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This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment.

It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models.

The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns.

This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

The purpose of this article is to present the idea of a T-spherical hesitant fuzzy set associated with probability and to develop an extended multi-attributive border approximation area comparison (MABAC) method under probabilistic T-spherical hesitant fuzzy (Pt-SHF) settings.

The authors define some basic operational laws for Pt-SHF sets (Pt-SHFSs) and a comparison method of two probabilistic T-spherical hesitant fuzzy numbers (Pt-SHFNs) is proposed. Moreover, some Pt-SHF aggregation operators and the multi-attributive border approximation area comparison (MABAC) method are established under Pt-SHF scenario to solve group decision making problems.

The developed Pt-SHF MABAC method for multi-attribute group decision making (MAGDM) can overcome the drawbacks of conventional MABAC method and limitations for decision makers, which they face while providing their assessment concerning any object.

Clearly, this paper is devoted to MABAC method, MAGDM and probabilistic T-spherical hesitant fuzzy set theory.

The approach established can be used in a variety of scenarios, including decision making, engineering, and economics. An explanatory example is illustrated which shows the superiority and effectiveness of our proposed technique.

If a T-spherical fuzzy MAGDM problem under the probabilistic scenario needs to be evaluated, the involvement of probabilities in fuzzy system will be lost because of no information. This work fills a gap in literature by establishing the notion of probabilistic t-spherical hesitant fuzzy set to deal with the ambiguity, uncertainty in decision making problems.

A banking system is essential for financial stability, especially economic growth and development. The authors investigate the dynamic linkage of key banking system stability measures, namely, liquidity, capital, profitability, and credit risk. To this end, the authors employ Panel Vector Autoregressive (VAR) to a panel data set of country-level banking system indicators from seven developing countries; from March 2010 to December 2020 (308 country quarter observations). A nation is selected on the basis of similar characteristics large and bank-based economy with the considerably same stage of economic development. The authors find a remarkable resilient feature of the banking system in which both liquidity risk and credit risk appears significant only in the short run (within three quarters). Shocks from both risk sources dissipate quickly, suggesting an internal mechanism is at work. This study provides evidence of how a good performance of financial safety net should be.

The primary purpose of this paper is to investigate the healthcare organization’s intention to use radio frequency identification (RFID) technology for improving efficiency. This paper also intends to identify various factors that influence the adoption of RFID in the healthcare organization. This paper develops and tests seven different hypotheses. These hypotheses are tested using structural equation modeling. Our results provide support for a number of relationships in the hypothesized model. These include direct relationships among the factors risk, resistance to change, supplier support and the factor perceived usefulness. However, the study did not find support for the relationship between the factors perceived ease of use and intention to use. The results provide support for several indirect relationships as well. These include indirect relationships between the factors perceived resistance to change, risk, suppliers’ support and perceived ease of use with the factor intention to adopt RFID technology in the healthcare organization. This research is grounded in the theory of reasoned action and applies the technology acceptance model (TAM) to the healthcare organization’s intention to use RFID technology.

Citizens are substantial stakeholders in every e-government system, thus their willingness to use and ability to access the system are critical. Unequal access and information and communication technology usage, which is known as digital divide, however has been identified as one of the major obstacles to the implementation of e-government system. As digital divide inhibits citizen’s acceptance to e-government, it should be overcome despite the lack of deep theoretical understanding on this issue. This research aimed to investigate the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. In order to get a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide.

The research problems were approached by applying two-stage sequential mixed method research approach comprising of both qualitative and quantitative studies. In the first phase, an initial research model was proposed based on a literature review. Semi-structured interview with 12 users of e-government systems was then conducted to explore and enhance this initial research model. Data collected in this phase were analyzed with a two-stage content analysis approach and the initial model was then amended based on the findings. As a result, a comprehensive research model with 16 hypotheses was proposed for examination in the second phase.

In the second phase, quantitative method was applied. A questionnaire was developed based on findings in the first phase. A pilot study was conducted to refine the questionnaire, which was then distributed in a national survey resulting in 237 useable responses. Data collected in this phase were analyzed using Partial Least Square based Structural Equation Modeling.

The results of quantitative analysis confirmed 13 hypotheses. All direct influences of the variables of digital divide on e-government system success were supported. The mediating effects of trust in e-government in the relationship between capability divide and e-government system success as well as in the relationship between innovativeness divide and e-government system success were supported, but was rejected in the relationship between access divide and e-government system success. Furthermore, the results supported the moderating effects of demographic variables of age, residential place, and education.

