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This study investigates how geopolitical risk moderates the relationship between investor attention and stock market volatility in both emerging and developed markets. To achieve this objective, the GARCH-MIDAS model is employed to decompose daily market returns into short- and long-term volatility components. The Google Search Volume Index is used as a proxy for investor attention along with country-specific geopolitical risk indices which are assessed for the period spanning from 2014 to 2023. The findings reveal that the effect of investor attention on stock market volatility is not uniform but instead varies across and within developed and emerging markets. Furthermore, GPR is found to be a crucial factor that can either weaken, magnify, or change the nature of the relationship between investor attention and volatility depending on the surveyed economy. The findings highlight the critical role of geopolitical risk in shaping market dynamics, suggesting that both investors and policymakers should incorporate geopolitical factors in risk management and portfolio strategies. The study contributes to existing literature by providing a novel perspective on how geopolitical risk moderates the relationship between investor attention and market volatility, utilizing a comparative analysis across different economic regions, which has not been done before.

This study empirically investigates hierarchical endogeneity and shock transmission among three major international stock markets the S&P 500, FTSE 100, and Nikkei 225 from 2010 to 2023. Utilizing a Vector Autoregression framework and Granger causality test, the research examines the direction and strength of cross-market dependencies. The findings reveal a clear, asymmetric structure where the US market acts as the primary exogenous driver, with unidirectional causality flowing from the S&P 500 to both the FTSE 100 and Nikkei 225. A secondary channel of influence from the UK to Japan was also identified. Forecast error variance decomposition confirms the US market's dominance, explaining over 25% of the movements in the UK's financial market and 18.7% of Japan's forecast error variance, while remaining largely insulated from feedback. The findings of this study suggest that international markets are characterized by hierarchical endogeneity, challenging notions of symmetric interdependence and highlighting significant implications for financial stability frameworks making a noteworthy contribution.

Long memory suggests that past returns can influence future returns over extended periods, indicating potential predictability. The aim of this study was to empirically examines the interplay between long memory effects, downside risk, and stock returns across five major international equity markets (S&P 500, FTSE 100, Nikkei 225, DAX, and ASX) from 2000 to 2022. An ARFIMA model which quantify fractional differencing was used in conjunction with a correlation matrix, regression and Granger causality test to investigate the interplay. The findings revealed a significant long memory across all markets, with Nikkei 225 exhibiting the strongest persistence, attributed to Japan’s prolonged economic stagnation. Counterintuitively, lagged downside risk positively predicts returns, aligning with behavioural theories of investor overreaction to tail events. Bidirectional causality between risk and returns underscores reflexive market dynamics. The study bridges a critical gap by integrating long memory into downside risk models, demonstrating that persistent volatility amplifies tail risk premiums. The findings of this study suggest that financial market regulators need to seriously considers adaptive regulatory tools due to the dual long memory in returns and downside risk necessitates. To the author´s knowledge, this study is the first to bridge a critical gap in the literature by systematically integrating long memory effects with downside risk metrics so as to provide a more robust framework for understanding risk-return dynamics, as it accounts for the persistent nature of volatility and tail risk, which traditional short-memory models often overlook.

Prior research on volatility spillover indicates that financial market spillovers are not uniform across all markets. Their intensity and direction depend on factors such as market size, economic linkages, and the nature of the shock. The aim of this study was to comprehensive investigation of volatility spillovers across five major international equity markets from January 2008 to January 2024, a period encompassing multiple financial crises. Utilizing a conditional volatility model, followed by the construction of a spillover index and Granger causality tests to investigate the direction and magnitude of volatility transmission, the findings of this study revealed that the S&P 500 is a net receiver of volatility, acting as a global shock absorber rather than a primary transmitter. In contrast, European markets and the Nikkei 225 emerged as net transmitters, with a tightly coupled, bidirectional relationship observed between the United Kingdom and European markets. The Shanghai Composite was also found to be nearly neutral, likely due to capital controls. These results reiterated the notion that volatility transmission networks are dynamic and not solely dictated by market size. The findings of this study suggest that asset managers and other investors can significantly increase the value of their portfolios if they adopt a multi-polar risk management framework, moving beyond a United States centric view for international diversification to include more European and Asian markets assets in their portfolios.

Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models often do not capture the inherent nonlinearities that exist between them. The aim of this study was to examine the interconnectedness of global financial markets using a multivariate Granger causality framework, focusing the United States, Europe, Asia, and emerging markets. Daily closing share prices spanning 13 years were utilised (January 2010 to December 2023) to analyze the shock transmission dynamics. The findings revealed unidirectional causality from the U.S. to European, Japanese, and Emerging markets, underscoring its dominant role in global financial networks. Conversely, European and Asian markets exhibit no reverse causality on the United States, highlighting asymmetrical interdependence. Notably, China’s Shanghai Composite Granger-causes Emerging market returns, reflecting its rising influence. These insights challenge conventional models that underestimate multilateral linkages, demonstrating that financial integration has intensified bidirectional interactions between Emerging markets and developed markets. By implication, investors should reassess exposure to dominant markets like the U.S. and China’s growing sway over Emerging economies while policymakers should prioritize cross-border spillover monitoring and systemic risk frameworks to address asymmetrical dependencies. Also, institutions must adopt nonlinear models to better capture shock transmission and evolving interdependencies, enhancing resilience against contagion.

The aim of this study was to investigate phase distribution and phase correlation dynamics across international financial markets to uncover cyclical patterns, synchronization, and contagion effects. The sample financial markets were the S&P 500, DAX, Nikkei 225, FTSE 100, Shanghai Composite, BSE Sensex with daily closing prices ranging from 2018–2023. Using the Hilbert-Huang Transform complemented by Phase Concentration Index, Kuiper tests, and Granger causality, the results reveal distinct phase clustering in the Shanghai and BSE Sensex. Developed markets exhibit lower volatility clustering (S&P 500: 28% high-volatility phases) but stronger cross-market synchronization (S&P-DAX: 0.82 correlation), underscoring their interconnectedness. The S&P 500 emerges as a global anchor, Granger-causing European and Asian markets with 1.8–3.5-day lags. Crisis periods, particularly COVID-19, amplify synchronization (average correlation=0.92), while non-crisis phases show market-specific volatility (correlation=0.65). Emerging markets demonstrate regional co-movement (Shanghai-BSE: 0.73), offering diversification opportunities. The findings highlight the hierarchical structure of global markets, where developed economies drive cycles, while emerging markets remain prone to instability. This study advances phase-based methodologies as tools for systemic risk monitoring, providing insights into crisis contagion and investor strategies balancing short-term synchronization with long-term diversification.

Financial markets areconstantly fluctuating in response to a range of factors. These fluctuations connote volatility which are unpredictable behaviour that deviates significantly from the patterns observed in stable conditions. It is therefore important to use standard stochastic volatility models to capture the time-varying nature of volatility during periods of financial distress in other to make informed decisions. The aim of this study was to explore stochastic volatility in financial markets during periods of financial distress using a Heston model. The sample financial markets were, the CAC 40, the DAX, the JSE, the Nasdaq Index and the Nikkei-225. The 2007/2008 financial crisis and the Covid-19 pandemic were used as sample time frames. The findings of this study revealed extortionate stochastic volatility during the Covid-19 pandemic compared to the 2007/2008 financial crisis. Since the 2007/ 2008 crisis stemmed from a collapse in the financial system, the nature of the event was more localized within the financial sector and had a somewhat more predictable unfolding than the Covid-19 pandemic. By Implication, it may be suggested that it was more difficult to price assets during the Covid-19 pandemic due to extreme uncertainty which resulted in inflated option premiums as investors sought hedging strategies.

Stock return modeling is essential for active investors and market players. Value and risk analysis benefit from these simulations. Despite extensive study on stock price modeling, little is understood about how internal and external shocks affect stock market returns. Therefore, this study sought to fill this gap. A sample of five international financial markets from December 1, 2007, to June 30, 2009 and January 1, 2020 to December 31, 2021, the 2007-2008 financial crisis and most recent COVID-19 pandemic, were tested using a Cramer-von Mises and Watson test. Research showed that internal market volatility is more damaging than external shocks. During financial instability driven by external shocks, portfolio managers should expect two to three standard deviations of volatility. However, financial system shocks should cause a greater range of volatility. The authors believe this is the first study to predict stock market returns in reaction to regulatory announcements from market shocks.

Using the stock index data of financial sector spanned from January 2, 2009 to December 31, 2014, this study examines the effects of some policies on stock returns and volatility in Vietnamese stock market. The empirical results of EGARCH model reveal that two policies, namely, M&A and VAMC have an significantly positive impact on stock returns but they do not represent any effects on stock volatility. The third policy, regulatory reform, does not show any affection on stock return but it has an impact on the stock volatility. It implies that investors should adjust and alter their portfolio accordingly when changing policies. Besides, policymaker needs to know when they should prioritize which policy to be issued because some policies sometimes can hurt the stock market if the stock market is efficient.