國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

特殊隨機微分方程下之二項式模型收斂速度 = The Convergenc...

國立高雄大學金融管理學系碩士班

特殊隨機微分方程下之二項式模型收斂速度 = The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation
作者:	張吉富,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	2015[民104]
面頁冊數:	51面圖，表 : 30公分;
標題:	幾何布朗運動
標題:	geometric Brownian motion (GBM)
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/77398911058794405959
附註:	104年10月31日公開
附註:	參考書目：面45-46
摘要註:	本文嘗試將傳統選擇權之標的物變動須遵循的前提,即幾何布朗運動放寬,透過樹狀圖方法之二項式模型對此類選擇權計價。藉由找出滿足局部一致性(local consistency)的二項式模型漲跌幅,及其相對應之機率,證明在此之下的二項式模型所計算出之選擇權價格,會收斂至正確的價格,並證明其誤差範圍。本文以漁業選擇權為例,以蒙地卡羅(Monte Carlo)模擬作為選擇權正確價格之依歸,除二項式模型外,另外加入Ewald (2013)所提出的於此漁業選擇權計價公式,最大概似法(Maximum likelihood estimation, MLE)與動差法(method of moment, MOM),一起比較準確度,發現在此情形下,二項式模型的表現為最佳。 This paper attempts to release the assumption that the price of the underlying asset on which an option is written follows a geometric Brownian motion (GBM). The local consistency conditions are adopted to construct a binomial tree which approximates the GBM. The convergence speed of pricing an option under the binomial model is shown in the paper. Several numerical examples are offered. The Monte Carlo simulation is applied to price the fishery option in Ewald (2013). In addition to the Monte Carlo method, the option pricing formula in Ewald (2013), the maximum likelihood method, the method of moment, and the binomial model are also applied. Comparing the result of each method to that of the Monte Carlo method, the binomial model performs better than other methods.

特殊隨機微分方程下之二項式模型收斂速度 = The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation
張, 吉富

特殊隨機微分方程下之二項式模型收斂速度 = The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation / 張吉富撰 - [高雄市] : 撰者, 2015[民104]. - 51面 ; 圖，表 ; 30公分.
104年10月31日公開參考書目：面45-46.
幾何布朗運動geometric Brownian motion (GBM)

特殊隨機微分方程下之二項式模型收斂速度 = The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation
LDR:02723nam0a2200289 450 001 458303
005 20170214094528.0
009 458303
010 0 $b 精裝
010 0 $b 平裝
100 $a 20170214d2015 k y0chiy50 e
101 0 $a chi $d chi $d eng
102 $a tw
105 $a ak am 000yy
200 1 $a 特殊隨機微分方程下之二項式模型收斂速度 $d The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation $z eng $f 張吉富撰
210 $a [高雄市] $c 撰者 $d 2015[民104]
215 0 $a 51面 $c 圖，表 $d 30公分
300 $a 104年10月31日公開
300 $a 參考書目：面45-46
314 $a 指導教授：王文楷博士
328 $a 碩士論文--國立高雄大學金融管理學系碩士班
330 $a 本文嘗試將傳統選擇權之標的物變動須遵循的前提,即幾何布朗運動放寬,透過樹狀圖方法之二項式模型對此類選擇權計價。藉由找出滿足局部一致性(local consistency)的二項式模型漲跌幅,及其相對應之機率,證明在此之下的二項式模型所計算出之選擇權價格,會收斂至正確的價格,並證明其誤差範圍。本文以漁業選擇權為例,以蒙地卡羅(Monte Carlo)模擬作為選擇權正確價格之依歸,除二項式模型外,另外加入Ewald (2013)所提出的於此漁業選擇權計價公式,最大概似法(Maximum likelihood estimation, MLE)與動差法(method of moment, MOM),一起比較準確度,發現在此情形下,二項式模型的表現為最佳。 This paper attempts to release the assumption that the price of the underlying asset on which an option is written follows a geometric Brownian motion (GBM). The local consistency conditions are adopted to construct a binomial tree which approximates the GBM. The convergence speed of pricing an option under the binomial model is shown in the paper. Several numerical examples are offered. The Monte Carlo simulation is applied to price the fishery option in Ewald (2013). In addition to the Monte Carlo method, the option pricing formula in Ewald (2013), the maximum likelihood method, the method of moment, and the binomial model are also applied. Comparing the result of each method to that of the Monte Carlo method, the binomial model performs better than other methods.
510 1 $a The Convergence Speed of Binomial Model under a Specific Stochastic Differential Equation $z eng
610 0 $a 幾何布朗運動 $a 局部一致性 $a 二項式模型
610 1 $a geometric Brownian motion (GBM) $a local consistency conditions $a binomial model
681 $a 008M/0019 $b 343408 1143 $v 2007年版
700 1 $a 張 $b 吉富 $4 撰 $3 709551
712 0 2 $a 國立高雄大學 $b 金融管理學系碩士班 $3 354008
801 0 $a tw $b NUK $c 20151016 $g CCR
856 7 $z 電子資源 $2 http $u http://handle.ncl.edu.tw/11296/ndltd/77398911058794405959