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| Title: | 基於BPNN- GJR GARCH組合模型在新台幣對美元匯率收益率預測中的應用
Application of BPNN-GJR GARCH Combination Model in the Forecast of the Exchange Rate of New Taiwan Dollar to U.S. Dollar |
| Authors: | 管奕錚
Guan, Yi-Zheng |
| Contributors: | 鄭宇庭
Cheng, Yu-Ting
管奕錚
Guan, Yi-Zheng |
| Keywords: | 波動率模型
BP神經元網絡
偏t分佈
組合模型
Volatility model
BP neural network
Skewed-t
Combined model |
| Date: | 2022 |
| Issue Date: | 2022-02-10 12:53:19 (UTC+8) |
| Abstract: | 匯率本身同時具有線性和非線性的混合特徵，所以單一的線性模型或是非線性模型都無法完美的準確預測匯率的變動。因此本文採用了數種ARCH族類的波動率模型和BP神經元網絡模型及其各自的組合模型，用於研究組合模型是否能提高匯率預測的精準度
將2018年1月2日到2020年12月31日的新台幣對美元日匯率數據序列進行轉換後得到匯率的收益率序列，並使用ARCH、GARCH、GJR GARCH的理論方法分別構建了收益率序列的波動率模型，同時考慮了其殘差服從常態分佈，對稱t分佈和有偏t分佈的三種情況，然後用這些模型對匯率收益率進行了預測同時也使用了BP神經網絡模型對匯率收益率序列進行了擬合與預測。
最後，本文根據各模型的預測誤差提出了模型組合的方法。並通過誤差檢驗指標證明BP- Skewed-t GJR GARCH的組合模型相比其餘的單一模型和組合模型都具有更高的收益率預測精準度。
The exchange rate itself has both linear and non-linear mixed characteristics, so a single linear model or a non-linear model cannot perfectly and accurately predict changes in the exchange rate. Therefore, this article uses several ARCH family volatility models and BP neural network models and their respective combined models to study whether the combined models can improve the accuracy of exchange rate forecasts
After converting the daily exchange rate data series of New Taiwan Dollar to USD from January 2, 2018 to December 31, 2020, the exchange rate return sequence was obtained, and the exchange rate return sequence was constructed using the ARCH ，GARCH, and GJR GARCH volatility models，also considering the residuals obey the normal distribution, the symmetric t distribution and the biased t distribution, and then use these models to predict the exchange rate return rate and also use the BP neural network model to fitted and predicted exchange rate return rate.
Finally, this paper proposes a model combination method based on the prediction error of each model. And through the error test index, it is proved that the BP-Skewed-t GJR GARCH combination model has a higher accuracy of return prediction than the rest of the single model and the combination model. |
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| Description: | 碩士
國立政治大學
統計學系
105354031 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105354031 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202200025 |
| Appears in Collections: | [統計學系] 學位論文
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