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研究生:蔡文豪
研究生(外文):TSAI, WEN-HAO
論文名稱:用時變模型預測匯率
論文名稱(外文):Forecasting Exchange Rates Using Time-Varying Parameter Model
指導教授:梁恕梁恕引用關係鄭宗松鄭宗松引用關係
指導教授(外文):LIANG-SHUHZHENG, ZONG-SONG
口試委員:溫福星姜一銘黃健銘
口試委員(外文):WEN, FUR-HSINGJUANG, I-MINGHUANG, CHIEN-MING
口試日期:2017-06-24
學位類別:碩士
校院名稱:東吳大學
系所名稱:國際經營與貿易學系
學門:商業及管理學門
學類:貿易學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:34
中文關鍵詞:預測匯率
外文關鍵詞:Forecasting Exchange Rates
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台灣是一個非常依賴匯率來保護國內企業或是制定政策的國家,所以匯率的波動會影響到台灣大小的企業。為了準確地預測匯率,我們提供了高頻率的日資料來預測匯率而把匯率內生化,而這些內生變數都是決定匯率的重要關鍵。
我們提供了貝氏時變參數模型(BVAR-TVP)來預測台灣對美國的短期匯率。一個很重要的貢獻是投資者可以藉由應用不同的投資策略來讓預測匯率有獲利的空間,尤其是我們運用落後一天期的匯率。
我們也提供了不同的檢測跟標準來評估預測能力,而這些預測也顯示BVAR-TVP相對於其他模型是更有預測能力的。
Taiwan has been relying on foreign exchange intervention policy to decide or protect various domestic financial products or industries, so exchange rate fluctuations will affect the Taiwanese enterprises. In order to accurately predict the exchange rates, we provide daily data on the endogenous variables to predict exchange rates, and these endogenous variables are the exchange rates decision of one of the important factors.
We propose a Bayesian vector auto-regressive model with time-varying parameters (BVAR-TVP) to examine the short-term predictability of exchange rates of Taiwan. An important contribution of the paper is the application of the BVAR-TVP model is that investors could have made excess profits if they had followed trading strategy based on the signals generated by the model’s one-day-ahead exchange rates forecasts.
We employ criteria and statistical tests to assess the exchange rates predictability. The predictions show that the predicted results prove that Bayesian time varying model can predict the exchange rates of Taiwan and forecast the situation after the profit.

1. Introduction
1.1 Motivation and Background
1.2 Research Methods and Purpose
1.3 Research Process
2. Literature Review
2.1. Literature Background
2.2. Literature of Taiwan’s Exchange rates Forecast
2.3. The Application of Literature and Connection
3. Research Methods
3.1. Bayesian VAR Outline
3.2. BVAR time-varying parameters technical background
3.3. Forecasting Tools
3.4. Application
4. Forecast Results
4.1. Accuracy Criteria
4.2. Model Performance Results
4.3. Out-of-Sample Forecasts Results
4.3.1 Different prior Out-of-Sample Forecasts Results
4.3.2 Different Models Out-of-Sample Forecasts Results
4.3.3 Profitability of Different Strategies
5. Conclusion
5.1. Summary
5.2. Evaluation
5.3. Future Work
References
Appendix

List of Tables
Table 1 : Descriptive Statistics of Variables
Table 2 : ADF-test (unit root test)
Table 3 : Lag Lengths
Table 4 : Model Performance (Response variable: Exchange Rates (EX))
Table 5 : Variance Decomposition of EX
Table 6 : Forecast Result with Different Prior Type
Table 7 : Out-of-Sample Forecast
Table 8 : Profitability of Forecasting Exchange Rates (set the stop loss)

List of Graph and Appendix
Graph 1 : Impulse Responses
Graph 2 : VAR Out-Sample Forecast
Graph 3 : BVAR Out-Sample Forecast
Appendix 1 : Graph of Variables (original)
Appendix 2 : Graph of Variables (stationary)


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