Skip to main content
Log in

An efficient Monte Carlo simulation for new uncertain Heston–CIR hybrid model

  • Fuzzy systems and their mathematics
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, we consider two new stock models in which their differential equations are modeled by Liu process in uncertain environment. Firstly, we study the uncertain Schöbel–Zhu–Hull–White hybrid model and obtain its closed European call option pricing using Liu calculus. Also, we solve this model by Monte Carlo simulation to ensure the performance of Monte Carlo method. Our main purpose is to present a new model, uncertain Heston–CIR hybrid model, in which its uncertain differential equations cannot be solved and so we can calculate the option value via Monte Carlo simulation. Finally, some examples are stated for illustrating these models to obtain successful results and show the efficiency of Monte Carlo method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

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

    Article  MathSciNet  Google Scholar 

  • Chen X (2011) American option pricing formula for uncertain financial market. Int J Oper Res 8:27–32

    MathSciNet  Google Scholar 

  • Chen X, Liu B (2010) Existence and uniqueness theorem for uncertain differential equations. Fuzzy Optim Decis Mak 9:69–81

    Article  MathSciNet  Google Scholar 

  • Chen X, Liu Y, Ralescu DA (2013) Uncertain stock model with periodic dividends. Fuzzy Optim Decis Mak 12:111–123

    Article  MathSciNet  Google Scholar 

  • Christoffersen P, Heston S, Jacobs K (2009) The shape and term structure of the index option smirk: why multifactor stochastic volatility models work so well. Manag Sci 55:1914–1932

    Article  Google Scholar 

  • Fan Y, Zhang H (2014) The pricing of Asian options in uncertain volatility model. Math Probl Eng 2014:1–19

    MathSciNet  MATH  Google Scholar 

  • Francesco M, Foschi P, Pascucci A (2006) Analysis of an uncertain volatility model. J Appl Math Decis Sci 2006:1–17

    Article  MathSciNet  Google Scholar 

  • Grzelak L, Oosterlee C (2011) On the Heston model with stochastic interest rate. SIAM J Financ Math 2:255–286

    Article  MathSciNet  Google Scholar 

  • Haastrecht A, Lord R, Pelsser A, Schrager D (2009) Pricing long-maturity equity and FX derivatives with stochastic interest rates and stochastic volatility. Insur Math Econ 45:1–28

    Article  Google Scholar 

  • Heston S (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev Financ Stud 6:327–343

    Article  MathSciNet  Google Scholar 

  • Hull J, White A (1990) Pricing interest-rate derivative securities. Rev Financ Stud 3:573–592

    Article  Google Scholar 

  • Hull J, White A (1993) One factor interest rate models and the valuation of interest rate derivative securities. J Financ Quant Anal 28(235):254

    Google Scholar 

  • Liu B (2007) Uncertainty theory, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Liu B (2008) Fuzzy process, hybrid process and uncertain process. J Uncertain Syst 2:3–16

    Google Scholar 

  • Liu B (2009) Some research problems in uncertainty theory. J Uncertain Syst 3:3–10

    Google Scholar 

  • Liu B (2010) Uncertainty theory: a branch of mathematics for modeling human uncertainty. Springer, Berlin

    Book  Google Scholar 

  • Liu Y, Ha M (2010) Expected value of function of uncertain variables. J Uncertain Syst 4:181–186

    Google Scholar 

  • Peng J, Yao K (2010) A new option pricing model for stocks in uncertainty markets. Int J Oper Res 7:213–224

    Google Scholar 

  • Schobel R, Zhu J (1999) Stochastic volatility with an ornstein uhlenbeck process: an extension. Eur Finance Rev 4:23–46

    Article  Google Scholar 

  • Stein JC, Stein EM (1991) Stock price distributions with stochastic volatility: an analytic approach. Rev Financ Stud 4:727–752

    Article  Google Scholar 

  • Vasicek OA (1977) An equilibrium characterization of the term structure. J Financ Econ 5:177–188

    Article  Google Scholar 

  • Yang X, Shen S (2015) Runge–Kutta method for solving uncertain differential equations. J Uncertain Anal Appl 3:1–12

    Article  Google Scholar 

  • Yao K, Chen X (2013) A numerical method for solving uncertain differential equations. J Intell Fuzzy Syst 25:825–832

    Article  MathSciNet  Google Scholar 

  • Zhang K, Wang S (2009) A computational scheme for uncertain volatility model in option pricing. Appl Numer Math 59:1754–1767

    Article  MathSciNet  Google Scholar 

  • Zhou Q, Li X (2019) Vulnerable options pricing under uncertain volatility model. J Inequal Appl 315:1–16

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Behrouz Fathi-Vajargah.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fathi-Vajargah, B., Mirzazadeh, M. & Ghasemalipour, S. An efficient Monte Carlo simulation for new uncertain Heston–CIR hybrid model. Soft Comput 25, 8539–8547 (2021). https://doi.org/10.1007/s00500-021-05702-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05702-8

Keywords

Navigation