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The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.

This paper aims to unravel the impact of post-pandemic COVID-19 on foreign direct investment (FDI) and its determinants in the South Asian Association for Regional Cooperation (SAARC) Countries.

The study utilized four macroeconomic variables includes growth domestic product growth rate (GDPG), inflation rate (IR), exchange rate (ER), and unemployment rate (UR) to assess their impact on post-pandemic FDI, along with two variables control of corruption (CC) and political stability (PS) to measure the influence of good governance. Random effects, fixed effects, cluster random effects, cluster fixed effects and generalized method of moments (GMM) models were applied to a balanced panel dataset comprising eight SAARC countries over the period 2010–2021. To identify the random trend component in each variable, three renowned unit root tests (Levin, Lin and Chu LLC, Im-Pesaran-Shin IPS and Augmented Dickey-Fuller ADF) were used, and co-integration associations between variables were verified through the Pedroni and Kao approaches. Data analysis was performed using STATA 17 software.

The major findings revealed that the variables have an order of integration at the first difference I (1). Nonetheless, this situation suggests the possibility of a long-term link between the series. And the main results of the findings show that the coefficients of GDPG, CC and PS are positive and significant in the long run, showing that these variables boosted FDI inflows in the SAARC region as they are significantly positively linked to FDI inflows. Similarly, the coefficients of UR, IR, ER and COVID-19 are negative and significant.

By identifying the specific impacts of the post-pandemic FDI and its determinants, governments and policymakers can formulate targeted policies and measures to mitigate the adverse effects and enhance investment attractiveness. Additionally, investors can gain a deeper understanding of the risk factors and adapt their strategies accordingly, ensuring resilience and sustainable growth. Finally, this paper adds value to the literature on the post-pandemic impact on FDI inflows in the SAARC region.

This paper is the first attempt to trace the impact of COVID-19 on Foreign Direct Investment and its determinants in the SAARC Countries. Most of the previous studies were analytical in nature and, if empirical, excluded some countries due to the unviability of the data set. This study includes all the SAARC member countries, and all variables' data are completely available. There is still a lack of empirical studies related to the SAARC region; this study attempts to fill the gap.

The direction of the causality relationship between Foreign Direct Investment (FDI) and economic growth is a highly controversial issue in the literature. There are two basic approaches advocating different causal directions between FDI and growth, which are called hypotheses of FDI-led Growth and Growth-led FDI. The aim of this study is to analyze the causality relationship between FDI and economic growth in RCEP countries and thus make a new contribution to the discussions in the relevant literature. In addition, the results of the study are expected to provide important implications for the policies to be designed for economic growth based on FDI flows to RCEP countries. Thus, by examining the direction of causality between FDI and economic growth in RCEP countries, we aim to provide a new contribution to related literature and make some implications for the policy design process of economic growth in the RCEP area.

We empirically examined the direction of a causal link between FDI and economic growth in the context of Regional Comprehensive Economic Partnership (RPEC) countries in order to test the hypothesis of FDI-led growth and Growth-led FDI. Accordingly, as our main variables of interest, we incorporated the inward foreign direct investment stock to gross domestic product ratio (FDI) and gross domestic product per capita (GDP). Hatemi-J (2012) asymmetric causality test has been employed in the investigation of the direction of causality between FDI and GDP over the period of 1980–2020. Thus, unlike most of the studies investigating the direction of causality between FDI and growth using the linear causality analysis method, our study performed a nonlinear causality analysis.

Empirical results reveal that the causal relationship between FDI and national income in RPEC countries is non-linear or asymmetric . The results of the symmetric causality test for both from FDI to national income and from national income to FDI are statistically insignificant for all countries. Therefore, this finding obtained from the study provided an important guide to the econometric methods to be used in other studies to be conducted in the same region in the future. Concerning the asymmetric causality relationship from FDI to growth, positive FDI shocks are an important cause of national income in most RCEP countries. However, the effect of negative FDI shocks on national income is quite weak compared to positive shocks. Regarding the asymmetric causality relationship from growth to FDI, positive national income shocks do not create a significant causal relationship with FDI. Similarly, the effects of negative national income shocks on FDI are statistically insignificant. Overall, asymmetric causality test results reveal that positive FDI shocks have an important causal impact on economic growth in most RCEP countries. Thus, the results of econometric analysis mostly support the argument that the FDI-led growth hypothesis rather than the Growth-led FDI hypothesis in RCEP countries. Accordingly, policy-makers in most of the RCEP countries should continue to provide more incentives and facilities to multinational companies in order to ensure constant economic growth.

Our study brings a significant difference in the econometric method used compared to most of the other studies in the literature. Existing empirical studies on the direction of causality between FDI and growth mostly use standard Granger-linear causality-type tests to detect the direction of causality among FDI and growth. Unlike most of the studies in the literature, our study adopted a different methodological approach, namely the Hatemi J test to detect the non-linear causality between FDI and economic growth in RCEP countries. Therefore, this paper made a new methodological contribution significantly to the literature focusing on the causal relationship between FDI and economic growth by using a non-linear causality method rather than a linear causality one.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.