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This study aims to examine the causal relationship between tourism and overall well-being. The main objective of this research is to inform the policymakers that tourism can play a vital role in shaping the overall well-being in the developing economies.

This investigation used several time series techniques and procedures that include bounds test and autoregressive distributed lag mechanism to analyze the relationship between tourism and overall well-being in Pakistan by using time series data for the period 1980-2016.

The findings suggest a significant positive relationship between tourism and overall well-being both in the short and long run. The authors find that tourism and overall well-being affect each other positively.

This research indicates that policymakers and government can improve the overall well-being through tourism development. However, tourism policies and long-term planning should be focused on sustainable developments for achieving long-term goals. Besides, special incentives should be provided to the private sector for tourism development.

To the best of the authors’ knowledge, this is the first investigation that examines the causal relationships between tourism and overall well-being through objective indicators in a developing economy. This study fills the immense literature gap and provides new directions to scholars to investigate the mentioned relationship through objective indicators.

The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

The main purpose of this paper is to find out the perception of different respondents' groups related to the factors that influence the online learning for higher education in Bangladesh during the COVID-19 pandemic.

A survey through a structured questionnaire was conducted to gather qualitative information from the 250 respondents (university students, faculty members and administrative officers) in Bangladesh. A questionnaire has been used for collecting primary data, which have been selected using the justification method under the non-probability sampling technique.

The findings of this study indicated that majority of the respondents told that online classes could be more challenging than the traditional classroom because of the technological constraints, digital divide, insufficient data pack to access the material to attend the class, poor connectivity, lack of device, poor learning environment, technophobia, delayed response and incapability of the teacher to handle efficiently the material and communication machineries.

Due to time restriction and the COVID-19 pandemic, the study was constrained only to Dhaka region in Bangladesh.

The outcomes of the work can be supportive to the governing bodies and proprietors of the higher schooling organizations who are forecasting to adopt online education as a consistent movement in the future.

At last, based on outcomes, investigators have presented some recommendations that can be taken into consideration at policy level. The study would help universities to comply with the pressing need to impart experiential learning through online education during the COVID-19 pandemic.

The study seeks to evaluate the extent and quality of environmental reporting following a longitudinal analysis and covering a wide spectrum of industries in a single frame. The study also attempts to identify the set of most favored environmental reporting items by firms and items which are least disclosed. Furthermore, the study attempts to test whether certain corporate attributes such as firm size, age of the firm, leverage ratio, profitability, presence of independent directors in the board and gender diversity have any influencing power over environmental disclosure practices. The whole study has been carried out from legitimacy theory setting.

The study follows longitudinal analysis to identify the extent and quality of environmental disclosures. A self-constructed checklist of 12 environmental reporting items has been developed analyzing the annual report and content analysis method is followed to measure the extent and quality of environmental disclosures and identify environmental reporting items which are mostly disclosed and which are least disclosed. The study further uses panel data regression analysis to investigate whether certain corporate attributes have any impact on environmental disclosures using multiple linear regression. Total of 345 annual reports of listed financial and nonfinancial institutions have been observed in this study ranging from 2015 to 2019.

The key finding suggests that strict enforcement of Green Banking Rules 2011 fosters country’s commercial banks to invest more to protect the environment and commercial banks encourage nonfinancial institutions for environmental performance and related disclosures through finance. Therefore, almost 50% of sample firms disclose their environmental performance through reporting in either narrative, quantitative or monetary format which was only 2.23% in the last decade. Findings also reveal that tree plantation is the most reported environment disclosure followed by investment in renewable energy and green infrastructural projects and the least reported items are fund allocation for climatic changes and carbon management policy. Further analysis shows that firm size and leverage ratio both have positive impact on environmental reporting.

An in-depth analysis may be conducted to identify why certain environmental items are least disclosed such as fund allotment for climatic changes, carbon management policy, etc. and how corporations may earn social appreciation and motivation by investing in those least preferred items in legitimacy theory setting. Future research may also take into consideration other corporate attributes which are not considered in the study.

The study conducted an in-depth analysis to understand the most favored form of environmental disclosures (narrative/quantitative/monetary) and their extent after incorporation of regulatory guidelines, which is the first of its kind in the research of environmental disclosures. The study indeed contributes to the documentation of environmental reporting in the context of a developing country where there is a lack of longitudinal analysis from the lens of legitimacy theory. Moreover, a wide spectrum of industries has been taken into consideration which facilitates the generalized findings on the environmental disclosure practices of corporations in Bangladesh.

本研究擬評估公司報告環境方面的程度和質量, 以及對就環境報告披露而言、最受青睞和最不受歡迎的項目加以處理。研究亦擬測試企業屬性對實踐環境信息披露的影響。

研究使用內容分析法、去測量環境信息披露的程度和質量。研究使用多元回歸分析、去探討企業屬性對環境信息披露的影響。研究涵蓋孟加拉國上市公司共345個年度報告, 涵蓋的年期為2015年至 2019年。

研究結果似乎顯示綠色金融規則 - 2011 、成功鼓勵機構為保護環境而投放更多資源; 機構最樂於匯報的項目為植樹, 而披露最少的則為氣候變化和碳管理政策。進一步的研究分析顯示, 公司的規模和杠杆比率均會對環境匯報帶來正面的影響。

本研究豐富了關於發展中國家環境匯報的官方文件記錄, 而在這類國家, 透過合法化理論而進行的縱貫性分析研究頗為缺乏。本研究以深度分析法、去瞭解環境信息披露方面最受青睞的信息披露方式 (故事形式的敘述/定量形式/金融形式), 也去瞭解納入強制的規管指引後環境信息披露的程度; 就此而言, 本研究為這類環境信息披露研究的首個研究。

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

The purpose of this paper is to provide comprehensive, theoretical and practical knowledge that will assist decision-makers in making informed decisions when promoting several religious sites in the Kingdom of Saudi Arabia (KSA). Specifically, this study examines the popularity of several religious sites, the personas of prospective visitors and their intentions to visit.

The study uses several methodological approaches to fulfil its main objective, namely, Google Trends analysis, K-means cluster analysis and linear regression analysis.

The results reveal that several religious sites in the KSA are popular and have potential for further consideration by various stakeholders. In addition, four personas were identified which can aid decision-makers and marketing practitioners in designing suitable plans for prospective visitors based on the participants’ motivation and demographics. Furthermore, a significant association was observed among three motivational variables (self-esteem, relationship and physiological needs) and the participants’ intentions to visit.

This study makes an original contribution to the literature, as it examines several religious sites in Saudi Arabia in addition to the sites that are part of the practices of Hajj and Umrah. Furthermore, this study provides comprehensive knowledge in this area to assist both future researchers and practitioners.