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The purpose of this study is to investigate the continuous intention to use blockchain and FinTech innovations, focusing on the direct impact of user trust and perceived risks. It seeks to test how information technology (IT) quality directly affects user-perceived risk and trust and to identify how IT quality can influence FinTech continuance intentions. By examining these relationships, the study provides insights into how improvements in IT quality can mitigate perceived risks and enhance user trust, ultimately fostering sustained use of FinTech and blockchain technologies.

To achieve the purpose of this study, the model and hypotheses were examined based on the partial least squares structural equation modeling (PLS-SEM).

Results revealed that perceived risk is negatively impacted by system quality, while trust is positively impacted by information quality, and the most significant result in the study is continuous-use intention and uncertainty both are impacted by service quality. Also, the study used some control variables, and two of them (i.e. FinTech type and education) showed a positive significant relationship with continuance-use intention.

This study identifies several causal relationships between the continuance-use intention of blockchain and FinTech innovations and various factors, which can provide valuable insights for managers, enabling them to formulate appropriate strategies to foster sustainable growth in FinTech and blockchain. By leveraging these findings, managers can enhance IT quality, reduce perceived risks and build user trust, thereby promoting the ongoing adoption and success of blockchain and FinTech innovations.

The outcomes obtained will help both FinTech providers and researchers elucidate and understand the situation of users’ concerns about the unexpected risks/uncertainty in FinTech transactions can be mitigated through providing a high level of quality IT service and systems. Two main strategies can be merged to be used by FinTech providers/managers, first: trust building, second: risk-mitigating, both strategies can be used in the light of IT innovation and its aspects to meet the sustainable growth of FinTech.

The purpose of this paper is to examine the connectedness among Memecoin, Halal exchange traded funds (ETF) and environmental, social and governance (ESG) indexes in different quantiles.

The authors consider Dogecoin to measure Memecoin while Wahed FTSE USA Shariah ETF (HLAL) and SP Funds S&P 500 Sharia Industry Exclusions ETF (SPUS) are used to represent Halaf ETF. Similarly, iShares ESG Aware MSCI USA ETF (ESGU) and Vanguard ESG US Stock (ESGV) proxy the ESG index ETF. The daily price of these examined markets is considered from January 2, 2020, to January 18, 2024. The quantile vector autoregression is deployed for the empirical computation.

The result reveals that Memecoin (Dogecoin) emerges as the best diversifier irrespective of various quantiles because it is least connected in terms of recipient and transmission of shock. In addition, the authors observe an intriguing observation that the total connectedness in higher quantile is large, followed by lower quantile.

This study is undertaken considering the novelty in the form of the proxies of examined markets along with natural outbreak (COVID-19) and man-made outbreak (Russia–Ukraine invasion) periods.

This paper aims to analyze the dynamic linkages of the energy market with the forex market. The energy market is measured by crude oil WTI, while the forex market is proxied by Brazilian real (RBRL), Mexican peso (RMXN), South African rand (RZAR), Turkish lira (RTRY) and British pound sterling (RGBP) exchange rate.

For the study, daily observations of these constituent asset classes extending from December 31, 2019, to August 16, 2022, are taken as the data. Furthermore, it is categorized into two different sub-samples in the form of the COVID-19 outbreak (December 31, 2019 to February 23, 2022) and the Russo−Ukraine invasion (February 24, 2022 to August 16, 2022). For empirical estimation, Diebold and Yilmaz model (2014) and Barunik and Krehlik test (2018) are used to examine the dynamic linkages.

The study concludes that the Mexican peso (RMXN) receives and transmits the highest spillover, while crude oil (RCOWTI) receives and transmits the least volatility to the network connection in full sample. In addition, the authors report that the dynamic linkage is not constant in the short, medium and long run. Furthermore, the spillover index in the Russo−Ukraine invasion is higher (29.92%) than full observation (22.03%) and COVID-19 outbreak (21.10%) in the short run.

This paper ventures to offer insight to investors, traders and policymakers based on normal trading days and crisis periods.

This paper aims to analyze the time-varying dynamic connectedness among environmental, social and governance (ESG)-compliant firms, Fintech-based firms and artificial intelligence (AI) firm’s stocks.

To examine the spillover from globally leading companies that systematically follow ESG reporting and standards into their financial books to top AI-based and Fintech-based companies, we use the daily observation extending from December 31, 2019 to October 9, 2023. For the empirical investigation, Diebold and Yilmaz (2012) model and Baruník and Křehlík (2018) model are employed.

An intriguing observation is found for both recipient and transmission as Northrop Grumman remains the least shock transmitter and receiver among all constituent markets irrespective of two different used models. On this note, Northrop Grumman can be classified among the safest stock comparatively which has to be held in short, medium and long run to mitigate the risk.

After extensive existing literature review and to the best of the authors knowledge, it is a novel study that examines the dynamic connectedness among ESG, Fintech and AI stocks covering two unprecedented events like the COVID-19 outbreak and the Russia–Ukraine invasion.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

Several 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.

The 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.

The 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.

This 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.

Earlier 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.

Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.

Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.

Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.

Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.