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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 楊睿中 | zh_TW |
dc.contributor.advisor | Jui-Chung Yang | en |
dc.contributor.author | 李書甫 | zh_TW |
dc.contributor.author | Shu-Fu Lee | en |
dc.date.accessioned | 2023-08-09T16:36:51Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-09 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-25 | - |
dc.identifier.citation | Adrian, Tobias, and Joshua Rosenberg (2008), Stock returns and volatility: Pricing the short-run and long-run components of market risk, Journal of Finance 63, 2997–3030.
Ahmed, S., Liu, X., & Valente, G. (2016). Can currency-based risk factors help forecast exchange rates?. International Journal of Forecasting, 32(1), 75-97. Akram, Q. F., Rime, D., & Sarno, L. (2008). Arbitrage in the foreign exchange market: Turning on the microscope. Journal of International Economics, 76(2), 237-253. Alexander, D., & Thomas III, L. R. (1987). Monetary/Asset models of Exchange Rate Determination: How well have they Performed in the 1980's?. International Journal of Forecasting, 3(1), 53-64. Ang, A., & Chen, J. (2011). Yield curve predictors of foreign exchange returns. In AFA 2011 Denver Meetings Paper. Ang, Andrew, Robert Hodrick, Yuhang Xing, and Xiaoyan Zhang (2006), The cross-section of volatility and expected returns, Journal of Finance 61, 259–299. Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry trade and momentum in currency markets. Annu. Rev. Financ. Econ., 3(1), 511-535. Christiano, L. J. and T. J. Fitzgerald (2003). The Band Pass Filter. International Economic Review 44:2, 435-465. Engel, C., & West, K. D. (2005). Exchange rates and fundamentals. Journal of political Economy, 113(3), 485-517. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56. Fama, Eugene F., and James MacBeth, (1973), Risk, return and equilibrium: Empirical tests, Journal of Political Economy 81, 607–636. Filippou, I., Gozluklu, A. E., & Taylor, M. P. (2018). Global political risk and currency momentum. Journal of Financial and Quantitative Analysis, 53(5), 2227-2259. Filippou, I., Rapach, D., Taylor, M. P., & Zhou, G. (2022). Out-of-Sample Exchange Rate Prediction: A Machine Learning Perspective. Available at SSRN 3455713. Franzoni, F. (2006). Where is Beta Going?. Groupe HEC. Giglio, S., & Xiu, D. (2021). Asset pricing with omitted factors. Journal of Political Economy, 129(7), 1947-1990. Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return., Journal of Financial Economics, 134(3), 501-524. Kunsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. The annals of Statistics, 1217-1241. Lettau, M., M. Maggiori, and M. Weber (2014): Conditional Risk Premia in Currency Markets and Other Asset Classes, Journal of Financial Economics, 114, 197–225. Lustig, Hanno, and Adrien Verdelhan, (2007), The cross section of foreign currency risk premia and consumption growth risk, American Economic Review 97, 89–117. Lustig, H., N. Roussanov, and A. Verdelhan (2011): Common Risk Factors in Currency Markets, Review of Financial Studies, 24, 3731–3777 Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample?. Journal of international economics, 14(1-2), 3-24. Menkhoff, L., L. Sarno, M. Schmeling, and A. Schrimpf (2012): Carry Trades and Global Foreign Exchange Volatility, Journal of Finance, 67, 681–718. Nucera, F., Sarno, L., & Zinna, G. (2021). Currency Risk Premia Redux. Available at SSRN 3796290. Santos, T., & Veronesi, P. (2004). Conditional betas. Taylor, M. P. (1989). Covered interest arbitrage and market turbulence. The Economic Journal, 99(396), 376-391. Verdelhan, A. (2018). The share of systematic variation in bilateral exchange rates. The Journal of Finance, 73(1), 375-418. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88338 | - |
dc.description.abstract | 本文利用Kelly, Pruitt, and Su (2019) 提出的工具變量主成分分析(Instrumented Principle Component Analysis, IPCA) 作為實證模型,對G10國家貨幣的超額報酬進行分析。IPCA是一種線性隱因子模型(linear latent factor model),允許研究者利用資產報酬以外的資產特徵估計風險因子(risk factor)和動態因子載荷(dynamic factor loadings)。本文以擴展窗口(expanding window)的方式遞歸地對樣本外資料進行預測,實證結果顯示IPCA模型對貨幣超額報酬在樣本外的預測表現優於隨機漫步模型(random walk model)和傳統上以PCA估計的線性隱因子模型。特徵重要性分析結果顯示利率相關變數如短、中、長期公債殖利率差額對貨幣超額報酬最為重要。同時本文也發現IPCA模型不止對貨幣超額報酬有很好的預測能力,對匯率一般報酬的預測表現也優於隨機漫步模型。 | zh_TW |
dc.description.abstract | This paper utilizes the Instrumented Principle Component Analysis (IPCA) proposed by Kelly, Pruitt, and Su (2019) as an empirical model to analyze the excess returns of G10 currencies. IPCA is a linear latent factor model that allows researchers to estimate risk factors and dynamic factor loadings using characteristics information other than asset returns. This paper employs an expanding window approach to recursively forecast out-of-sample data. The empirical results show that the IPCA model outperforms the random walk model and the traditional linear latent factor model estimated via PCA in predicting currency excess returns. The results of the feature importance analysis indicate that interest rate related variables, such as the yield spread of short-term, medium-term, and long-term government bonds between a given country and US, are the most important for currency excess returns. Additionally, this paper also finds that the IPCA model not only exhibits good predictive ability for currency excess returns but also outperforms the random walk model in predicting general exchange rate returns. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:36:51Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-09T16:36:51Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 謝辭 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 1 第二章 文獻回顧 3 第三章 研究方法 6 3.1 實證模型 6 3.2 模型估計 7 3.2.1 無限制式模型 Γ_α≠0_(L×1) 7 3.2.2 限制式模型 Γ_α=0_(L×1) 8 3.3 資產定價檢定 9 3.3.1 檢定 Γ_α=0_(L×1) 10 3.3.2 檢定個別資產特徵顯著性 11 第四章 實證資料與結果 12 4.1 匯率資料 12 4.2 國家特徵資料 13 4.3 樣本內模型表現 15 4.4 樣本外模型表現 17 4.5 特徵顯著性 18 4.6 特徵貢獻度 19 4.7 特徵與因子載荷 20 4.8 穩健性測試 21 第五章 結論 23 參考文獻 24 附錄 27 附錄一、區塊拔靴法(block bootstrap) 27 | - |
dc.language.iso | zh_TW | - |
dc.title | 樣本外匯率預測:使用IPCA | zh_TW |
dc.title | Out-of-Sample Exchange Rate Prediction: Using IPCA | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 傅萱;陳由常 | zh_TW |
dc.contributor.oralexamcommittee | Hsuan Fu;Yu-Chang Chen | en |
dc.subject.keyword | 匯率,預測,隱因子模型,主成分分析,資產定價, | zh_TW |
dc.subject.keyword | Forex,Prediction,Latent factor model,PCA,Asset pricing, | en |
dc.relation.page | 27 | - |
dc.identifier.doi | 10.6342/NTU202302033 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-07-26 | - |
dc.contributor.author-college | 社會科學院 | - |
dc.contributor.author-dept | 經濟學系 | - |
顯示於系所單位: | 經濟學系 |
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