Using Least-Square Monte Carlo Simulation to Price American Multi Underlying Stock Options

被引:0
|
作者
Palupi, Irma [1 ]
Sitorus, Indra Utama [1 ]
Umbara, Rian Febrian [1 ]
机构
[1] Telkom Univ, Sch Comp, Bandung, Indonesia
来源
2015 3rd International Conference on Information and Communication Technology (ICoICT) | 2015年
关键词
American Options; Least-Square Monte Carlo; Optimal Exercise Boundary; Simulation method;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Stock options is a contract which give the right (without obligation) to the owner to buy or to sell stock asset at certain price during specified time period. Stock option is derivate product of stock, created to hedge and speculate. This research use Least-Square Monte Carlo (LSM) method to estimate American put option price. Firstly, LSM method is applied to determine single asset of American put option price and its optimal exercise boundary. According to parameter volatility, the computation result using implied volatility approach market value better than using estimated volatility from historical data. In determining the value of multiple assets put option, it is used the similar algorithm scheme as single asset put option. The comparison result of multi asset option price either using implied volatility and estimated volatility from historical data, does not give a significant differences. Also, this research observe the sensitivity of option price in Stock option is a contract which gives the right (without obligation) to the owner of the option to buy or sell stocks at a specified price within a certain period of time. Contract of the option has several parameters, such as the maturity date, interest rate, volatility of the underlying asset, and dividend rate. Based on the type of the rights granted, the option can be divided into two, namely call option and put option.
引用
收藏
页码:504 / 509
页数:6
相关论文
共 50 条
  • [1] Collaborative processing of Least-Square Monte Carlo for American Options
    Yang, Jinzhe
    Guo, Ce
    Luk, Wayne
    Nahar, Terence
    PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (FPT), 2014, : 52 - 59
  • [2] Optimal Parameters for Pricing of the American Put Options with Least Square Monte Carlo Simulation
    侯乃聪
    张贯立
    Journal of Beijing Institute of Technology, 2010, 19 (04) : 499 - 502
  • [3] Risk Measurement of the American Basket Options Based on the Least Square Monte Carlo Simulation Approach
    Li, Qiang
    Zhu, Xueling
    PROCEEDINGS OF THE 7TH ANNUAL MEETING OF RISK ANALYSIS COUNCIL OF CHINA ASSOCIATION FOR DISASTER PREVENTION, 2016, 128 : 507 - 512
  • [4] THE SENSITIVITY ANALYSIS OF AMERICAN OPTIONS BASED ON LEAST SQUARE MONTE CARLO
    Sun, Haifeng
    Hang, Liyan
    PROCEEDINGS OF THE 38TH INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, 2008, : 2971 - 2975
  • [5] Accelerating the least-square Monte Carlo method with parallel computing
    Chen, Ching-Wen
    Huang, Kuan-Lin
    Lyuu, Yuh-Dauh
    JOURNAL OF SUPERCOMPUTING, 2015, 71 (09): : 3593 - 3608
  • [6] Accelerating the least-square Monte Carlo method with parallel computing
    Ching-Wen Chen
    Kuan-Lin Huang
    Yuh-Dauh Lyuu
    The Journal of Supercomputing, 2015, 71 : 3593 - 3608
  • [7] On the Mixing Time of Markov Chain Monte Carlo for Integer Least-Square Problems
    Xu, Weiyu
    Dimakis, Georgios Alexandros
    Hassibi, Babak
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 2545 - 2550
  • [8] Research on the improvement of Least-Square Monte Carlo pricing method of Convertible bond
    Yang, Fei
    Ma, JunHai
    PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON FINANCIAL ENGINEERING AND RISK MANAGEMENT 2008, 2008, : 205 - 209
  • [9] An exposition of least square Monte Carlo approach for real options valuation
    Ahmadi, Rouholah
    Bratvold, Reidar Brumer
    GEOENERGY SCIENCE AND ENGINEERING, 2023, 222
  • [10] How Many Inner Simulations to Compute Conditional Expectations with Least-square Monte Carlo?
    Alfonsi, Aurelien
    Lapeyre, Bernard
    Lelong, Jerome
    METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY, 2023, 25 (03)