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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/11745


    題名: 探討B-S模型分段模擬匯率波動性及適用性-以新台幣兌美元為例;Employing Piecewise Simulation to investigate on the Volatility and Applicability of the B-S Model for Exchange Rate-Example of NT dollar to US dollar
    作者: 呂孟柔;Meng-Jou Lu
    貢獻者: 企業管理研究所
    關鍵詞: 蒙地卡羅模擬;B-S 模型;波動性;volatility;Monte Carlo Simulation;B-S model
    日期: 2007-01-02
    上傳時間: 2009-09-22 14:31:07 (UTC+8)
    出版者: 國立中央大學圖書館
    摘要: 現在大部分的投資者偏向使用Black-Scholes模型來定價金融資產的價格。原因在於我們將基本的資產價格、執行價格、無風險利率、到期日及波動性的參數帶入B-S模型而可以發現封閉解。B-S模型的研究學者通常認為波動性常數是固定的。然而在此篇研究論文裡,經由分段模擬我們證明了B-S模型可以接近新台幣兌美元匯率途徑的實際值。根據每一組 及 ,我們可以預測下一個10天、20天、30天的新台幣兌美元的匯率,且發現在B-S模型下所預期匯率的可適用性。 此篇論文的研究包含了Metlab程式所進行的蒙地卡羅模擬,有三種方法來決定模擬的期間,當求出每一組 及 時,經由天真的方法我們預測到下一期新台幣兌美元匯率的實際值。 經由模擬過去歷史的匯率,此篇研究結果顯示固定時間區間法(Fixed Time Interval method)是優越於其他的方法,事件驅動法(Event- Drive method)是其次, 而逐月推移估計法(Month Downward method)是最後。經由固定時間區間法求得的每一組 及 值去預測下一個10天的結果是優越於其他的方法。而事件驅動法的結果是劣於固定時間區間法卻優於逐月推移估計法。在預測下一個20天的結果中,固定時間區間法是優越於其他的方法,而逐月推移估計法的結果是劣於固定時間區間法卻優於事件驅動法。 在結論上,此篇的研究是可以用來解釋 、 及跳躍之間動態關係的重要性,也證明出一個最好的方法來預測匯率。 Now a day, most investors like to use the Black-Scholes model to price the financial asset value. Because we substitute the underlying asset price, exercise price, the risk-free interest rate, time to maturity and volatility to the B-S model, we can find the closed-form solution. Researchers of Black-Scholes model often reject the constant-volatility. However, in this article we proof that the B-S model can be close to the path of the exchange rate’s actual value for NTD/USD by individual simulation. By each bank of μ and σ, we can predict the next 10 days, 20 days, and 30days of NTD/USD, and find the applicability of predicting under the B-S model. This research involves the Monte Carlo simulation by the Metlab program. There are three kind of method for deciding the duration for simulation. When drawing on each bank of μ and σ, we predict the next period’s actual value of NTD/USD by Naïve method. Results of this study show the Fixed Time Interval method is superior to the others by simulating the historic exchange rate. The Event- Drive method is second, and the Month Downward method is last. For predicting the next 10 days, it is drawn out μ and σ by the Fixed Time Interval method that is superior to the others. The Event-Drive method is inferior to Fixed Time Interval method but superior to Month Downward method. For predicting the next 20 days, the built-in duration method is superior to the others. The Month Downward method is subordinate to the Fixed Time Interval method but better than the Event-Drive method. To conclude, this study may be of importance in explaining the dynamic relationship between μ , σ and jumps, as well as providing the best method to anticipate the exchange rate.
    顯示於類別:[企業管理研究所] 博碩士論文

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