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題名 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作者 陳郁婷
Chen, Yu-Ting貢獻者 林建秀
Lin, Chien-Hsiu
陳郁婷
Chen, Yu-Ting關鍵詞 利差交易
長短期記憶模型
時間序列預測
機器學習
Carry Trade
Long Short-Term Memory
Time Series Forecasting
Machine Learning日期 2019 上傳時間 7-Aug-2019 16:12:16 (UTC+8) 摘要 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。傳統利率模型僅以遠期溢價預測匯率變動，但在實證結果上卻往往與理論相悖離，因此，本研究將國家風險因素納入考量，並將機器學習中的類神經網路概念及技術引入金融領域當中，以長短期記憶模型(LSTM)對未來匯率變動進行預測，同時也將其預測能力與傳統迴歸模型之預測效果相互比較。實證發現，在新興市場國家當中使用LSTM神經網路模型，並以考量國家風險因子之遠期溢價預測未來匯率走勢有較傑出的預測效果。除了將類神經網路用以預測匯率變動之外，我們一樣能將人工智慧的技術應用於其他金融商品之價格預測上，隨著技術日漸進步、人工智慧相關領域的研究逐年倍增，數以萬計的應用場景將在人工智慧的環境下得以升級。
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.參考文獻 1. Anker, P. (1999) “Uncovered Interest Parity, Monetary Policy and Time-Varying Risk Premia” Journal of International Money and Finance, Elsevier, Vol. 18(6), Pages 835-851.2. Bengoechea, A.G., Uretaz, C. O., Saavedra, M. M., & Medina, N. O. (1996) “Stock Market Indices in Santiago de Chile: Forecasting Using Neural Networks” IEEE International Conference on Neural Networks , Vol. 4, Pages 2172-21753. Bildirici, M., & Ersin, Ö. Ö. (2009) “Improving Forecasts of GARCH Family Models with the Artificial Neural Networks” Journal Expert Systems with Applications: An International Journal archive, Vol. 36, Issue 44. 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 581–5885. Cavallo, M. (2006) ”Interest Rates, Carry Trades, and Exchange Rate Movements” FRBSF Economic Letter6. Chinn, M. D., & Meredith, G. (2004) “Monetary Policy and Long-Horizon Uncovered Interest Parity” IMF Staff Papers – Vol. 51, No 37. Fama, E. F. (1984) “Forward and Spot Exchange Rates” Journal of Monetary Economics, Vol. 14, Issue 3, Pages 319-3388. Fischer, T., & Krauss, C. (2017) “Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions” European Journal of Operational Research, Vol. 270, Issue 2, Pages 654-6699. Froot, K. A., & Thaler, R. H. (1990) “Anomalies: Foreign Exchange.” Journal of Economic Perspectives, 4(3):179-192.10. Hochreiter, S., & Schmidhuber, J. (1997) “Long Short-Term Memory” Neural Computation 9(8): 1735-8011. Hutchison, M., & Sushko, V. (2013) “Impact of Macro-Economic Surprises on Carry Trade Activity” Journal of Banking & Finance, Vol. 37, Issue 4, Pages 1133-114712. Jylhä, P., Lyytinen, J. P., & Suominen, M. (2008) “Arbitrage Capital and Currency Carry Trade Returns” AFA 2009 San Francisco Meetings Paper13. Kimoto, T., & Asakawa, K. (1990) “Stock Market Prediction System with Modular Neural Networks” 1990 IJCNN International Joint Conference on Neural Networks, Vol. 1, Pages 1-614. Lustig, H., Roussanov, N., & Verdelhan, A. (2011) “Common Risk Factors in Currency Markets” The Review of Financial Studies, Vol. 24 ,Issue 11, Pages 3731-377715. Lustig, H., & Verdelhan, A. (2005) “The Cross-Section of Currency Risk Premia and US Consumption Growth Risk” NBER Working Paper No. 1110416. MacDonald, R., & Torrance, T. S. (1989) “Some Survey Based Tests of Uncovered Interest Parity” In MacDonald, R. and Taylor, M.P. (eds.) Exchange Rates and Open Economy Macroeconomics, Pages 239-24817. Namin, S. S., & Namin, A. S. (2018) “Forecasting Economic and Financial Time Series: ARIMA VS. LSTM”18. Persio, L. D., & Honchar, O. (2017) “Recurrent Neural Networks Approach to the Financial Forecast of Google Assets” International Journal of Mathematics and Computers in Simulation, Vol. 11, Pages 7-1319. