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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/3730


    Title: 神經網路在選擇權定價上之應用
    Authors: 陳威光;蔡瑞煌
    Keywords: 類神經網路;選擇權定價;亞式選擇權;倒傳遞類神經網路
    Neural network;Option pricing;Asian option;Back propagation neural network
    Date: 1999
    Issue Date: 2007-04-18 16:34:53 (UTC+8)
    Publisher: 臺北市：國立政治大學金融系
    Abstract: 應用類神經網路的技術在財務金融領域是最近幾年來熱門的課題，如同Kuan and White(1994)所言：神經網路是一種非線性參數模型，而網路系統的學習過程好比進行模型參數之估計。本文採用最常被應用的倒傳遞網路系統，以及理解神經網路兩系統來做為亞式選擇權的評價。隨著特殊選擇權產品的快速發展，如亞式選擇權(或稱平均式選擇權)，這些選擇權的定價公式對理論界或實務界也愈來愈有其迫切性。然而最為市場採用的算術平均選擇權，因其分配不是對數常態分配，因此並沒有標準公式解。大多是以數值的方法逼近。本研究的目的在於利用神經網路非線性的學習特性來評價這些選擇權。本文採用神經網路的學習系統來評價亞式選擇權。本文結果發現網路學習效果具有相當不錯的準確性。
    It has been a hot issue to utilize the technique of artificial Neural Network (ANN) to the financial markets such as stocks, and futures markets. The purpose of this paper is to apply the Back Propagation Neural Network (BP) and Reasoning Neural Network (RN) to the valuation of Asia option. The option markets have grown dramatically since Black-Scholes (1973) derived their famous option pricing model. The markets for the exotic products especial for Asia options (average rate option) have grown rapidly for the past decade. There is no closed-form solution for the arithmetic average option since the distribution of the sum of the stock price is not lognormal. The propose of the paper is to utilize the BP Neural Network as well as RN Neural Network to price the Asia option. It is shown that artificial neural networks can be successfully employed to approximate formulas for the Asian option, for which analytic formulas cannot derived.
    Description: 核定金額：238500元
    Data Type: report
    Appears in Collections:[金融學系] 國科會研究計畫

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