International Journal of Business Analytics and Intelligence

Mandeep Kaur ¹, Kapil Gupta ¹

Affiliations
1 Department of Management, I. K. Gujral Punjab Technical University, Kapurthala, Punjab, IN

Abstract

Present study estimates the hedging effectiveness by applying variance-reduction framework and risk-return framework using near month contracts of three benchmark indices (NIFTY50, NIFTYIT, and BANKNIFTY) traded at National Stock Exchange of India (NSE) for the sample period from June 2000 to March 31, 2017 by using nine optimal hedge ratio models. Out of these nine models, six are constant hedging models and three are time-varying hedging models. The study finds that using variance-reduction framework, highest hedging effectiveness is achieved using Ordinary Least Square model; whereas, 1:1 naïve hedge ratio gives lowest hedging effectiveness. On the other hand, when hedging effectiveness is estimated in a risk-return framework, naïve hedge ratio gives highest hedging effectiveness; whereas, OLS gives the least estimate. Secondly, the coefficients of both optimal hedge ratio as well as hedging effectiveness have increased during post-crisis period implying an increase in the cost of hedging. These findings suggests that conventional hedging models are more efficient than highly complicated time-varying hedging models for estimating optimal hedge ratio, these findings are consistent with the findings of Lien (2005), Bhaduri and Durai (2007), Bhargava (2007), Mandal (2011), Wang et al. (2015).

Keywords

Hedging Effectiveness, Optimal Hedge Ratio, Equity Futures Market, Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH), Constant Hedge Ratio, Time-Varying Hedge Ratio.

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Anju Bala ¹, Kapil Gupta ²

Affiliations
1 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, IN
2 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, IN

Abstract

This paper tests the existence of long term memory with reference to structural changes/breaks in Indian Stock Market. Furthermore, the present paper applied Hurst Exponent in Rescaled Range Analysis as suggested by Hurst (1951) and Lo (1991) and structural breaks detected by using Multiple Break Test (Balcilar et al., 2015) by using daily returns of sectoral indices from January 2010 to May 2018. Empirical evidence shows the predictable structure in all sectoral indices (2010-2018) except Nifty Private Bank with H value 0.4972. The findings imply that existence of long memory would be useful for the investors, practitioners, academicians, and policymakers.

Keywords

Emerging Market, Long Term Memory, Hurst Exponent, Structural Breaks, Market Efficiency.

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