本篇論文主要在外匯市場建立市場投資組合、利差交易投資組合與動能交易投資組合，探討透過不同情境適當改變投資組合比重配置，是否能夠顯著提升交易策略的報酬表現。 以1999年1月至2015年10月為樣本期間，根據28個國家外匯市場資料建構市場投資組合、利差交易投資組合與動能交易投資組合等，之後根據三種投資組合報酬情況透過馬可夫情境轉換模型區分成三種情境。按三種情境的各種投資組合超額報酬表現，再利用馬可維茲的平均數-變異數投資組合模型配置各情境下各項交易的比重，再依據計算出的預期情境與相對應比重進行投資。其結果顯示在樣本期間內，本篇論文的交易策略相較於外匯市場投資組合、利差交易投資組合與動能交易組合有較佳的投資表現。 在樣本外測試部分，採用自2012年中開始的連續情境二資料進行分析。報酬方面，在其他交易型態呈現負報酬較多情況下，就本文交易策略而言，投資者隨時根據其各種交易平均報酬與共變異數進行交易比重配置，適時放空交易策略或投資無風險資產，產生正報酬。但從標準差可以推斷投資者面對未來的不確定，在整個樣本外期間歷時的34個月當中標準差亦無法有效降低，說明了投資者面對下一期總體環境的高不確定性。 / In this thesis, we mainly investigate whether it could improve the performance of currency portfolio by adjusting weights among carry trade, momentum and market return in foreign exchange market under different kinds of regimes. Based on a sample of 28 market currencies, we form three kinds of transactions in our portfolio, including carry trade, momentum, and market return. Under Markov switching model, we divide the sample period into three regimes, and then determine weights among carry trade, momentum and market return by parameters of each re-gime using Markowitz mean-variance analysis. Finally, we invest different weights among three transactions according to each expected regime. We find the result that although the return of the strategy is just a little higher than the carry trade, the risk is much lower compared to other transactions. In our out-of-sample testing, we analyze the performance by using the data of the regime two which begins September, 2012. With the respect to the return, most of other risky transactions have negative return, but we get positive return by adjusting the long position and short position according to the result of the mean-variance anal-ysis. However, we can not effectively reduce risk by using the strategy, and in the meantime it can explain the high uncertainty investors face toward the next period.

本研究主要是以「外匯報酬三因子模型」為基礎，故先檢視在本樣本期間內(1985/2至2016/10) ，以雙分類法將37國主流貨幣分為9個投組後，外匯超額報酬解釋力，是否會因加入動能策略因子形成之三因子模型，而較原本兩因子模型(市場因子、利差策略因子)來的強？最終測得三因子模型在判斷係數及殘差等適切度表現較佳。 接著利用逐步迴歸分析法(限制所有自變數均須於90%信心水準內顯著)嘗試尋找獲利成因，主要挑選出不同面向之11種經濟成因因子(股價指數波動、投機活動、流動性、貨幣波動、落後短期利率、落後股利率、落後期限利差、落後違約利差、)落後避險基金套利資本、工業生產量及通膨率因子)來檢測可否解釋三因子模型中獲取報酬之利差、動能策略因子，並利用Fama-MacBeth兩步驟橫斷面迴歸法評估模型市場定價能力。結果發現定價能力均顯著，而利差交易策略之成因為股價指數波動因子(△EVOL)，因其可能連動匯率波動而呈現負相關；動能交易策略成因則為股價指數波動因子(△EVOL)及落後期限利差因子(△LTS)，主要因動能交易主要來自於市場資訊反應不完全，前者成因因子提供更大的動量執行交易策略、後者則因投資人在不同景氣循環下而有不同的投資反應，如景氣擴張的過度自信與樂觀、景氣衰退下產生行為財務領域中的處置效果，使兩成因與動能策略因子呈現正相關。 / This paper is based on the model of three-factors foreign exchange returns. So we test whether three-factors FX model which adds the factor of momentum can have stronger ability to explain currency excess return than two-factors FX model in the sampling period of February 1985 to October 2016. And the 37 kinds of currency are sorted by double sort method and become 9 portfolios. Finally, no matter coefficient of determination or residual error, three-factors FX model performs well. Further, we use stepwise LS regression (independent variable should have statistical significance in 90% confidence interval) to find which factor we choose can cause carry and momentum strategy profit in three-factors FX model. Next, using Fama-MacBeth two-step regression to estimate the asset pricing ability. The results represent that all contribution factors which get from stepwise LS method are significant. Carry trade strategy and △EVOL are negative correlation, because volatility of stock index will influence volatility of FX. And there have the positive correlation between momentum trade strategy and two factors(△EVOL and △LTS). Just because the profit from momentum strategy comes from the incomplete reaction of market information and △EVOL give more motive force. Besides, there have different investment reactions in diverse business cycle. Investors are over confident and optimistic during the period of recession and have disposition effect during the period of boom.