Real-Time Pothole Detection Using YOLOv8 for Smart Road Monitoring Systems

The empirical analysis provides strong evidence of interconnectedness and asymmetric spillover effects among the selected global equity markets comprising the S&P 500, Nifty 50, DAX 30, Nikkei 225, and Hang Seng indices. Based on the Akaike Information Criterion (AIC), a VAR(3) model was selected as the optimal specification because it captures both immediate and lagged interactions among the markets, thereby providing a reliable framework for measuring return and volatility connectedness. The estimated BEKK-GARCH model indicates that volatility transmission is not uniform across markets. The strongest and statistically significant spillover is observed from the United States to India, with a coefficient of 0.312, followed by the spillover from the United States to Japan (0.285). These results confirm the dominant role of the U.S. equity market in transmitting financial shocks to other international markets. Germany also exerts a significant influence on the Indian equity market, with a spillover coefficient of 0.198, while the Hang Seng market generates a moderate spillover effect toward India (0.155). Conversely, the spillover from India to the United States is relatively weak (0.102), indicating that the Indian market primarily absorbs volatility originating from developed markets rather than influencing them. These findings suggest an asymmetric pattern of volatility transmission in which developed markets act as major sources of financial shocks, whereas emerging markets are more vulnerable to external disturbances. The Diebold–Yilmaz Spillover Index further supports these findings by demonstrating a high degree of market connectedness. The Total Spillover Index of 65.5% indicates that a substantial proportion of market volatility is explained by shocks originating from other international markets rather than domestic factors alone. The directional spillover results show that the United States contributes 28.3% of volatility transmitted to other markets, confirming its position as the principal transmitter within the global financial network. In comparison, India receives 18.7% of directional spillovers from external markets, highlighting its growing integration with global financial markets while simultaneously reflecting its greater sensitivity to international shocks. The generalized forecast error variance decomposition results reveal that although more than 99% of the forecast error variance of each market is explained by its own innovations, statistically significant cross-market spillovers continue to exist. Among the selected markets, the Hang Seng Index records the highest net spillover value (0.79), followed by the S&P 500 (0.59) and DAX (0.36), suggesting that these markets play relatively stronger roles in transmitting information and volatility to other markets. The Nifty 50 exhibits the lowest net spillover value (0.05), indicating that it functions primarily as a receiver of external shocks rather than as a significant source of volatility transmission. Overall, the empirical findings clearly demonstrate that the selected equity markets are highly integrated and exhibit asymmetric return and volatility spillover behaviour. The magnitude and direction of spillovers differ considerably across markets, with developed economies, particularly the United States, exerting a stronger influence on global financial dynamics than emerging markets. These results support the argument that financial markets react differently to favourable and unfavourable information and that international shock transmission is characterized by unequal directional effects. Consequently, the null hypothesis stating that there is no significant asymmetric return spillover among the selected equity market indices is rejected, while the alternative hypothesis is accepted. The statistical evidence obtained from the BEKK-GARCH estimates and the Diebold–Yilmaz connectedness measures confirms the existence of significant asymmetric return and volatility spillovers among the selected global equity markets. The findings imply that international investors should consider cross-market interconnectedness when constructing globally diversified portfolios, as shocks originating in major developed markets can rapidly influence the performance of emerging equity markets. Similarly, policymakers and financial regulators should continuously monitor developments in major global financial centres to strengthen risk management practices and enhance financial stability. The study therefore concludes that asymmetric connectedness is an important characteristic of modern global equity markets, with developed markets serving as dominant transmitters of financial shocks and emerging markets functioning predominantly as recipients of international volatility.

Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171. Diebold, F. X., & Yilmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting. Diebold, F. X., & Yilmaz, K. (2015). Financial and Macroeconomic Connectedness. Oxford University Press. Baruník, J., Kočenda, E., & Vácha, L. (2016). Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers. Journal of Financial Markets, 27, 55–78. Baruník, J., & Křehlík, T. (2018). Measuring the Frequency Dynamics of Financial Connectedness. Journal of Financial Econometrics. Pesaran, M. H., & Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity. Econometrica. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics.