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Pricing and hedging competitiveness of the tree option pricing models: Evidence from India

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  • Vipul Kumar Singh

    (National Institute of Industrial Engineering (NITIE))

Abstract

The study focusses on the price convergences, implied volatility structures, and hedging competencies of the discrete tree options pricing models. In addition, the paper also studies the efficiency of optimization techniques involved in the calibration of discrete time models. The process involves matching of quoted market option prices with the model prices – relied on the assumption that there are sufficiently large numbers of liquidly traded options. To minimize the price bias between the model and market, this paper uses Differential Evolution and Simulated Annealing methods of optimization. To benchmark, price statistics of European-style Nifty index options and American-style stock options traded on the National Stock Exchange (the world’s third largest Futures and Options exchange) of India have been used. The concepts of optimization and relative pricing are used to demonstrate the price effectiveness of the tree models with respect to continuous-time classical options pricing model of Black–Scholes–Merton. The outcome of the research reveals that the pricing and hedging errors of forward-inducted implied that binomial and trinomial trees are stable and lower with Simulated Annealing methods of optimization than their backward-inducted counterparts.

Suggested Citation

  • Vipul Kumar Singh, 2016. "Pricing and hedging competitiveness of the tree option pricing models: Evidence from India," Journal of Asset Management, Palgrave Macmillan, vol. 17(6), pages 453-475, October.
    • Handle: RePEc:pal:assmgt:v:17:y:2016:i:6:d:10.1057_s41260-016-0024-5
    DOI: 10.1057/s41260-016-0024-5
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    binomial; Black–Scholes; implied volatility; Nifty index; options; optimization; trinomial;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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