Friend or Foe of Efficiency? The Impact of Algorithmic Trading on Price Discovery in Indian Base Metal Futures

Abstract

In this study, we introduce innovative methodologies to assess market-specific contributions to the process of price discovery within non-overlapping sequential markets, utilizing a structural Vector Autoregressive (VAR) model. The application of these methods is demonstrated through an empirical analysis focused on the trading dynamics of the futures and spot markets within the Indian base metal commodity market, spanning an extensive eight-year period.

Our findings reveal a prevailing dominance of price discovery by the futures market in the base metal sector, emphasizing its significant role in shaping market dynamics. Moreover, our investigation underscores the positive influence of algorithmic trading on the price discovery process, highlighting its role in enhancing market efficiency and contributing to a more robust discovery mechanism.

This study not only provides valuable insights into the intricate dynamics of base metal markets but also introduces a methodological framework that can be applied more broadly to assess the interplay of sequential markets in the context of price discovery. The implications of our research extend beyond the specific market examined, contributing to the broader understanding of the impact of algorithmic trading on market dynamics and efficiency.