• DocumentCode
    3413759
  • Title

    Analytics and algorithms for geometric average trigger reset options

  • Author

    Tian-Shyr Dai ; Chen, I-Yuan ; Fang, Yuh-Yuan ; Lyuu, Yuh-Dauh

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • fYear
    2003
  • fDate
    20-23 March 2003
  • Firstpage
    55
  • Lastpage
    62
  • Abstract
    The geometric average trigger reset option resets the strike price based on the geometric average of the underlying asset´s prices over a monitoring window. This paper derives an analytic formula and two numerical methods for pricing this option with multiple resets. The analytic formula in fact is a corollary of a general formula that holds for a large class of path-dependent options: It prices any option whose payoff function can be written as eb·X1{X∈A}. For general American-style reset options, an O(n4h2)-time algorithm on n-period binomial lattice is presented. A much more efficient O(n3 hm)-time algorithm prices European-style reset options. Monte Carlo simulation suggests that the European-style geometric average trigger reset option and the arithmetic version have similar option values. This implies that results in this paper give tight prices for the difficult arithmetic version.
  • Keywords
    Monte Carlo methods; computational complexity; costing; financial data processing; stock markets; Monte Carlo simulation; arithmetic version; binomial lattice; complexity; geometric average trigger reset options; monitoring window; numerical methods; option pricing; strike price; Algorithm design and analysis; Arithmetic; Computer science; Computerized monitoring; Finance; Investments; Lattices; Monte Carlo methods; Pricing; Protection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
  • Print_ISBN
    0-7803-7654-4
  • Type

    conf

  • DOI
    10.1109/CIFER.2003.1196242
  • Filename
    1196242