The Compensation and Benefits Package is considered the main employee’s motivator. As such, it plays a crucial role in determining successful recruiting, engagement, and retention strategies. Failing to offer the right package, will be translated into additional costs. This study aimed to identify what type of compensation and benefits Albanian employees receive and their relationship with job satisfaction, before and after COVID-19. We surveyed 127 employees and conducted 10 in-depth interviews, in different institutions in Albania. Descriptive and inferential statistics were used to analyze the data. The findings showed that most received benefits are medical insurance and bonuses, and employees are less than considerably satisfied with their package. The majority stated that it had great importance in their job satisfaction, and most would leave their current job for another one that offers more benefits. Findings related to the COVID-19 impact showed that employees’ lifestyle has changed and flexible working hours are the top benefit, followed by more paid time-off options. The conclusions of the study indicate that Compensations and Benefits packages should be redesigned to meet the needs of working in the “new normal”, for current and future employees, paying close attention to their preferences.

In the last two decades, corporate governance has had its “shining era of research”, especially in developed countries, where good management, transparency, and reaching the investors’ basis meant a huge deal for big corporations. Without reducing the importance of the subject, such issues have reached the developing countries a bit later in time, and investigation on the matter has been not so extensive, especially due to the lack of proper legal framework and real data. Yet corporate governance is reaching an interest lately in emerging markets, like Albania which aspires to be in the Union, and due to the “new normality” in the management style, which requires a re-adjustment of business structures. This paper attempts to determine the role that board size and board independence as corporate governance variables have in a company’s performance, measured by the board of directors’ characteristics. Even though at a very initial stage of its development, the insurance industry in Albania has applied certain corporate governance structures, mainly because insurance companies are part of international groups. As the insurance industry is integrated into the wider financial services and nowadays is getting more important in the local service-based financial market, understanding the factors affecting its profitability becomes important too. The results of this study show that there is a negative relationship between board size and corporate performance, while there is a positive relationship between board independence and corporate performance.

The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.

Financial technologies (Fintech) have rapidly reshaped access to financial services, particularly in developing countries where traditional banking remains limited. This study investigates fintech’s role in advancing financial inclusion by analyzing panel data from 89 developing economies gathered from Global Findex reports (2011–2021), complemented by International Monetary Fund (IMF), UNU-WIDER, and PRIO datasets. We applied a random-effects regression model and GMM, incorporating fintech adoption alongside macroeconomic and institutional variables such as education, governance quality, and trade openness. Our results show that fintech is the most significant driver of financial inclusion, especially in expanding account ownership, with education and institutional quality further enhancing outcomes. Conversely, we show that population growth and income disparities constrain progress, while government expenditure and GDP growth display mixed effects. We also find that fintech reduces transaction costs and barriers, yet its impact depends on digital literacy, infrastructure, and governance. In conclusion, our findings highlight that fintech represents a transformative but unevenly utilized tool, capable of fostering broader economic participation and reducing inequality when paired with supportive policies and institutional frameworks.

This study examines the ability of asymmetric GARCH-family models, specifically EGARCH and GJR-GARCH, to capture and forecast the volatility of major decentralized cryptocurrencies. We analyzed the returns of seven leading assets (BTC, ETH, ADA, XRP, LTC, XLM, DASH). We used the Crypto Fear & Greed Index (CFGI) as a dummy variable, covering a period when all cryptocurrencies were active simultaneously. Notably, the Student-t distribution provided the best in-sample results with the lowest AIC and BIC for both models. When comparing the models directly, EGARCH consistently outperforms GJR-GARCH across in-sample metrics. The use of the CFGI dummy variable marginally improves in-sample results for only three of the seven cryptocurrencies, suggesting it may be adding noise to the models for some coins. Additionally, there is no clear rule of asymmetry across all cryptocurrencies, suggesting a fundamental structural difference from the traditional stock market. Out-of-sample metrics and performance vary more than in-sample metrics, with normal and GJR-GARCH models yielding better performance and lower QLIKE values for specific cryptocurrencies. This study contributes to the growing literature on volatility modeling and forecasting in cryptocurrencies, highlighting the importance of asset-specific valuation in the cryptocurrency market. It also provides a framework for integrating specific market indicators into the modeling framework.