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) “Learning Internal Representations by Error Propagation” DTIC Document, Tech. Rep.20. Santos, M. B. C., Klotzle, M. C., & Pinto, A. C. F. (2016) “Evidence of Risk Premiums in Emerging Market Carry Trade Currencies” Journal of International Financial Markets, Institutions & Money, Vol. 44, Pages103-11521. Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2002) “Combining Neural Network Model with Seasonal Time Series ARIMA Model” Technological Forecasting & Social Change, Vol. 69, Issue 1, Pages 71–8722. Yim, J. (2002) “A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index.” Lecture Notes in Computer Science, Pages 25-35 描述 碩士
國立政治大學
金融學系
106352035資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106352035 資料類型 thesis dc.contributor.advisor 林建秀 zh_TW dc.contributor.advisor Lin, Chien-Hsiu en_US dc.contributor.author (Authors) 陳郁婷 zh_TW dc.contributor.author (Authors) Chen, Yu-Ting en_US dc.creator (作者) 陳郁婷 zh_TW dc.creator (作者) Chen, Yu-Ting en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 16:12:16 (UTC+8) - dc.date.available 7-Aug-2019 16:12:16 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 16:12:16 (UTC+8) - dc.identifier (Other Identifiers) G0106352035 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124737 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 106352035 zh_TW dc.description.abstract (摘要) 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。傳統利率模型僅以遠期溢價預測匯率變動，但在實證結果上卻往往與理論相悖離，因此，本研究將國家風險因素納入考量，並將機器學習中的類神經網路概念及技術引入金融領域當中，以長短期記憶模型(LSTM)對未來匯率變動進行預測，同時也將其預測能力與傳統迴歸模型之預測效果相互比較。實證發現，在新興市場國家當中使用LSTM神經網路模型，並以考量國家風險因子之遠期溢價預測未來匯率走勢有較傑出的預測效果。除了將類神經網路用以預測匯率變動之外，我們一樣能將人工智慧的技術應用於其他金融商品之價格預測上，隨著技術日漸進步、人工智慧相關領域的研究逐年倍增，數以萬計的應用場景將在人工智慧的環境下得以升級。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景 1第二節 研究動機 4第三節 研究目的 5第二章 文獻探討 7第一節 UIP模型 7第二節 類神經網路應用於金融分析 8第三節 文獻回顧總結 10第三章 理論介紹 12第一節 利差交易、UIP介紹 12第二節 簡單線性迴歸最小平方法 13第三節 類神經網路介紹 13第四章 研究對象及資料收集 18第一節 研究對象 18第二節 參數收集及資料期間 18第三節 敘述統計 20第四節 單根檢定 22第五章 研究方法 24第一節 模型架構 24第二節 迴歸模型建構 24第三節 LSTM神經網路模型建構 25第六章 實證分析 31第一節 迴歸模型實證結果 31第二節 LSTM神經網路實證結果 34第三節 迴歸模型與LSTM神經網路比較 36第七章 結論與建議 38第一節 研究結論 38第二節 未來建議 39參考文獻 40附錄一 42附錄二 43 zh_TW dc.format.extent 1113258 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106352035 en_US dc.subject (關鍵詞) 利差交易 zh_TW dc.subject (關鍵詞) 長短期記憶模型 zh_TW dc.subject (關鍵詞) 時間序列預測 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Carry Trade en_US dc.subject (關鍵詞) Long Short-Term Memory en_US dc.subject (關鍵詞) Time Series Forecasting en_US dc.subject (關鍵詞) Machine Learning en_US dc.title (題名) 利差交易之風險溢酬預測－長短期記憶神經網路之應用 zh_TW dc.title (題名) Predicting the Risk Premium of Carry Trade with LSTM Neural Network en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Anker, P. (1999) “Uncovered Interest Parity, Monetary Policy and Time-Varying Risk Premia” Journal of International Money and Finance, Elsevier, Vol. 18(6), Pages 835-851.2. Bengoechea, A.G., Uretaz, C. O., Saavedra, M. M., & Medina, N. O. (1996) “Stock Market Indices in Santiago de Chile: Forecasting Using Neural Networks” IEEE International Conference on Neural Networks , Vol. 4, Pages 2172-21753. Bildirici, M., & Ersin, Ö. Ö. (2009) “Improving Forecasts of GARCH Family Models with the Artificial Neural Networks” Journal Expert Systems with Applications: An International Journal archive, Vol. 36, Issue 44. 