Price Discovery Process in Spot and Futures Markets in India: Evidence from Nifty and Bank Nifty Index

Abstract

Purpose: This study examines dynamic nature of relationship among spot (cash) market and its respective futures markets of Nifty & Bank Nifty indices on the Indian stock exchange, with a focus on identifying long-term equilibrium and the direction of price discovery.
Methodology: Using daily data from 1stJanuary 2017, to 31stDecember, 2021, the analysis is structured in three stages. First, stationarity of the data is evaluated using Augmented Dickey-Fuller, and further confirmed using Phillips–Perron methods. Second, cointegration analysis is conducted using both Engle and Granger residual-based approach and Johansen-Juselius approach to assess short-run and long-term co-movements between stock prices in spot market and futures prices. Ultimately, temporal relationships and adjustment rates for price imbalances are examined through Vector Error Correction Approach(VECM) methodology.
Results: The findings validate extensive long-run cointegration between spot and futures market for both indices, reflecting strong integration of the markets. The VECM estimates also confirm that futures market has leading role in pricing process since it reacts faster than the spot market. This suggests futures segment drives the cash market in absorption of information, hence improving overall market efficiency.
Conclusions: The research reaffirms the pivotal role of futures market towards guiding direction of price movements in the spot market prices and justifies the efficient market hypothesis in the Indian context. It underscores the maturity and responsiveness to information of India’s derivatives market, particularly with regard to the Nifty and Bank Nifty indices.
Implications: For traders and hedgers, the futures market serves as a key signal for timing positions. Institutional investors can use these findings to refine hedging and arbitrage strategies. Regulators should focus on ensuring market liquidity and transparency to support continued efficiency.
Future Directions: Future research could incorporate high-frequency intraday data and modern techniques such as GARCH models, regime-switching frameworks, or machine learning to better capture short-term volatility and evolving price dynamics in Indian financial markets.