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| 研究生： |
郭依帆 |
|---|---|
| 論文名稱： |
使用最近鄰域法預測匯率—以美元兌新台幣為例 Predicting exchange rates with nearest-neighbors method: The case of NTD/USD |
| 指導教授： | 郭炳伸 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 國際經營與貿易學系 Department of International Business |
| 論文出版年： | 2009 |
| 畢業學年度： | 97 |
| 語文別： | 英文 |
| 論文頁數： | 48 |
| 中文關鍵詞： | 最近鄰域法 、隨機漫步 、匯率 |
| 外文關鍵詞： | Nearest-Neighbors Method, Random Walk, Exchange Rates |
| 相關次數： | 點閱：274 下載：23 |
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建立模型來估計匯率早已行之有年。較早期的匯率模型，不論是在樣本內的配適或是樣本外的預測，其實表現的並不理想。之後的研究針對這樣的結果指出，這是因為匯率的表現是非線性的，並非傳統線性模型可描繪出來。而對於捕捉匯率非線性的特性，傾向使用無母數的估計方式。因此，本研究採用最近鄰域法進行美元兌新台幣的匯率預測。另外，許多早期的研究發現，隨機漫步模型與其他模型相比較之後，在匯率預測上的表現最好，因而引發了”打敗隨機漫步”的一連串熱潮。本研究欲延續這項議題，將隨機漫步模型做為與最近鄰域模型比較的基準。
本研究使用的資料為即期匯率，包含日資料、週資料和月資料三種。將每種資料皆切割為樣本內與樣本外兩個部分，其中最後三分之一的樣本數用於樣本外預測。平均絕對誤差與平均誤差平方根則是用來衡量比較模型預測的準確性。實證結果發現，使用局部加權估計的最近鄰域模型在樣本內的配適表現上優於隨機漫步模型；然而，在樣本外的預測能力上，隨機漫步模型仍舊略勝一籌。
A wide variety of empirical exchange rate models have been estimated over the years. Earlier findings indicated that exchange rate equations do not fit particularly well, and forecast no better. Later researches then provided a potential reason for the poor performance that traditional exchange rate models, because they are nonlinear. To find a resolution for nonlinearity, nonparametric techniques tend to be useful tools. In this study, we use one of nonparametric techniques called nearest-neighbors method to predict NTD against USD. Besides, many earlier papers found that forecasts from popular models for the foreign exchange rate generally fail to improve upon the random walk out-of-sample. “Beat the random walk” became an emerging issue then. This has motivated this research, and thus we include the random walk as a linear benchmark.
The data set consists of the daily, weekly and monthly spot rates for NTD/USD. We divide each data set into a fitting set and a prediction set for in-sample analysis and out-of-sample forecast, respectively. The out-of-sample forecasts are calculated from the last one-third of each series. As a measure of performance the mean squared error (MAE) and root mean squared error (RMSE) are used. In our empirical results, we find that nearest-neighbors model using local weights easily tops the random walk in-sample. However, as we turn to the out-of-sample prediction, no models produce forecasts superior to the random walk. It seems difficult to beat the random walk out-of-sample in this study.
ABSTRACT....................................... I
CONTENTS....................................... II
CHAPTER 1. INTRODUCTION........................ 1
CHAPTER 2. LITERATURE REVIEW................... 4
CHAPTER 3. METHODOLOGY......................... 6
3.1 NONPARAMETRIC PREDICTION................. 6
3.2 NEAREST-NEIGHBORS MODEL.................. 8
3.3 NEIGHBOR SELECTION AND CONSISTENCY....... 12
3.4 LOCALLY WEIGHTED REGRESSION.............. 14
3.5 THE RANDOM WALK MODEL.................... 20
3.6 ERROR MEASUREMENT STATISTICS............. 21
3.6.1 Mean Absolute Error (MAE)........... 21
3.6.2 Root Mean Squared Error (RMSE)...... 22
3.6.3 Advantages of MAE and RMSE.......... 23
CHAPTER 4. EMPIRICAL ANALYSIS.................. 24
4.1 DATA DESCRIPTION......................... 24
4.2 UNIFORM WEIGHTS.......................... 26
4.3 LOCAL WEIGHTS............................ 28
CHAPTER 5. SUMMARY AND CONCLUSIONS............. 39
REFERENCES..................................... 41
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