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    政大典藏 > College of Commerce > Department of Finance > Theses >  Item 140.119/136061


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136061


    Title: 以台幣及美金計價觀點檢視主要貨幣的避險特性
    Examining the Safe-haven Characteristics of Major Currencies：Evidence from New Taiwan Dollar and US Dollar Perspectives
    Authors: 葉家豪
    Yeh, Chia-Hao
    Contributors: 張元晨
    Chang, Yuan-Chen
    葉家豪
    Yeh, Chia-Hao
    Keywords: 避險貨幣
    金融危機
    複迴歸分析
    主成分分析
    Safe-haven Currencies
    Financial Crisis
    Multiple Regression Analysis
    Principal Component Analysis
    Date: 2021
    Issue Date: 2021-07-03 00:54:46 (UTC+8)
    Abstract: 　　本文以2000~2020年間主要貨幣之匯率資料，分別以美金及台幣計價的觀點，探討當投資人主要使用貨幣為美金或台幣時，主要貨幣是否能分散其投資風險，即具避險的效果。本研究主要以複迴歸模型及主成分分析的方式，檢視主要貨幣及台幣與各國股市、美國國庫券、波動度指數、泰德價差及外匯波動度之相關性，藉以判斷主要貨幣是否為避險貨幣。
    　　實證結果發現，日圓及瑞士法郎較符合避險貨幣之特性，即當股市下跌，美國國庫券價值上漲及風險指標提升時，日圓及瑞士法郎將傾向升值。若從台幣持有者的觀點來看，有避險特性的貨幣則為日圓、瑞士法郎及美金。不僅如此，本研究亦發現風險指標中僅外匯波動度對匯率之超額報酬具解釋力，以及金融危機事件並未對避險貨幣的報酬帶來額外正向的影響。
    　　I use major currencies’ data from 2000 to 2020 to examine the safe-haven characteristics of major currencies from the perspectives of both New Taiwan dollar’s and US dollar’s investors. Using multiple regression model and principal component analysis, I investigate the relationship between excess returns of major currencies and several explanatory variables, which include each country’s stock market returns, US bond prices, VIX, TED spread and FX volatility, to study whether major currencies are safe-haven currencies.
    　　The empirical results demonstrate that Japanese Yen and Swiss France behave more like safe-haven currencies. They tend to appreciate when stock market falls, US bond prices increase, and FX volatility rises. From the perspectives of New Taiwan dollar’s investors, US dollar can also have hedging benefit. Furthermore, this paper finds that FX volatility is the only risk indicator that has significant effect on safe-haven currencies, and crisis events have no extra effects on the excess returns of safe-haven currencies.
    Reference: 1. Cheema, Faff, and Szulczyk, 2020. The influence of the COVID-19 pandemic on safe haven assets. VOX, CEPR Policy Portal.
    2. Cho and Han, 2021. The tail behavior of safe haven currencies: A cross-quantilogram analysis. Journal of International Financial Markets, Institutions & Money.
    3. Coudert, Guillaumin and Raymond, 2014. Looking at the Other Side of Carry Trades: Are there any Safe Haven Currencies?, CEPII research center, No. 2014-03.
    4. Fatum and Yamamoto, 2016. Intra-safe haven currency behavior during the global financial crisis. Journal of International Money and Finance.
    5. Fan and Mitchell, 2017. Equity-Bond Correlation: A Historical Perspective. Graham Capital Management Research Note.
    6. Habib and Stracca, 2012. Getting beyond carry trade: What makes a safe haven currency? Journal of International Economics 87, 50–64.
    7. Hossfeld and MacDonald, 2015. Carry funding and safe haven currencies: A threshold regression approach. Journal of International Money and Finance, Vol. 59, 185-202.
    8. Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141-151.
    9. Kanyongo, G.Y., 2005. Determining the correct number of components to extract from a principal components analysis: A Monte Carlo study of the accuracy of the scree plot. Journal of modern applied statistical methods, 4(1), 13.
    10. Maharaj, Kelly-Scholte and Aguirre, 2020. Rethinking safe haven assets. Portfolio Insight.
    11. Mason, C. H., & Perreault Jr, W. D., 1991. Collinearity, power, and interpretation of multiple regression analysis. Journal of marketing research, 28(3), 268-280.
    12. Newey and West, 1987. A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, Vol. 55, No. 3, 703-708.
    13. O’brien, R.M., 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Qual Quant 41, 673–690. https://doi.org/10.1007/s11135-006-9018-6.
    14. Rahayu, Sugiarto, Madu, Holiawati and Subagyo, 2017. Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014. International Journal of Educational Methodology, 3(2), 75 - 83.
    15. Ranaldo and Soderlind, 2009. Safe Haven Currencies. Review of Finance.
    16. Yuki MASUJIMA, 2019. What Determines A Safe-Haven Currency? RIETI Discussion Paper Series XX-E-00X.
    17. 林志南，2007。新台幣匯率避險時機模式之探討－以新台幣、日元兌美元匯率為例。台灣碩博士論文知識加值系統，https://hdl.handle.net/11296/t7cx7g。
    18. 楊鴻洲，2017。重大事件對於外匯市場報酬與波動的影響。台灣碩博士論文知識加值系統，https://hdl.handle.net/11296/b8583j。
    19. 王子嘉，2020。避險貨幣在全球重大金融事件之探討：以日圓與瑞士法郎為例。台灣碩博士論文知識加值系統，https://hdl.handle.net/11296/53a3f6。
    20. 財經M平方全球大世紀，https://www.macromicro.me/time_line?id=17&stat=2。
    Description: 碩士
    國立政治大學
    財務管理學系
    108357029
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108357029
    Data Type: thesis
    DOI: 10.6814/NCCU202100622
    Appears in Collections:[Department of Finance] Theses

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