In this paper, we examine the Moderating Role of Governance on the Relationships between social inclusion (SI), Information and communication technology infrastructure (ICT), and financial inclusion (FI) in 46 countries representing a global sample span from 2010 to 2020. We collect the data from the IMF's financial access survey and construct a multidimensional FI index. Based on the FI index, we divide the sample into two sub-samples (med-high level and low-level FI countries). For the empirics, we employed panel-corrected standard errors, fully modified ordinary least squares and dynamic ordinary least squares techniques. We find that SI is negatively related to FI. ICT infrastructure positively influences FI. Further, we find that governance with sound ICT infrastructure and socially inclusive communities enhances FI. The findings of sub-samples are similar to the full sample results except for a promoting effect of SI and governance in the case of med-high financially inclusive economies. Moreover, the Interaction term of governance and ICT infrastructure is insignificant in med-high financially inclusive economies and negatively significant in low financially inclusive economies. Our study reports novel findings which have significant implications for policymakers and financial institutions to effectively develop and implement new policies which strengthen the institutional base, develop digital banking infrastructure, enhance SI to boost up FI and ensure sustainable economic growth.

In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions.

In this paper, we examine whether jumps matter in both equity market returns and integrated volatility. For this purpose, we use the swap variance (SwV) approach to identify monthly jumps and estimated realized volatility in prices for both developed and emerging markets from February 2001 to February 2020. We find that jumps arise in all equity markets; however, emerging markets have more jumps relative to developed markets, and positive jumps are more frequent than negative jumps. In emerging markets, the markets with average volatility earn higher returns during jump periods; however, highly volatile markets earn higher returns during jump periods in developed markets. Furthermore, markets with low continuous returns and high volatility are more adversely affected during periods of negative jumps. The average ratio of jump variations to total variation shows considerable variations due to jumps. Integrated volatility is high during periods of negative jumps, and this pattern is consistent in both developed and emerging markets. Moreover, the peak volatility of stock markets is observed during periods of crises. The implication of this study is useful in the asset pricing model, risk management, and for individual investors and portfolio managers for both developed and emerging markets.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.