Search

Purpose: The authors examine network features such as connectivity, centrality, adjacency matrices, closeness and betweenness measures through a variety of indicators. The results of the study indicate that over time there is a tendency for markets to integrate and segment due to various factors such as pandemics, financial crises, global trade relations and international investments. Design/methodology/approach: This paper employs a visualized network technique to study the dynamics of integration and comovements in global equity markets of emerging economies. Daily closing prices of stock market indices of 24 countries from January 2013 to July 2020 are used to construct a minimum spanning tree network (MSTN) and graph network (GN). Findings: The authors identify India and China as global power hubs and clusters among the emerging economies. India and Bangladesh serve as bridging countries connecting to various other clusters. Bosnia serves as a center in the European region owing to Bosnia's trade relations with neighboring countries. Although Brazil has witnessed the worst recession in the early years of the decade, Brazil has risen to be a central cluster among the Latin American countries. Finally, the authors find that African countries tend to form links with the rest of the world rather than with economies within the Africa continent. Originality/value: This is the pioneering study that uses network models such as MSTN and GN supplemented with measures of centrality and connectivity to study financial market integration in emerging countries. Against this backdrop, this paper aims to work on a network visualization strategy to examine global stock market integration. The authors also try to use graphs and the spanning trees instead of the correlation models to understand the association between the markets, avoiding the downsides of the existing models. The authors' approach tries to visualize the network integration to examine the interconnectedness in the global stock market. [ABSTRACT FROM AUTHOR]

Purpose – The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets. Design/methodology/approach – The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)). Findings – The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases. Research limitations/implications – In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets. Originality/value – The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods. [ABSTRACT FROM AUTHOR]

This paper analyzes access to finance based on the usage of financial services by Indian households. Based on two rounds of nationally representative All India Debt and Investment Survey (AIDIS) for 2002 and 2012, we examine how access to credit is associated with a household's socioeconomic and demographic characteristics using Heckman's two-step approach procedure. Our results show that belonging to higher asset quintile groups has a statistically significant positive effect on the household's decision to take loans only from the formal sector in both rounds and significant negative effects on taking a loan only from the informal sector. Similarly, social and religious groups play a significant role in the decision of the household to take a loan. Households with more educated adults are more likely to take a loan. Households involved in regular wage-earning occupations in urban areas are more likely to take a loan from formal sources. Thus, the importance of policy placing greater emphasis on demand-side barriers rather than on improving the physical availability of formal financial services to promote financial inclusion in India. [ABSTRACT FROM AUTHOR]

This research introduces a more reliable model for predicting market volatility. The model incorporates Transformer and Multi-transformer layers with the GARCH and LSTM models and compares their performance to classic GARCH-type models. Euro-US Dollar FX Spot Rate (EURO-USD), Australian-US Dollar FX Spot Rate (AUD-USD), S&P 500 Index, FTSE 100 Index, Reliance Industries Ltd., and Samsung Electronics Co Ltd. are analyzed. The timeframe examined is January 2005 to December 2021, with training from 2005 to 2016 and testing from 2017 to 2021. Empirical evidence suggests that hybrid Neural-network models, notably Transformer-based models, outperform individual transformers, deep learning, neural networks, and traditional GARCH-type models, even in unpredictable conditions like the COVID-19 pandemic. Within the hybrid Neural-network models, MT-GARCH and MTL-GARCH showed lower validation error (RMSE) than the other models, showing that the bagging mechanism added to the attention mechanism of the Multi-Transformer architecture helped to lower the error in the variance in the noisy data of the daily returns of the assets, reducing the RMSE of the hybrid multi-Transformer models. In addition, three to four of the five hybrid neural-network models showed appropriate risk estimates for the entire test period, as observed from the Christoffersen test. Moreover, the lengthy sample period helps test whether hybrid models perform better than classic GARCH-type models in volatile conditions like the COVID-19 pandemic. • Multi-Transformer layers are modified to predict the market volatility. • This study indicates that the combination of Transformer and Multi-Transformer layers with other models results in more precise volatility forecasting models. • The suggested stock volatility models produce suitable risk assessments during periods of low and high volatility. [ABSTRACT FROM AUTHOR]

This study tries to examine the empirical relationship between corporate governance and financial performance of Indian firms by developing a corporate governance index (CGI) based on a variety of corporate governance characteristics. Such characteristics include board structure, ownership structure, director participation and busyness, market for external control, and product market competition. We employ a panel dataset for Indian non-financial firms listed on the National Stock Exchange for the period 2010–2018. Using a System GMM dynamic panel approach, we analyse the relationship of CGI with different firm performance measures including market-based performance measures like Tobin's Q (TQ), and accounting-based performance measures like Return on Assets (ROA) and Return on Net Worth (RONW). Based on the empirical results, we find a significant positive relationship of corporate governance index (CGI) with ROA and RONW and significant negative relationship with TQ. Lastly, the CGI used by our study is based on a broad spectrum of dimensions of corporate governance and serves as a direction for business houses and shareholders to work towards governance practices leading to better financial performance of the firms. [ABSTRACT FROM AUTHOR]

Purpose: Though Indian economy since 1980s has expanded very rapidly, yet the benefits of growth remain very unequally distributed. The purpose of this paper is to provide new evidence about the shape, intensity and decomposition of inequality change between 2005 and 2012. The authors find that Gini, as a measure of income inequality, has increased irrespective of geographic regions. Design/methodology/approach: Based on a recent distribution analysis tool, "ABG," the paper focuses on local inequality, and summarizes the shape of inequality in terms of three inequality parameters (α, β and γ) to examine how the income distributions have changed over time. Here, the central coefficient (α) measures inequality at the median level, with adjustment parameters at the top (β) and bottom (γ). Findings: The results reveal that at the middle of distribution (α), there is almost the same inequality in both the periods, but the coefficients on the curvature parameters β and γ show that there is increasing inequality in the subsequent period. Finally, an analysis of decomposition of inequality change suggests that though income growth was progressive, however, this equalizing effect was more than offset by the disequalizing effect of income reranking. Research limitations/implications: This paper shows how it can be possible both for "the poor" to fare badly relatively to "the rich" and for income growth to be pro-poor. Practical implications: This paper stresses the significance of inequality reduction. Social implications: Inequality reduction is very much imperative in ending poverty and boosting shared prosperity. Originality/value: Perhaps, this research work is first of its kind to examine the shape and decomposition of change in income inequality in India in recent years. [ABSTRACT FROM AUTHOR]

India launched the National Health Mission (NHM) in 2005 to reduce maternal and child mortality by providing equitable, affordable, and quality healthcare services. Studies evaluating the impact of NHM primarily focused on the uptake of maternal and child health (MCH) services, revealing a dearth of evidence on related health outcomes. Using annual time-series data, we estimated the population-level (intention-to-treat effect) impact of the NHM on MCH outcomes such as infant mortality rate (IMR), stillbirth rate, neonatal mortality rate (NMR), and maternal mortality ratio (MMR). Our results exhibit strong consensus on the positive impact on IMR in the post-NHM period, with a significant reduction in its rate and regional inequity. Conversely, our estimates show a moderate impact on stillbirth rate and an insignificant impact on NMR and MMR. Our findings inform policy decisions for strengthening the public health system to amplify the positive gains in MCH outcomes. [ABSTRACT FROM AUTHOR]

This study tests spatial interactions in Kaldor's growth laws in a cross‐country perspective during 2000–2001 to 2014–2015. In particular, we examine the interactive hypothesis through spatial proximity using a spatial panel data framework. The results reveal that ordinary least square estimates undermine the relationship defined through standard Kaldorian setting, besides an inconsequential relationship in some cases. Spatial estimates support the Kaldorian growth strategy with a strong spatial interaction across the countries chosen. The first and third law show significantly positive spatial interactions in lag as well as error component. While as Verdon's law reflects similar as well as dissimilar spatial patterns in error and lag components respectively. The results reflect the fact that manufacturing labor productivity and output growth are more spatially interactive which is imperative for sustained higher economic growth. Therefore, highlighting the significant role of the manufacturing sector in attaining the higher steady state. The empirical findings suggest that the relationship between economic growth, manufacturing output, and labor productivity is global and spatially interactive. Considering these interactions may lead to narrowing down the disparities across the countries. [ABSTRACT FROM AUTHOR]