使用遺傳基因演算法及蒙地卡羅模擬來對美式賣權進行評價

Using Genetic Algorithms and Monte Carlo Simulation to Value American put Options

高志賢(Chih-Hsien Kao)

指導教授 ： 廖光彬

崑山科技大學/商業管理學院/企業管理研究所/碩士(2003年)

https://doi.org/10.6828/KSU.2003.00027

摘要

本研究使用遺傳基因演算法及蒙地卡羅模擬，來嘗試做出最佳的履約決策以對美式賣權加以評價。文獻上有一些學者提出利用蒙地卡羅模擬來評價美式選擇權的方法，這些方法中大多要依賴每一時間點持有價值的估計。一旦得到了持有價值的估計值，藉由此估計值與提前履約價值之比較，即可做出是否提前履約之決策。在本研究中將在不估計持有價值之情況下對美式賣權加以評價。基因演算法將用來協助蒙地卡羅法盡量做出最佳的履約決策。此方法之所以可行乃由於較佳的履約決策能導致較大的預期折現利潤。

關鍵字

美式賣權 ； 蒙地卡羅模擬 ； 基因演算法

並列摘要

This study uses a genetic algorithm and Monte Carlo Simulation to value American put options by trying to make optimal early exercise decisions. In the literature, some researchers have proposed approaches to valuing American options by using Monte Carlo simulation. Most of the approaches rely on the estimation of the value of continuing at each early exercise point. Once the estimated value of continuing has been obtained, early exercise decision can be made by comparing this value with the value of exercising. In this study, American put options will be valued without the estimation of the value of continuing. A genetic algorithm will be used to assist the Monte Carlo method in making the early exercise decisions as optimally as possible. This is possible because better early exercise decisions can bring about a bigger expected discounted profit.

並列關鍵字

Monte Carlo Simulation ； Genetic Algorithms ； American put Options

參考文獻

[2] 陳木松,廖鴻翰,”適應性圖變運算及其應用”大葉學報 第七卷 第一期 民國87年。

[5] 張森林，何振文，”蒙地卡羅模擬在選擇權評價上之運用，”國立中央大學財務管理研究所碩士論文民國89年6月。

[6] Andersen, L. A simple approach to the pricing of Bermudan swaptions in the multifactor LIBOR market model. The Journal of Computational Finance, 3, No. 2(Winter 2000), 1-32.

[7] Andre, J. Siarry, P. and Dognon, T.(2001). An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advances in Engineering Software, 32, 49-60.

[8] Barraquand, J., and Martineau, D.(1995). Numerical valuation of high dimensional multivariate American securities. Journal of Financial and Quantitative Analysis, 30, 383-405.