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This paper aims to examine the specific findings on the level of technical compliance and operational effectiveness of the national financial intelligence units (FIUs) in 55 members of the Asian Development Bank (ADB) under the mutual evaluations carried out by the Financial Action Task Force (FATF) and its regional bodies (also referred to as FATF-style regional bodies) in connection with the current international standard for combating money laundering and terrorism financing (i.e. the FATF recommendations). It also provides three observations for enhancing the use of financial information and intelligence.

Review of published reports on country mutual evaluations from the FATF and its regional bodies.

A majority of the FIUs from these 55 members of the ADB were rated around the “mid-range” under the FATF methodology used for the mutual evaluations (i.e. “compliant and substantially effective”, “largely compliant and substantially effective”, “compliant and moderately effective” and “largely compliant and moderately effective”). Observations were also provided on cross-cutting areas for enhancing the use of financial information and intelligence.

FIU operations are key to combating money laundering and terrorism financing, and this examination of the level of technical compliance with the international standard and related operational effectiveness provides an useful account of current developments in this space and suggestions for further actions by relevant national authorities and provision of country technical assistance and support by donor partners.

The purpose of this study was to examine racial/ethnic differences in AFS use and their contributing factors using a decomposition analysis.

The 2018 National Financial Capability Study dataset was used to analyze the four major types of AFS—title loans, payday loans, pawnshops, and rent-to-own (RTO) stores—as proxies for AFS use. The study conducted both logistic regression analysis and decomposition analysis to examine the contributing factors.

The results of the logistic regression analysis demonstrated significant disparities in the use of alternative financial services (AFS) among racial and ethnic groups. Specifically, it was found that Blacks were more likely to utilize title and payday loans, pawnshops, and rent-to-own (RTO) stores compared to Whites. In contrast, Hispanics and Asians/individuals of other ethnicities were less likely to use title loans, but Hispanics were more likely to opt for payday loans over Whites. Furthermore, objective financial literacy exhibited a negative association with the likelihood of using these four types of AFS, whereas subjective financial literacy consistently showed a positive association. When examining the decomposition analyses, it became evident that both objective and subjective financial literacy played significant roles in explaining the racial and ethnic disparities in AFS usage. However, the patterns varied in three specific pairwise comparisons.

This study revealed the relative contributions of each factor to the racial/ethnic disparities through decomposition analysis. Our Fairlie decomposition approach addressed non-linearities within the decomposition framework, particularly in estimating the probabilities of AFS utilization, given its binary outcomes. This extension builds upon the Oaxaca decomposition. The study offers valuable insights into the variations in AFS use among different racial and ethnic groups.

This paper aims to investigate the sentiment connectedness among 10 European stock markets between January 2020 and July 2022, associating such connectedness with the level of the geopolitical risk index.

For this purpose, a time-varying parameter vector autoregressive connectedness framework is used.

Results show a high degree of sentiment connectedness. Overall, the sentiments of Portugal, France, the Netherlands, Spain, Germany and Italy are net transmitters of shocks while those of Poland, Sweden, Norway and Romania are net receivers. Additional evidence indicates that when geopolitical risks increase, the sentiment connectedness tends to decrease. However, the reverse holds under extremely high levels of geopolitical risks.

Overall, this study provides some significant contributions to the literature. First, to the best of the authors’ knowledge, this is among the first few studies to examine the dynamic connectedness among stock market sentiment across countries. This issue needs special consideration for European countries because of their close geographical distance and strong integration due to the European Union’s co-development strategies. Second, the association of sentiment connectedness with geopolitical risk is examined for the first time. This is even more meaningful in the context of growing geopolitical risks stemming from the Ukraine war, which could affect international financial markets.

This study aims to empirically connect green logistics performance, renewable energy, non-renewable energy, CO₂ emissions and gross domestic product in Vietnam from 2000 to 2022.

Within this study, the author uses innovative tools, specifically a R2 decomposed linkage method, to scrutinize the connections between green logistics, environmental issues and the use of green and dirty energy.

The results highlight the two-way relationship between green logistics and energy security in Vietnam. Green logistics plays various roles in diverse periods, from a net shock transmitter to a net shock receiver in the designed system. Using a dynamic and contemporaneous dynamic linkage method, this study emphasizes the change in the role and the dominance of green logistics and renewable energy consumption. Notably, the unexpected shocks also lead to changes in these variables’ roles.

This paper presents two significant contributions to the existing body of literature. Firstly, as previously emphasized, this research marks a pioneering effort to examine the connection between green logistics, environmental issues and the use of green and dirty energy when it comes to developing nations such as Vietnam. Secondly, this research introduces a novel approach to investigating the interconnectedness of volatility across diverse markets, offering a more suitable method for such analyses. Within this study, the author uses innovative tools, specifically an R2 decomposed linkage method, to scrutinize the connections between green logistics, environmental issues and the use of green and dirty energy. In this analysis, the author examined data from 2000 through 2022. A thorough analysis is presented using the data, exploring the connections between the volatilities resulting from various problems in Vietnam.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.

The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.

The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.

The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.

This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

This study aims to investigate the dynamics between exogenous shocks, financial stress and economic performance in the USA from January 1995 to August 2023.

Granger-causality tests and impulse response analyses are used to examine causal relationships and dynamic responses among crude oil prices, real M2 money supply, financial stress and key economic indicators.

This study reveals a significant correlation between elevated financial stress and reduced real output, along with disruptions in the labor market, potentially leading to economic recessionary trends. Failure to address these challenges could perpetuate labor market difficulties, weaken capital accumulation within the loanable funds market and ultimately hinder long-term economic growth prospects in the USA.

This study offers insights for policymakers to mitigate financial stress. Recommendations include enhancing financial surveillance, strengthening regulatory frameworks, promoting economic diversification and implementing countercyclical policies to stabilize the economy and support labor markets. In addition, proactive monitoring of financial stress indicators can serve as early warning signals, aiding in timely interventions and effective risk management strategies.

This research provides a comprehensive analysis of how the financial stress index (FSI) mediates the effects of external shocks on the US economy, addressing a gap in existing literature. The integration of the FSI into the analysis enhances the understanding of the transmission channels through which external shocks influence the economy.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.