PurposeThis study aims to conduct a comprehensive bibliometric analysis to determine the intellectual structure of cross-listing studies and suggests a road map for future research in this field.Design/methodology/approachA step-by-step procedure was carried out. With the help of a defined search string, 580 articles from reputed journals have been retrieved from the Scopus database. Bibliographic coupling and keyword analysis were executed to understand the current research scenario and future research directions in this research field. In addition, R Studio combined with VOSviewer was employed to analyse and visualise the data.FindingsThe results provide a deeper insight into publication trends, most prolific countries, institutions and journals in the area of cross-listing. The highest collaboration was observed between the authors in the USA and Canada. Moreover, the results contradict Bradford's and Lotka's laws. A thorough review of the literature identifies five clusters in this domain. Finally, keyword analysis offers a future road map in cross-listing research.Originality/valueResearchers have shown greater interest in cross-listing topics over the past decades. Even though the research volume on this subject is increasing, the current retrospective is still insufficient. To the best of the authors' knowledge, this study is the first to provide valuable insights to practitioners, academicians, and prospective researchers about the intellectual structure of cross-listing and also offers future avenues in this research field through bibliometric analysis.

PurposeSeveral empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approachThe volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.FindingsThe findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implicationsThis study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/valueEarlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

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Purpose Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies. Design/methodology/approach The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020. Findings Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar. Originality/value This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

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Purpose This study aims to examine the determinants of adoption intention toward the rooftop solar photovoltaic (RSPV) systems among residents of peri-urban villages of Gurugram, Haryana, India. This study also analyzes the impact of the adoption of RSPV systems on carbon neutrality from a behavioral perspective. Design/methodology/approach Data was collected using a self-administrated structured questionnaire from 208 male villagers (195 usable) of 22 villages using the purposive sampling technique. Findings Results revealed that relative advantage, followed by simplicity, trialability, observability and compatibility, positively and significantly impact villagers’ attitude toward adopting RSPV systems in their homes. Perceived severity and perceived vulnerability significantly influence the perceived behavioral control of villagers toward adopting the RSPV systems. The results show villagers’ attitudes, subjective norms and perceived behavioral control are the essential predictors of their adoption intention of the RSPV systems. Most notably, carbon neutrality was significantly affected by villagers’ adoption intention of RSPV systems as the renewable energy source in their homes. Originality/value The findings of this study provide that innovation attributes are important factors in shaping the adoption intentions of customers toward RSPV systems. This study is also the extent of previous studies measuring customers’ perception of adopting renewable energy in developed and emerging countries worldwide.

This study examines the relationship that both domestic and foreign institutional net equity flows have with the India stock markets. The motivation behind is the study to examine whether increased net equity investments from domestic institutional investors has reduced the influence of foreign equity flows on the Indian stock market volatility. Our results indicate that only during periods in which domestic equity inflows surpass foreign flows by a significant margin, as seen during 2015–2018, is the Indian stock market volatility not significantly influenced by foreign equity investments. However, during periods of re-emergence of strong foreign net inflows, the Indian market volatility is still being impacted significantly, as has been observed since 2019. Furthermore, we find that both large-scale net buying and net selling by domestic funds increased the stock market volatility as observed during 2015–2018 and COVID-impacted year 2020 respectively. The implications of this study are multi-fold. First, the regulators should discuss with industry bodies before enforcing major structural changes like reconstituting of mutual fund investment mandate in 2017 which forced domestic funds to quickly change portfolio allocation amongst large-cap, mid-cap and small-cap stocks resulting in higher stock market volatility. Second, adequate investor educational and awareness programmes need to be conducted regularly for retail investors to minimize herd behaviour of investing during market rise and heavy redemptions at times of fall. Third, the economic policies should be stable and forward-looking to ensure foreign investors remain attracted to the Indian stock markets at all times.

The study is about contributing to the ongoing discussion on the diversification opportunities for emerging markets with non-conventional asset class. The limited literature in the era of fourth industrial revolution motivates us to gauge diversification opportunities. This study is focusing on identifying diversification opportunities with a set of unique asset classes that are the proxies for Green Funds, FinTech and Artificial Intelligence-based index funds. The method and model applied in the study are time and frequency connectedness in a Wavelet Coherence, and for the robustness check—Network analysis has been applied. The originality of the study lies in identifying the impact of the outbreak of COVID-19. The results captured that FinTech-based asset was the most resilient asset class during the pre- and post-outbreak of COVID-19, followed by AI-based fund and finally by Green fund. Henceforth, FinTech provides superior diversification opportunities among all with MSCI Emerging Market. AI and Green funds are captured to be invested in the long term for diversification, whereas FinTech is suitable for both long- and short-term assets. The results are relevant for investors in emerging markets and for policymakers as well.