This study aims to investigate the interaction between market volatility, economic growth, and cognitive biases over the period from April 2006 to March 2024. Market volatility and economic growth are critical indicators that influence economic stability and investment behavior. Financial market volatility, defined by abrupt and erratic changes in asset values, can have a big impact on the expansion and stability of the economy. According to conventional economic theory, there should be an inverse relationship between market volatility and economic growth since high volatility can discourage investment and erode trust. Market participants’ cognitive biases are a major aspect that complicates this connection. Due to our innate susceptibility to cognitive biases, including herd mentality, overconfidence, and loss aversion, humans can make poor decisions and increase market volatility. These prejudices frequently cause investors to behave erratically and irrationally, departing from reasonable expectations and causing inefficiencies in the market. Cognitive biases have the capacity to sustain feedback loops, which heighten market turbulence and may hinder economic expansion. Similarly, cognitive biases have the potential to cause investors to misread economic indicators or ignore important details, which would increase volatility. This study uses the generalized autoregressive conditional heteroskedasticity (GARCH) model on GDP growth data from the US, the UK, and India, alongside S&P 500, FTSE 100, and NIFTY 50 data sourced from Bloomberg, to examine evidence of these biases. The results show evidence of the predictive nature of market fluctuations on economic performance across the markets and highlight the substantial effects of cognitive biases on market volatility, disregarding economic fundamentals and growth, emphasizing the necessity of considering psychological factors in financial market analyses and developing strategies to mitigate their adverse effects.

This study aims to identify and examine the factors that affected users' behavioral intentions, attitudes, and perceptions toward mobile payment services during COVID-19. It also attempts to determine the impact of demographic variables on the ease of use of mobile payment services. A survey approach was adopted to collect primary data and capture attitudes and behaviors on the use of mobile payment services during and after COVID-19. Being a quantitative study, a multiple regression analysis was used to analyze the primary data. The results indicate that education was the main reason for the adoption of mobile payment services, and security was the leading factor contributing to user satisfaction. Ease of use of the payment app was important for continued usage of the app. The statistical analysis explains that ease of use is an important variable for the usage of payment wallets or mobile payment services by users, followed by familiarity, safety, and security of the mobile payment wallet. Based on the findings of this study, it is suggested that mobile payment companies should concentrate more on the performance and ease of use of their applications. Mobile payment companies should also concentrate on meeting their users’ needs, which will indirectly lead to the retention of their users for a long period.

This paper investigates the dynamic conditional correlations and volatility spillovers between global stock markets and international green bonds. Envisaging the linkages between the emerging green bond markets and global equities given the unforeseen global pandemic. We use R software to run bivariate VAR-BEKK GARCH and BEKK GARCH models to investigate the time-varying conditional volatility between global stock indices and green bond indices. Daily prices of all variables from October 2014 to April 2023 are sourced from the Bloomberg database. The short-term influence of past events, as well as the long-term persistence of green bonds, on the current conditional volatility of global equity indices of developed and emerging economies is observed. Whereas from equity markets to green bond indices, the short-term as well as long-term impact is confined to only 2-3 indices. The results provide future direction for policymakers, researchers, and global investors in hedging and creating an optimal portfolio.

The purpose of this empirical study is to examine the aspects that are driving factors for the acceptance of central bank digital currency (CBDC) among Indian bankers using a structural equation modeling (SEM) approach. The responses of bankers were collected by a questionnaire, and the data was analyzed using AMOS to understand the relationships between various factors and acceptance of CBDC among Indian bankers. We collected the data from 380 bankers in two cities: Mumbai and Pune. The data was collected using the justified and snowball sampling method used in this research study. The findings show that several factors, including key factors such as Perception of Digital Currency (PDC), Financial Literacy Competency (FLC), Digital Currency Usage (DCU), and Digital Technology and Adoption (DTA), play a significant role in affecting Intention to Use (ITU) CBDC. The study finds a significant positive relationship between all these factors and the intention to use them. The implications of this research study are important for policymakers, central banks, and financial institutions aiming to implement CBDC initiatives and launch CBDC design frameworks. By accepting the factors, participants can make strategies to promote CBDC adoption, address the potential concerns, and leverage the transformative potential of digital currencies in India's financial ecosystem.

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem.