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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/124737
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124737

    Title: 利差交易之風險溢酬預測-長短期記憶神經網路之應用
    Predicting the Risk Premium of Carry Trade with LSTM Neural Network
    Authors: 陳郁婷
    Chen, Yu-Ting
    Contributors: 林建秀
    Lin, Chien-Hsiu
    Chen, Yu-Ting
    Keywords: 利差交易
    Carry Trade
    Long Short-Term Memory
    Time Series Forecasting
    Machine Learning
    Date: 2019
    Issue Date: 2019-08-07 16:12:16 (UTC+8)
    Abstract: 外匯市場是全球金融體系當中重要的一環,利差交易為投機客、外匯交易員、避險基金等在外匯市場當中普遍採用的交易策略。近年來,新興市場貨幣在利差交易中所佔份額逐漸增加,由於新興市場利率普遍高於成熟市場,其獲利可能性使得外匯市場參與者蜂擁而至,然而,匯率之劇烈波動卻可能侵蝕掉賺取的利潤。
    The foreign exchange market plays an important role in the global financial system. Carry Trade is one of the most popular trading strategies for speculators, forex traders, hedge funds, etc. In recent years, emerging market currencies gained market share gradually. Owing to the interest spread between emerging markets and developed markets, lots of foreign exchange market participants attracted by the profitability. However, the volatility of exchange rates might cause profit erosion.
    Uncovered Interest Rate Parity (UIP) only use the forward premium to predict the changes in the foreign exchange rate, but empirical results often deviate from the theoretical studies. Therefore, this study introduces the country risk factor into the UIP equation. Adopting the concept of neural networks into the financial field, we use Long Short-Term Memory (LSTM) neural network for the foreign exchange rate prediction. Then, compares its predictive ability to the traditional regression model’s. According to the empirical study, we predict the future trend of the foreign exchange rate in the emerging markets. By using the forward premium with the country risk, LSTM neural network shows the outstanding result. Besides the implementation of currency forecasting via neural networks, Artificial Intelligence (AI) technologies can also apply to other financial products’ price prediction. With the advances of technology and AI, thousands of application scenarios will be able to be promoted.
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    4. Burnside, C., Eichenbaum, M., & Rebelo, S. (2008) “Carry Trade: The Gains of Diversification” Journal of the European Economic Association, Vol. 6, Issue 2-3, Pages 581–588
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106352035
    Data Type: thesis
    DOI: 10.6814/NCCU201900148
    Appears in Collections:[金融學系] 學位論文

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