Role of Islamic Banking during COVID-19 on Political and Financial Events: Application of Impulse Indicator Saturation

This paper attempts to detect the unavoidable impacts of COVID-19 on geopolitical and financial events related to Islamic banking and the finance sector in Pakistan. It considers only those major events that triggered imbalances in the equity prices of selected Islamic banks. Employed here is the GARCH model, used to predict the volatility series using daily data from January 2007 to July 2020. The Impulse Indicator Saturation (IIS) helps to identify the structural breaks due to COVID-19, as well as the effects of political and financial events on the returns and volatility series of Islamic banks. The results indicate that all the events due to COVID-19 are significant. While 19 out of 21 political and financial events impacted the returns and volatility series, there were only 2 political events out of 18 that showed no significant effect on the returns and the volatility series. The state’s and Islamic banks’ policymakers can use these results to build an effective and sustainable financial policy regarding Islamic finance and the banking sector.

Outliers detection in state-space model using indicator saturation approach

Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009.

Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015–2017)

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

Detection of Outliers in the Volatility of Malaysia Shariah Compliant Index Return: The Impulse Indicator Saturation Approach

Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS).Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). Keywords: outliers; volatility; stock indices; IIS

Event Study and Impulse Indicator Saturation Analysis to Assess Reaction of Terrorist and Political Events: Evidence from Oil and Gas Sector of Pakistan

The objective of this study is twofold, first, to assess the impact ofterrorist attacks and political events on returns and volatility oil and gassector of Karachi Stock Exchange from the period of 2004 to 2014.Second, to compare the results of these events applying event studymethodology, event dummy analysis and impulse indicator saturation.Results indicate that the oil and gas sector reacts on the occurrence ofterrorism and political events and the results of two methodologiesevent study and event dummy analysis are almost similar. However,impulse indicator saturation is able to provide better results incomparison to event study and event dummy analysis because as itcaptures all breaks and co-breaks within a sample period,moreover it clearly helps in defining rebounding period of the market. JEL Classification Codes: G12, G14

Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies

We used a time-series cross-section dataset to test several hypotheses pertaining to the role of macroprudential policy instruments in the management of the financial cycle in advanced open economies. The short-run effects are most significant for caps on loan to value and income (LTV and LTI) and risk weights (RW). The long-run coefficients of credit growth with respect to the indicators of amortisation requirements (Amort) and RW are also significant. The estimation results when house price growth is the dependent variable are consistent with these results. Our findings do not support that Basel III type countercyclical buffer (CCyB) has affected credit growth, and we suggest that the variable is mainly a control in our dataset. In that interpretation, it is interesting that the estimated coefficients of the other instruments are robust with respect to exclusion of CCyB from the empirical models. The main results are also robust to controls in the form of impulse indicator saturation (IIS), which we employed as a novel estimation method for macro panels.

Outlier Detection

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article. Detection of outliers is an important explorative step in empirical analysis. Once detected, the investigator will have to decide how to model the outliers depending on the context. Indeed, the outliers may represent noisy observations that are best left out of the analysis or they may be very informative observations that would have a particularly important role in the analysis. For regression analysis in time series a number of outlier algorithms are available, including impulse indicator saturation and methods from robust statistics. The algorithms are complex and their statistical properties are not fully understood. Extensive simulation studies have been made, but the formal theory is lacking. Some progress has been made toward an asymptotic theory of the algorithms. A number of asymptotic results are already available building on empirical process theory.