This research has both theoretical and practical contributions. The study contributes to the developments of literature on digital divide and e-government by providing a more comprehensive framework, and also to the implementation of e-government by local governments and the improvement of e-government Readiness Index of Indonesia.

The purpose of this paper is to empirically investigate the disclosure practices of market risk by 30 banks in ten countries of different size and geographic distribution (USA, Canada, UK, Germany, Japan, Italy, The Netherlands, France, Greece and Cyprus).

The paper uses content analysis and other statistical techniques (regression and correlation analysis) to produce qualitative and quantitative indicators of the degree of market risk disclosure to ascertain if differences exist across countries and across banks of different size.

The findings validate the testing hypotheses, namely that there are still significant differences across banks in different countries, meaning that there is no harmonization in disclosure practices; that the banks in the Anglo‐Saxon countries (UK and USA) are consistently better in their overall risk reporting practices; that the banks that are “good” in reporting qualitative information are also “good” in reporting quantitative information on risk types; OLS regression analysis and correlation analysis point to a positive association between bank size (as measured by the market capitalization) and the level of risk reporting.

The study contributes to a research area that is under‐researched, especially focusing on market risk of banks across countries.

The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series.

A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented.

Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio.

The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices.

This paper is the first comprehensive approach to the risk properties of Bitcoin.

The purpose of this paper is to aid understanding of the changes in Basel Committee on Banking Supervision (BCBS) regulatory strategies after the global financial crisis.

The author uses the credit valuation adjustment (CVA) charge reform as a test case for inquiring whether BCBS has departed from its pre-crisis facilitative regulatory strategy path. The regulatory strategy of the CVA charge is discussed.

The charge exhibits a new regulatory strategy that BCBS has adopted. It seeks to manipulate market structures by imposing risk-insensitive capital charge methodologies.

The paper offers a new heuristic to analyse regulatory initiatives and their significance. The CVA charge has not been subject to a regulatory theory-based analysis in prior literature.

This study aims to understand how the COVID-19 pandemic dramatically impacts the maturity of all industrial sectors globally. This paper analyses the general patterns of managing maturity in terms of performance and risk-taking of S&P 500 industrial sectors while determining their association with COVID-19.

To analyse the immediate response of COVID-19 on maturity management, the authors gather time-series daily index data of S&P sectors from October 2019 until June 2020 from Bloomberg. The authors select this study period to show the immediate effect of COVID-19 on industrial sector maturity management. The performance and volatility of stock are proxies for managing the maturity of each sector. The authors use vector auto-regression (VAR) methodology to determine the impact of global coronavirus.

This study’s findings suggest that the information technology sectors outperform the other sectors; in contrast, the utility sector exhibits the worst performance during a pandemic. Furthermore, the real estate sector depicts a higher level of systematic risk pattern than other sectors. Interestingly, the empirical result of VAR shows that almost every sector is significantly negatively affected by this pandemic; however, the consumer discretionary sector is immune to it.

Overall, this study’s findings for individual economic sectors demonstrate that the managing maturity of each sector acts differently to the coronavirus outbreak. This study offers insights to researchers, policymakers, regulators, financial report users, investors, employees, clients and society.

This paper contributes to the existing literature on managing the maturity of industry sectors in terms of observing their trends during the financial crisis.

Monetary policy has major impacts on macroeconomic indicators of the country. Accordingly, uncertainty regarding monetary policy shifts can cause challenges and risks for businesses, financial markets and investors. Thus, the purpose of this study is to investigate how real estate market volatility responds to monetary policy uncertainty.

The GARCH-MIDAS model is applied in this study to investigate the nexus between monetary policy uncertainty and real estate market volatility. This model was fundamentally instituted to accommodate low-frequency variables.

The results of this study reveal that increased monetary policy uncertainty highly affects the volatility in real estate market during the peak period of COVID-19 as compared to full sample period and COVID-19 recovery period; hence, a significant decline is evident in real estate market volatility during crisis.

This study is particularly focused on peak and recovery period of COVID-19 considering the geographical region of Greece, Japan and the USA. This study provides a complete perspective on the nexus between monetary policy uncertainty and real estate markets volatility in three distinct economic views.