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系統識別號 U0026-1706201413385100
論文名稱(中文) Cox-Ingersoll-Ross 模型是否能有效預測美元兌換日圓匯率的走勢?
論文名稱(英文) Is Cox-Ingersoll-Ross Model a Good Predictor for Future U.S./Japan Exchange Rate Movement?
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 102
學期 2
出版年 103
研究生(中文) 林雅芳
研究生(英文) Ya-Fang Lin
電子信箱 lucy648lin@gmail.com
學號 R16011242
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 指導教授-王澤世
口試委員-林軒竹
口試委員-陳嬿如
中文關鍵字 匯率  預測  時間序列  移動視窗  CIR 模型  預測力 
英文關鍵字 exchange rate  predicting  time series  moving window  CIR model  predictive power 
學科別分類
中文摘要 匯率是一種不穩定、複雜且難以預測的時間序列資料,時間序列的預測,傳統上是以統計方法為主。一般而言,自我迴歸整合移動平均(Autoregressive Integrated Moving-Average, ARIMA)模型對於線性資料之預測績效是頗佳的。因此,先以資料樣建立ARIMA模型,得出線性預測值,Cox-Ingersoll-Ross (CIR) 模型是一個數理金融模型,藉由未拋補利率平價可以用來預測匯率,因此,我們接著使用美國與日本的本息分離債券來估計 CIR模型參數後再來進行樣本時間內的匯率估計。樣本皆以移動視窗法生成模型並且預測,最後計算各個模型實際值與預測值的均方根誤差(Root Mean Square Error, RMSE),平均絕對誤差(Mean Absolute Error, MAE)與平均誤差百分比(Mean Absolute Percentage Error, MAPE)三項指標衡量績效。實驗結果發現,CIR 模型的預測績效,較 ARIMA預測模型佳。
英文摘要 The exchange rate is time series data that unstable, complex and difficult to predict. In tradition, the forecasting in time series data is to use statistical method. Generally speaking, autoregressive integrated moving-average (ARIMA) model for forecasting in linear data is quite good. Hence, we use the sample data to establish the ARIMA model at first and derive the linear predictive values. The mathematical financial model, Cox-Ingersoll-Ross (CIR) model also be used to predict the exchange rate through the uncover interest rate parity (UIRP). Therefore, second, we use STRIPS bonds of U.S. and Japan to obtain the estimated CIR models to predict the exchange rate in our sample period, Jan.2, 2012 to Mar. 30, 2012. We use the moving window method to generate the estimated exchange rates. Finally, in order to measure the predictive power, we calculate the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the forecasting models. The empirical results show that the predictive power of the CIR model is significantly better than traditional ARIMA model.
論文目次 Contents
I. Introduction .............. 1
II. Literature Review ........... 4
2.1 Autoregressive integrated moving-average (ARIMA) model ..... 4
2.2 The term structure of interest rates model ....... 5
2.3 Uncovered interest rate parity .......... 8
III. Methodology ............ 11
3.1 Autoregressive integrated moving-average (ARIMA) model ... 11
3.1.1 Predicted U.S./Japan exchange rate by the ARIMA model .... 12
3.2 The Cox-Ingersoll-Ross Model ......... 13
3.2.1 Estimating the parameters of the Cox-Ingersoll-Ross Model ... 15
3.2.2 Predicted U.S./Japan exchange rate by the CIR model .... 17
3.3 Evaluating the predictive power between the CIR model and ARIMA model . 18
IV. Data .............. 21
4.1 Data Statistics Summary .......... 21
4.2 Unit root test ............ 23
V. Empirical Results ........... 25
5.1 The Parameters of the CIR Model ......... 25
5.2 The estimated exchange rates by the CIR Models ...... 29
5.3 Predictive Power of the CIR Models ......... 36
5.4 Predictive Power between the CIR Model and ARIMA Model .... 39
VI. Conclusions ............ 49
VII. References ............ 51
參考文獻 Box, G. E. P. and Jenkins, G., 1970. Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.

O. Valenzuela, I. Rojas, F. Rojas, H. Pomares, L.J. Herrera, A. Guillen, L. Marquez, M. Pasadas.,2008.Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems, 159,821–845.

Chi Chieh Yu, 2012. Forecast exchange rate of the U.S. Dollar against NT-time series model and support vector machine.

Bansal, R. and M. Dahlquist, 2000. The Forward Premium Puzzle: Different Tales from Developed and Emerging Economies. Journal of International Economics, v51, 115-144.

Brown, S. J. and P. H. Dybvig, 1986. The empirical implications of the Cox, Ingersoll, and Ross theory of the term structure of interest rate. Journal of Finance, v41, 627-630

Cox, J. C., J. E. Ingersoll and S. A. Ross, 1985. A theory of the term structure of interest rate. Econometrica, v53, 385-407

Dickey, D. A. and W. A. Fuller, 1979. Distributions of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, v74, 427-431.

Engle, R. F., 1982. Autoregressive Conditional Heteroscedasticity with Estimates of theVariance of United Kingdom Inflation. Econometrica, v50, 987-1007
Fama, E. F., 1984. Forward and spot exchange rates. Journal of Monetary Economics, v14, 319-338

Froot, K. and R. Thaler, 1990. Foreign exchange. Journal of Economic Perspectives, v4, 179-192.

Hakkio, C. S., 1981. Expectations and the Forward Exchange Rate. Journal of
International Economic, v22, 663-678.

Ho, T., and S. B. Lee, 1986. Term Structure Movements and the Pricing of Interest Rate Contingent Claims. Journal of Finance, v41, 1011-1029.

Huang, R. D., 1987. Expectations of Exchange Rates and Differential Inflation Rates:Further Evidence on Purchasing Power Parity in Efficient Markets. Journal of Finance, v92, 69-79.

Koedijk, K.G., F.G.J.A. Nissen, P.C. Schotman and C.C.P. Wolff, 1998.The Dynamics of Short-Term Interest Rate Volatility Reconsidered. European Finance Review, v1, 105-23.

McCallum, B., 1994. A Reconsideration of the Uncovered Interest Rate Parity Relationship. Journal of Monetary Economics, v33, 105-132

Meese, R. and K. Rogoff, 1988. Was It Real? The Exchange Rate-Interest Rate Differential over the Modern Floating-rate Period. Journal of Finance, v43 (4), 933-948.

Sargent, T. J., 1979. A Note on Maximum Likelihood Estimation of the Rational Expectations Model of the Term Structure. Journal of Monetary Economics, v 5, 133-143

Vasicek, O., 1977. An Equilibrium Characterization of the Term Structure. Journal of Financial Economics, v5, 177-188.

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