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A novel data-driven neuro arch (DDNA) model for option pricing on cloud

Journal of Banking and Financial Technology volume 5, pages 89–103 (2021)Cite this article

Abstract

Unpredictable nature of financial derivatives market has always been a challenge for investors to profit from these markets. It is known that computing volatility of underlying assets is a source of this challenge. In this work, we present an application of our data-driven neuro arch (DDNA) volatility model that is driven by the market data of the stock prices to price options. That is, we use the volatility forecast from our DDNA model along with the Monte Carlo (MC) simulations to compute option prices. Since the MC method requires a large number of simulations for better precision, we implement the proposed model on two cloud resources (Amazon’s EMR and Google’s Cloud DataProc) using the Hadoop MapReduce paradigm. Also, since the MC strategy is prone to errors due to uncertainties and random numbers, we propose to generate a fuzzified range of option prices instead of a single crisp option value to minimize these errors. Our experimental configuration consists of c3.xlarge instances and n1-standard-4 instances, both having 4 vCores, for EMR and GDP respectively. For a largest number of 10 million simulations on 40 VMs, the option pricing results (computed in 39 and 33 s respectively on EMR and GDR) are close to the last traded option value found on option chain tables with an error of 0.00101 (0.1%). The proposed DDNA model for forecasting volatility together with MC option pricing model implemented on MapReduce outperforms the existing option pricing models in terms of efficiency and accuracy. This proposed strategy could be used by investors for computing option prices precisely with relative ease, allowing them to value the numerous available option contracts for their investment decisions.

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References

  1. Hull J (2017) Options, futures, and other derivatives, 10th edn. Pearson, London

    MATH  Google Scholar 

  2. Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Political Econ 81(3):637–654

    MathSciNet  Article  Google Scholar 

  3. Boyle PP (1977) Options: a Monte Carlo approach. J Financial Econ 4(3):323–338

    Article  Google Scholar 

  4. Heston SL, Nandi S (2000) A closed-form GARCH option valuation model. Rev Financial Stud 13(3):585–625

    Article  Google Scholar 

  5. Thavaneswaran A, Thulasiram RK, Frank J, Zhu Z, Singh M (2019) Fuzzy option pricing using a novel data-driven feed forward neural network volatility model. In: 2019 IEEE international conference on fuzzy systems (FUZZ-IEEE). pp 1–6

  6. Efron B, Hastie T (2016) Computer age statistical inference, vol 5. Cambridge University Press, Cambridge

    Book  Google Scholar 

  7. Acworth PA, Broadie M, Glasserman P (1998) A comparison of some Monte Carlo and quasi Monte Carlo techniques for option pricing. In: Niederreiter H, Hellekalek P, Larcher G, Zinterhof P (eds) Monte Carlo and Quasi-Monte Carlo Methods 1996. Springer, New York, pp 1–18

    MATH  Google Scholar 

  8. Xu Y, Qu W, Li Z, Min G, Li K, Liu Z (2014) Efficient \(k\) -means++ approximation with mapreduce. IEEE Trans Parallel Distrib Syst 25(12):3135–3144

  9. Zhang Y, Gong B, Ying P, Hui L (2011) Parallel option pricing with BSDEs method on MapReduce. In: IEEE international conference on computer research and development (ICCRD 2011), vol. 1, pp. 289–293, Shanghai, China

  10. Google (2019) Cloud Dataproc-Google Cloud. [Online] https://cloud.google.com/dataproc/. Accessed 1 Apr 2019

  11. Amazon. (2019) Amazon EMR-Amazon Web Services. [Online] https://aws.amazon.com/emr/. Accessed 1 Apr 2019

  12. Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: USENIX sixth symposium on operating system design and implementation (OSDI’04), San Francisco, USA, pp 137–150

  13. Taylor RC (2010) An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinform 11(12):1–6

    Article  Google Scholar 

  14. Apache Hadoop. [Online] https://hadoop.apache.org/. Accessed 1 Apr 2019

  15. Hamid SA, Iqbal Z (2004) Using neural networks for forecasting volatility of S&P 500 index futures prices. J Bus Res 57(10):1116–1125

    Article  Google Scholar 

  16. Kourentzes N, Barrow DK, Crone SF (2014) Neural network ensemble operators for time series forecasting. Expert Syst Appl 41(9):4235–4244