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 581–5885. Cavallo, M. (2006) ”Interest Rates, Carry Trades, and Exchange Rate Movements” FRBSF Economic Letter6. Chinn, M. D., & Meredith, G. (2004) “Monetary Policy and Long-Horizon Uncovered Interest Parity” IMF Staff Papers – Vol. 51, No 37. Fama, E. F. (1984) “Forward and Spot Exchange Rates” Journal of Monetary Economics, Vol. 14, Issue 3, Pages 319-3388. Fischer, T., & Krauss, C. (2017) “Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions” European Journal of Operational Research, Vol. 270, Issue 2, Pages 654-6699. Froot, K. A., & Thaler, R. H. (1990) “Anomalies: Foreign Exchange.” Journal of Economic Perspectives, 4(3):179-192.10. Hochreiter, S., & Schmidhuber, J. (1997) “Long Short-Term Memory” Neural Computation 9(8): 1735-8011. Hutchison, M., & Sushko, V. (2013) “Impact of Macro-Economic Surprises on Carry Trade Activity” Journal of Banking & Finance, Vol. 37, Issue 4, Pages 1133-114712. Jylhä, P., Lyytinen, J. P., & Suominen, M. (2008) “Arbitrage Capital and Currency Carry Trade Returns” AFA 2009 San Francisco Meetings Paper13. Kimoto, T., & Asakawa, K. (1990) “Stock Market Prediction System with Modular Neural Networks” 1990 IJCNN International Joint Conference on Neural Networks, Vol. 1, Pages 1-614. Lustig, H., Roussanov, N., & Verdelhan, A. (2011) “Common Risk Factors in Currency Markets” The Review of Financial Studies, Vol. 24 ,Issue 11, Pages 3731-377715. Lustig, H., & Verdelhan, A. (2005) “The Cross-Section of Currency Risk Premia and US Consumption Growth Risk” NBER Working Paper No. 1110416. MacDonald, R., & Torrance, T. S. (1989) “Some Survey Based Tests of Uncovered Interest Parity” In MacDonald, R. and Taylor, M.P. (eds.) Exchange Rates and Open Economy Macroeconomics, Pages 239-24817. Namin, S. S., & Namin, A. S. (2018) “Forecasting Economic and Financial Time Series: ARIMA VS. LSTM”18. Persio, L. D., & Honchar, O. (2017) “Recurrent Neural Networks Approach to the Financial Forecast of Google Assets” International Journal of Mathematics and Computers in Simulation, Vol. 11, Pages 7-1319. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) “Learning Internal Representations by Error Propagation” DTIC Document, Tech. Rep.20. Santos, M. B. C., Klotzle, M. C., & Pinto, A. C. F. (2016) “Evidence of Risk Premiums in Emerging Market Carry Trade Currencies” Journal of International Financial Markets, Institutions & Money, Vol. 44, Pages103-11521. Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2002) “Combining Neural Network Model with Seasonal Time Series ARIMA Model” Technological Forecasting & Social Change, Vol. 69, Issue 1, Pages 71–8722. Yim, J. (2002) “A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index.” Lecture Notes in Computer Science, Pages 25-35 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900148 en_US
