The Classical and Stochastic Approach to Option Pricing

被引:0
|
作者
Benada, Ludek [1 ]
Cupal, Martin [1 ]
机构
[1] Masaryk Univ, Fac Econ & Adm, Dept Finance, Brno 60200, Czech Republic
来源
EUROPEAN FINANCIAL SYSTEMS 2014 | 2014年
关键词
option pricing; lattices; Black-Scholes model; volatility; Geometric Brownian motion; LATTICE METHOD;
D O I
暂无
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Black-Scholes model (BS) and lattices are well-known methodologies applied to option pricing, with their own specific features and properties. Briefly, lattices are discrete in the inner computing process and stochastically based, while BS is represented by a continuous functional form without single steps, but deterministic only. The strong assumption of constant volatility and the inability of application in valuing "American" options represent major disadvantages of the BS model. Its main advantage is its simplicity and ease of application. The use of Monte Carlo simulations constitutes an alternative to this model. Its main advantages include a relatively easy procedure of calculation and efficiency. Problems can arise when applied to the "American" option. Likewise, this method does not belong among highly sophisticated ones due to the requirements of prerequisites. If we were to consider a model that can work with the "American" option, i.e. an option that may be exercised at any time before maturity, then calculation using the lattice approach is conceivable. In contrast, the disadvantage of this method lies in the lack of ability to apply continuous consistency with price development history as well as inability to work with a model that would require more underlying assets. Finally, the two approaches, Black-Scholes model and the lattice approach, considered for pricing options, derived their value from IBM stocks as an underlying asset. On individual valuations, accuracy of these valuation models will be observed in accordance with the real option price.
引用
收藏
页码:49 / 55
页数:7
相关论文
共 50 条
  • [31] Discrete-time option pricing with stochastic liquidity
    Leippold, Markus
    Scharer, Steven
    JOURNAL OF BANKING & FINANCE, 2017, 75 : 1 - 16
  • [32] Numerical solution for option pricing with stochastic volatility model
    Mariani, Andi
    Nugrahani, Endar H.
    Lesmana, Donny C.
    WORKSHOP AND INTERNATIONAL SEMINAR ON SCIENCE OF COMPLEX NATURAL SYSTEMS, 2016, 31
  • [33] OPTION PRICING UNDER THE FRACTIONAL STOCHASTIC VOLATILITY MODEL
    Han, Y.
    Li, Z.
    Liu, C.
    ANZIAM JOURNAL, 2021, 63 (02) : 123 - 142
  • [34] Martingalized historical approach for option pricing
    Chorro, C.
    Guegan, D.
    Ielpo, F.
    FINANCE RESEARCH LETTERS, 2010, 7 (01) : 24 - 28
  • [35] GARCH option pricing: A semiparametric approach
    Badescu, Alexandru M.
    Kulperger, Reg J.
    INSURANCE MATHEMATICS & ECONOMICS, 2008, 43 (01) : 69 - 84
  • [36] Option Pricing with Fractional Stochastic Volatility and Discontinuous Payoff Function of Polynomial Growth
    Bezborodov, Viktor
    Di Persio, Luca
    Mishura, Yuliya
    METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY, 2019, 21 (01) : 331 - 366
  • [37] Reconstruction of the Stochastic Volatility Based on the Black-Scholes Option Pricing Model
    Han, Yi-tong
    Jiang, Ming-hui
    Dou, Yi-xin
    INTERNATIONAL CONFERENCE ON COMPUTER, NETWORK SECURITY AND COMMUNICATION ENGINEERING (CNSCE 2014), 2014, : 573 - 576
  • [38] Stochastic analysis and invariant subspace method for handling option pricing with numerical simulation
    Hejazi, Seyed Reza
    Dastranj, Elham
    Habibi, Noora
    Naderifard, Azadeh
    COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS, 2022, 10 (02): : 419 - 430
  • [39] Measuring banks' liquidity risk: An option-pricing approach
    Zhang, Jinqing
    He, Liang
    An, Yunbi
    JOURNAL OF BANKING & FINANCE, 2020, 111
  • [40] Repeated spatial extrapolation: An extraordinarily efficient approach for option pricing
    Ballestra, Luca Vincenzo
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2014, 256 : 83 - 91