consequence, spillovers are identified across markets. This study estimates the volatility spillover among twelve European stock markets representing all four regions of Europe. The data consists of 10,990 intraday observations from 2 December 2019 to 29 May 2020. Using the methodology of Diebold and Yilmaz, we use static and rolling windows to characterize five-minute volatility spillovers. Our results show that 77.80% of intraday volatility forecast error variance in twelve European markets comes from spillovers. Furthermore, the highest gross directional volatility spillovers are found in Sweden and the Netherlands, while the minimum spillovers to other stock markets are observed in the stock markets of Poland and Ireland. However, German and Dutch markets transmit the highest net directional volatility spillovers. Splitting the whole sample in pre- and post-pandemic declaration (11 March 2020) we find more stable spillovers in the latter. The findings reveal important information about European stock market interdependence during COVID-19, which will be beneficial to both policy-makers and practitioners

Unveiling Market Connectedness: Dynamic Returns Spillovers in Asian Emerging Stock Markets

This study investigates the returns spillovers across the equity markets of Asian emerging economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, South Korea, Taiwan, and Thailand). To achieve this objective, we used two different spillover methodologies (DY 2012 and BK 2018). Moreover, this study used the daily closing prices of equity indices ranging from 5 January 2005 to 13 November 2021. The empirical findings revealed that the total spillover index using DY 2012, and the short-term frequency index using BK 2018, are close to each other, with values of 46.92% and 43.04%, respectively. However, the spillover index value is high, with a value of 56.25% in the long run. Furthermore, the results showed that the stock markets of South Korea and Taiwan are the major spillover transmitters in the Asian emerging markets. Also, the financial association among all emerging Asian equities is at its peak, subject to the mobility of cash flows across the global economies. The results of this study provide meaningful insight for policymakers and investors to implement an effective strategy to overcome the possible influence of any financial crisis in the future. Our paper provides a potential contribution to the financial literature by examining the transmission of spillovers across the Asian emerging stock markets. Furthermore, it provides in-depth information regarding stock market interdependence

COVID-19 Pandemic & Financial Market Volatility; Evidence from GARCH Models

Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future

The footprints of COVID-19 on Central Eastern European stock markets: an intraday analysis

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