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Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India

Author

Listed:
  • Gupta Kapil

    (Punjab institute of Management (PIM), India)

  • Kaur Mandeep

    (Punjab institute of Management (PIM), India)

Abstract

The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 – June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington’s Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness.

Suggested Citation

  • Gupta Kapil & Kaur Mandeep, 2015. "Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India," South East European Journal of Economics and Business, Sciendo, vol. 10(2), pages 69-88, December.
  • Handle: RePEc:vrs:seejeb:v:10:y:2015:i:2:p:69-88:n:3
    DOI: 10.1515/jeb-2015-0009
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    More about this item

    Keywords

    Hedge ratio; hedge horizon; financial crisis; heteroscedasticity; conditional volatility;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • N25 - Economic History - - Financial Markets and Institutions - - - Asia including Middle East
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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