Impact of exchange rate fluctuations on Nifty bank and FinServ indices: A financial modelling perspective

  • 20 Views
  • 2 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
This study examines the impact of exchange rate fluctuations on banking and financial service indices in India. To validate this, five exchange rates are considered based on their relative share in the total foreign remittance inflows to India, viz., Arabian Dirham (AED/INR), Great Britain Pound (GBP/INR), Saudi Riyal (SAR/INR), Singapore Dollar (SGD/INR), and US Dollar (USD/INR). The study includes daily data of a decade (2015–2025), and employs various econometric techniques such as ADF test, Johansen cointegration, Vector Error Correction Model (VECM), and Impulse Response Function (IRF) for the analysis. The Johansen cointegration test indicates a long-run relationship between exchange rates and both the sectoral indices, as the probabilities are less than 0.05. The VECM analysis for both the Nifty Bank and Nifty FinServ identified USD/INR (2,308.66; 2,257.58) and SAR/INR (373.25; 360.73) as the dominant long-term drivers, whereas AED/INR (–2,671.406; –2,608.011) acts as a persistent structural anchor with a negative influence. In the short run, shocks in USD/INR and SGD/INR generate immediate positive effects, whereas volatility in AED/INR and SAR/INR leads to temporary negative deviations before the system converges back to the equilibrium. The impulse response function indicates that exchange rate shocks have temporary effects on both the indices, which dissipate quickly, reflecting rapid market adjustment and overall efficiency. The findings of this study will help policymakers to improve the exchange rate risk monitoring system and executives in banks and financial institutions to formulate their hedging strategies. For investors and portfolio managers, the findings suggest that currency movements can serve as early indicators of market fluctuations, thereby supporting more informed investment decisions.

view full abstract hide full abstract
    • Figure 1. Time series graph
    • Figure 2. Impulse response (Nifty Bank)
    • Figure 3. Impulse response (Nifty FinServ)
    • Table 1. ADF test
    • Table 2. Descriptive statistics
    • Table 3. Cointegration test
    • Table 4. Lag length criteria
    • Table 5. VECM results of Nifty Bank
    • Table 6. VECM results of Nifty FinServ
    • Conceptualization
      Amiya Kumar Mohapatra, Aditya Prasad Sahoo, Shradha Gupta, Rajesh Kumar Panda
    • Formal Analysis
      Amiya Kumar Mohapatra, Debasis Mohanty, Rajesh Kumar Panda
    • Investigation
      Amiya Kumar Mohapatra, Debasis Mohanty, Aditya Prasad Sahoo, Shradha Gupta
    • Methodology
      Amiya Kumar Mohapatra, Debasis Mohanty
    • Project administration
      Amiya Kumar Mohapatra, Aditya Prasad Sahoo
    • Validation
      Amiya Kumar Mohapatra, Shradha Gupta, Rajesh Kumar Panda
    • Writing – original draft
      Amiya Kumar Mohapatra, Debasis Mohanty, Aditya Prasad Sahoo
    • Writing – review & editing
      Amiya Kumar Mohapatra, Debasis Mohanty, Aditya Prasad Sahoo, Shradha Gupta, Rajesh Kumar Panda
    • Data curation
      Debasis Mohanty, Aditya Prasad Sahoo
    • Software
      Debasis Mohanty, Shradha Gupta
    • Visualization
      Debasis Mohanty, Aditya Prasad Sahoo, Rajesh Kumar Panda
    • Resources
      Aditya Prasad Sahoo, Shradha Gupta, Rajesh Kumar Panda
    • Supervision
      Shradha Gupta, Rajesh Kumar Panda