Exploring Exchange Rate–Stock Market Interactions: Evidence from Nifty via Toda–Yamamoto Technique

Purpose The study aims to examine the causal relationship between exchange rates and stock market indices, specifically the Nifty index, while addressing the methodological limitations of traditional Granger causality approaches. It seeks to provide a more robust framework for understanding financial market interdependencies. Research Methodology/Tool & Techniques The research utilizes 20 years of daily time-series data (3rd April 2005–31st December 2025), comprising 4,671 observations. Stationarity is tested using the Augmented Dickey–Fuller (ADF) test. The Toda–Yamamoto causality test, an extension of the Vector Autoregression (VAR) model, is applied to detect causal linkages without imposing strict stationarity requirements. Additionally, the Impulse Response Function (IRF) is employed to capture the dynamic effects of unexpected shocks. Major Findings The analysis establishes bidirectional causality between exchange rates and the Nifty index. Movements in the Nifty index exert a stronger influence on exchange rates compared to the reverse. IRF results reveal that the Nifty index responds positively to its own shocks and marginally to exchange rate shocks, while exchange rates react negatively to shocks originating from the Nifty index. These findings highlight the asymmetric nature of financial market interactions. Social Implications The study underscores the importance of robust causality testing in financial research, offering insights for policymakers, investors, and regulators. By clarifying the dynamic interplay between currency markets and stock indices, the findings can inform strategies for managing exchange rate volatility, guiding investment decisions, and shaping economic policies that strengthen financial stability.