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Article
Publication date: 28 August 2024

Kithsiri Samarakoon and Rudra P. Pradhan

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

Abstract

Purpose

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

Design/methodology/approach

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

Findings

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

Practical implications

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

Originality/value

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

Research highlights

 

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Details

Managerial Finance, vol. 50 no. 12
Type: Research Article
ISSN: 0307-4358

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