Abstract
The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.
Highlights
Futures trading plays an important role in the price discovery process
This study focuses on the volatility facet of Nifty Index futures traded on the National Stock Exchange of India (NSE), and analyzes different categories of traders who trade Nifty Index futures
In order to understand how volatility responds to volume and open interest, we further analyze our work with a three-variable vector autoregressive (VAR) model of volume, open interest and volatility for individual and all other categories
Summary
Futures trading plays an important role in the price discovery process. Volatility in relation to other liquidity variables, such as volume and open interest, is of prime importance to hedgers, arbitrageurs and speculators for developing trading strategies. Girma and Mougoue (2002) opine that factors other than volume affect the persistence of volatility in the futures price they studied, and inefficiency could be due to the fact that futures traders base their prices on the previous day’s trading volume and open interest as a measure of both market consensus and market depth. The first objective is to find a best fit model for the different categories of investors who trade Nifty Index futures so that shocks to the uncertain variance are not firm after considering volume and open interest. GARCH models are fitted to understand the extent to which previous positive and negative shocks affect the trading dynamics of specific traders by capturing specific details of volatility volume and open interest. The paper is further organized in the following manner: Section 2 summarizes the literature reviewed; Section 3 provides the data description; Section 4 provides model development and observed findings, and Section 5 concludes
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