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Options Pricing by Monte Carlo Simulation, Binomial Tree and BMS Model: a comparative study of Nifty50 options index

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  • Ali Bendob

    (Institute of economic sciences, management, and commercial sciences, LMELSPM Laboratory at university center of Ain Temouchent)

  • Naima Bentouir

    (Institute of economic sciences, management, and commercial sciences, LMELSPM Laboratory at university center of Ain Temouchent)

Abstract

Investment behaviour, techniques and choices have evolved in the options markets since the launch of options trading in 1973. Today, we are entering the field of Big Data and the explosion of information, which has become the main feature of science, impacts investors' decisions and their trading position, particularly in the financial markets. Our paper aims to testing the effectiveness of the most popular options pricing models , which are the Monte Carlo simulation method, the Binomial model, and the benchmark model; the Black-Scholes model, when we ignore/take on account the Moneyness categories and different time to maturities; five months, one year, and two years, in addition to comparing these models, we will then test the effect of each model on the prediction of the current options prices, using the regression analysis, and the Nifty50 option index during the period of 25/07/2014 to 30/06/2016. The result shows that all models are overpriced in all Moneyness categories with a high level of volatility in In-the money category, other finding concludes that the Monte Carlo Simulation method is outperforming when the volatility is lower, while the Black-Sholes model and the Binomial model are outperforming in the entire sample with ignoring the Moneyness.

Suggested Citation

  • Ali Bendob & Naima Bentouir, 2019. "Options Pricing by Monte Carlo Simulation, Binomial Tree and BMS Model: a comparative study of Nifty50 options index," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(11), pages 79-95, January.
    • Handle: RePEc:sgm:jbfeuw:v:1:y:2019:i:11:p:79-95
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    References listed on IDEAS

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    1. Jying-Nan Wang & Hung-Chun Liu & Lu-Jui Chen, 2017. "On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(9), pages 133-136, September.
    2. Poon, Ser-Huang, 2005. "Asset Pricing in Discrete Time: A Complete Markets Approach," OUP Catalogue, Oxford University Press, number 9780199271443.
    3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    4. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
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    Cited by:

    1. Shvimer, Yossi & Herbon, Avi, 2020. "Comparative empirical study of binomial call-option pricing methods using S&P 500 index data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    2. Ramona Birau & Jatin Trivedi & Cristi Spulbar, 2021. "Estimating Volatility and Investment Risk: An Empirical Case Study for NIFTY MIDCAP 50 Index of National Stock Exchange (NSE) in India," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 691-696, August.

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    More about this item

    Keywords

    options pricing; option markets; Black-Scholes model; Binomial model; Monte-Carlo Simulation model; Greek letters;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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