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研究生: 呂孟柔
Meng-Jou Lu
論文名稱: 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
指導教授: 謝浩明
How-Ming Shieh
口試委員:
學位類別: 碩士
Master
系所名稱: 管理學院 - 企業管理學系
Department of Business Administration
畢業學年度: 95
語文別: 英文
論文頁數: 75
中文關鍵詞: 蒙地卡羅模擬B-S 模型波動性
外文關鍵詞: volatility, Monte Carlo Simulation, B-S model
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  • 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.

    中文摘要………………………………………………………….. I Abstract………………………………………………………….... III Acknowledgement………………………………………………… V The figures list…………………………………………………….. VII The tables list……………………………………………….……… IX Main Contexts 1. Introduction……………………………………………………….. 1 1.1 General Background Information………………………….. 1 1.2 Purpose of Research………………………………………… 2 2. Literature Review……………………………………….................. 4 2.1 Black and Scholes Model………………………………….... 4 2.2 Jump-Diffusion Process……………………………………... 5 2.2.1 Merton Model………………………………………... 6 2.2.2 Bates Model…………………………………………. 8 2.3 GARCH(1,1) Model……………………………………….... 10 2.4 Comparisons for B-S and GARCH Model………………….. 11 3. Method…………………………………………………….............. 15 4. Model……………………………………………………………..... 18 5. Simulation Results………………………………………………...... 21 5.1 Data Description…………………………………………….. 21 5.2 Numerical Analysis……………………………………..…... 21 5.3 Mean and Numbers of Simulations……………………….…. 30 5.4 Anticipation………………………………………………….. 36 6. Conclusions……………………………………………………….... 42 Reference……………………………………………………………. 44 Appendix I: The tables of piecewise simulation for three kinds of methods………………………………………………………… 45 Appendix II: The tables of anticipation for three kinds of methods… 48

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