• BHANU Rawat
• DR. LOTICA SURANA

This study evaluates the interrelationships among major global stock indices, specifically FTSE 100, Hang Seng, Karachi, NASDAQ, Nikkei 225, and Sensex, through a comprehensive econometric analysis. The model fit analysis reveals that while the R-squared values are relatively low, indicating limited explanatory power, the overall model significance is supported by the F-statistic, and the sum of squared residuals suggests a decent fit. Coefficient significance, assessed through t-statistics, identifies several statistically significant relationships at the 5% level, necessitating a detailed examination of individual coefficient signs and magnitudes for precise interpretation.Granger causality tests uncover notable predictive relationships, such as NASDAQ and Sensex indices Granger causing the FTSE 100, highlighting their influence on the UK market. However, the lack of reciprocal causality indicates complex, non-bidirectional interactions among the indices. Variance decomposition further elucidates these dynamics, showing the extent to which the variance in each index is attributable to its own past values versus the past values of other indices over various time horizons. These findings contribute to a deeper understanding of the interconnectedness and predictive dynamics among key global stock markets, providing valuable insights for investors and policymakers.

Granger causality, Global stock indices, Variance decomposition, Econometric analysis, Predictive relationships.

"Analyzing the Interdependence between Key European Community Stock Markets and the BSE Sensex: A Vector Autoregression Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.g522-g543, June-2024, Available :http://www.jetir.org/papers/JETIRTHE2131.pdf

"Analyzing the Interdependence between Key European Community Stock Markets and the BSE Sensex: A Vector Autoregression Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppg522-g543, June-2024, Available at : http://www.jetir.org/papers/JETIRTHE2131.pdf