    Article  Google Scholar 

  17. Arratia A (2014) Computational finance an introductory course with R, vol 1, 1st edn. Atlantis Press, Paris

    MATH  Google Scholar 

  18. Ciaburro G, Venkateswaran B (2017) Neural networks with R: smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing, Birmingham

    Google Scholar 

  19. Ripley B, Venables W (2016) Package ‘nnet’ - cran.r-project.org

  20. Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Wang E, Yasmeen F (2018) Package ‘forecast’- the comprehensive R archive network

  21. Fritsch S, Guenther F, Suling M, Mueller SM (2016) Training of neural networks [R package neuralnet version 1.33]

  22. Kourentzes N (2017) Package ‘nnfor’-the comprehensive R archive network

  23. Sheela KG, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013. https://doi.org/10.1155/2013/425740

  24. Dubois D, Prade H (2012) Possibility theory. Springer, New York, pp 2240–2252

    MATH  Google Scholar 

  25. Carlsson C, Fullér R (2003) A fuzzy approach to real option valuation. Fuzzy Sets Syst 139:297–312

    MathSciNet  Article  Google Scholar 

  26. Thavaneswaran A, Appadoo S, Paseka A (2009) Weighted possibilistic moments of fuzzy numbers with applications to garch modeling and option pricing. Math Comput Model 49(1):352–368

    MathSciNet  Article  Google Scholar 

  27. Thavaneswaran A, Thiagarajah K, Appadoo SS (2007) Fuzzy coefficient volatility (FCV) models with applications. Math Comput Model 45(7–8):777–786

    MathSciNet  Article  Google Scholar 

  28. Muzzioli S, De Baets B (2017) Fuzzy approaches to option price modeling. IEEE Trans Fuzzy Syst 25(2):392–401

    Article  Google Scholar 

  29. Chrysafis KA, Papadopoulos BK (2009) On theoretical pricing of options with fuzzy estimators. J Comput Appl Math 223(2):552–566

    MathSciNet  Article  Google Scholar 

  30. Hafner CM, Herwartz H (2001) Option pricing under linear autoregressive dynamics, heteroskedasticity, and conditional leptokurtosis. J Empir Finance 8(1):1–34

    Article  Google Scholar 

  31. McLeish DL (2005) Monte Carlo simulation and finance, vol 276. Wiley, New York

    MATH  Google Scholar 

  32. Sun X-H, Gustafson JL (1991) Toward a better parallel performance metric. Parallel Comput 17(10):1093–1109

    Article  Google Scholar 

  33. Wang S, Zhou L, Li Y, Wu J (2016) Parallel monte carlo method with mapreduce for option pricing. In: IEEE Trustcom/BigDataSE/ISPA, pp. 2116–2121

  34. Pradeepkumar D, Ravi V (2017) Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Elsevier Appl Soft Comput 58:35–52

    Article  Google Scholar 

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Acknowledgements

The first author acknowledges the financial support from Faculty of Science and University of Manitoba during his graduate program in Computer Science. The second and third authors acknowledge the Discovery Grant from Natural Sciences and Engineering Research Council (NSERC) Canada and Faculty of Science (University of Manitoba) InterDisciplinary Research Grant that supported this research study.

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Authors and Affiliations

  1. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Manmohit Singh

  2. Department of Statistics, University of Manitoba, Winnipeg, MB, Canada

    Ruppa K. Thulasiram & A. Thavaneswaran

Authors
  1. Manmohit Singh
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  2. Ruppa K. Thulasiram
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  3. A. Thavaneswaran
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Correspondence to Ruppa K. Thulasiram.

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Singh, M., Thulasiram, R.K. & Thavaneswaran, A. A novel data-driven neuro arch (DDNA) model for option pricing on cloud. J BANK FINANC TECHNOL 5, 89–103 (2021). https://doi.org/10.1007/s42786-021-00032-7

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  • DOI: https://doi.org/10.1007/s42786-021-00032-7

Keywords

  • Data-driven application
  • Neuro ARCH model
  • Financial derivatives
  • Fuzzy option pricing
  • MapReduce approach
  • Amazon EMR
  • Google Dataproc