| 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 |
| 相關次數: | 點閱:9 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
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.
Bakshi, G., Charles C., &Zhiwu C. (1997), Empirical Performance of Alternative
Option Pricing Models. Journal of Finance, 52(5), 2003-2049.
Bjørn, E., Michael, J., & Nicholas, P. (2003). The Impact of Jumps in Volatility and
Returns. Journal of Finance, 58(3), 1269-1300.
Chung-Ki, C., Taekkeun, K., & YongHoon K. (2005). Estimation of Local Volatilities
in a Generalized Black-Scholes Model. Applied Mathematics and Computation,
162(3), 1135-1149.
Corrado, C., & Tie S. (1998). An Empirical Test of the Hull-White Option Pricing
Model. The Journal of Futures Markets, 18(4), 363-378.
Clive,W. J. Granger.(1969). Investigating Causal Relations by Econometric Models
and Cross-spectral Methods. Econometrica, 37(3), 424-438.
David, S. B. (1996). Jumps and Stochastic Volatility: Exchange Rate Processes
Implicit in Deutsche Mark Options. Review of Financial Studies, 9(1), 69-107.
Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the
Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1008.
Jiang, G. (1999). Index Option Pricing Models with Stochastic Volatility and
Stochastic Interest Rates. European Finance Review, 3(3), 273-310.
Meade. (1993). Forecasting the Return and Risk on a Portfolio of Assets,
International Journal of Forecasting, 9(3), 373-386.
Merton, R. C. (1976). Option PricingWhen the Underlying Stock Returns are
Discontinuous. Journal of Financial Economics, 3(1-2), 125-144.
Paolo, B. (2002). Numerical Methods in Finance. New York: JohnWiley & Sons. Inc.
Wei, J.Z., & Duan, J.C. (1999). Pricing Foreign Currency and Cross-Currency
Options under GARCH. Journal of Derivatives, 17(1), 51-63.
陳力凡. (2002). CIR、GARCH 及Jump-Diffusion 模式在指數選擇權定價之研究.
國立清華大學科技管理所碩士論文.
陳樂軒. (2004). 台灣股市之漲跌停板的限制對B-S 選擇權定價模式之影響. 國
立成功大學統計學系碩士班論文.
陳浚泓. (2003). B-S 模式與隨機波動性定價模式之比較: 台灣股價指數選擇權
之實證. 國立成功大學企業管理研究所碩士論文
楊金龍. (2005). 美國外匯市場波動性研究-Cox 模型的應用. 私立東吳大學經
濟學系碩士論文.