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In continuation to Chapter 3, the present chapter tries to quantify the impact of credit upon GDP and HDI as the first attempt and the linkages of NPA and security investments with credit, GDP and HDI of the countries as the second attempt. For these purposes, this chapter starts with the measurements of credit elasticity with respect to GDP and HDI to know the impact of credit on the private sectors upon the income and human development of the countries. Then, it focuses on the implications of common banking operating tools such as their investments in the governments’ securities in relation to credit to the private sectors, GDP and HDI of the selected countries in a panel data format. The results of the credit elasticity of GDP show that it has taken the positive sign in all of the countries and the negative changes are very little in number. Furthermore, the results on the linkages show that all the variables are mostly cointegrated and therefore maintain stable and equilibrium relationships in the long run among them. But the short-run results show that investment and credit make a cause to NPA, and investment and NPA make a cause to GDP. No variables make any interrelationships with the HDI in either the long-run or short-run systems. Thus, the countries in the list should put more emphasis on the working of the financial sectors as the key partner in the income-generating activities.

This chapter examines the long-run and short-run spillover effects of US quantitative easing (QE) on the money market in India. The study adopts the autoregressive distributed lag bounds testing co-integration approach for monthly data from September 2008 to May 2019 to investigate the spillover impact. The results reveal that a 10% rise in US QE led to a 25 bps softening of the weighted average call rate and a 2–5 bps hardening of the Treasury Bill. This impact was beyond the active participation by the Reserve Bank of India on the policy rate and liquidity in the system during the QE episodes. It suggests that the Indian money market is susceptible to US QE.

The initiative from the world economic community to integrate different types of economies was globalization that ensured free flow of goods and services, it is popularly known as trade openness. The extension of this effort was to cover the flow of financial capital across the economies in terms of net foreign direct investment and foreign portfolio investment, the combination of these types of capital flow is called financial integration (FI). The primary objective of the policies of globalization and FI was to boost up the global as well as country-specific growth rates. Although a list of works is there in the literature on the related fields for different country or group levels, it is hardly to find such works in the highly emerging economies of the world. This study has strived to investigate whether globalization and FI at all influence the growth of incomes of the commonly accepted three top emerging economies, Brazil, China, and India. This study uses unit roots test, Johansen cointegration test, and causality test in a VAR setup for the period 1990–2016 to find long-run associations and short-run dynamics among the variables. It reveals that all the four indicators have long-run associations for the three countries but the errors are corrected for Brazil and China only. However, only for China, the FI and globalization factors have made a cause to PCGDP; no such causal relations are observed for Brazil and India.

Recent evidences show that terrorism is becoming frequent in Nigeria, ranging from incessant Boko Haram activities in the North East; Independent People of Biafra (IPOB) activities in the South-East states, kidnapping and vandalizing oil pipes in the South-South, Fulani-herdsmen attacks in the Middle Belt, among others. In an attempt to tackle terrorism, the Federal Government at different times adopted military actions with little or no lasting solution. The Have and Have-nots hypothesis (Shahbaz, 2013) stresses the role of economic phenomenon in determining the causes of terrorism. It is on this note that this chapter investigates the linkages between economic growth proxy by gross domestic product per capita (GDPPC) and other fundamental variables such as inflation, unemployment, and inequality gaps, among others; and terrorism in Nigeria. We intend to know whether cointegration exists between the two constructs; and if it does, is there causality? The study employed both the autoregressive distributed lag (ARDL) and the vector error correction model (VECM) approaches to examine the existence of or otherwise a long-run relationship as well as causality among the constructs. Results reveal that a compelling cointegrating relationship exists among the variables. It is further revealed that unemployment, inequality, poverty, inflation, among others, Granger cause terrorism. It stresses that the Have-not hypothesis explained the causes of terrorism in Nigeria. The study therefore suggests that policy makers should, in order to prevent or combat terrorism, focus on improving the economy by creating job opportunities through provision of conducive environment that supports businesses and reduces inequality gaps.

The present study has two objectives. First, one is to clarify the terms, “co-movement” and “co-integration” in the context of stock market indices. Second, to investigate empirically, whether an emerging stock market index represented by Nifty has moved together with DJI and N225 during the study period and whether they are co-integrated or not. This chapter tries to search out an answer for co-movement and co-integration staying within the theoretical framework through an extensive review of the literature. Moreover, the present study is unique because it tries to focus mostly on the pros and cons of financial integration and trade liberalization and the contributing factors responsible for trade and financial integrations leading to co-movement and co-integration among the countries considered in this study. India is taken as a proxy for an emerging economy. Furthermore, this chapter considers America and Japan as proxies for the developed countries around the globe and a significant country among the APAC nations, respectively. The empirical results reveal that not only three indices are highly correlated but they also possess a co-integrating relationship. This establishes the fact that neither is there any scope of international diversification in the short run nor in the long run. However, the Granger causality test results point out the fact that Nifty granger causes DJI and N225 during the study period.