匯率報酬之非線性調整與經濟價值可預測性

Nonlinear Adjustment and Economic Value Predictability of Exchange Rate Returns

申志偉(Chih-Wei Shen)

指導教授 ： 吳博欽

中原大學/商學院/國際貿易研究所/碩士(2007年)

https://doi.org/10.6840/cycu200700653

摘要

多數狀態轉換模型可提供相較於線性模型較佳的配適度，但樣本外預測能力
卻難以推翻Meese and Rogoff (1983) 指出名目匯率不可預測的結論，亦即名目匯
率呈現隨機漫步 (Random Walk) 走勢，且投資者無法在外匯市場賺取超額報酬。
文獻上衡量匯率模型的預測能力，大多建立在預測誤差等衡量指標，但較低的預
測誤差並不保證具有較高的收益性或經濟價值。因此，本研究認為狀態轉換模型
所提供的經濟價值是值得探討的議題。
本研究主要探討英鎊與日元等兌美元之報酬在1990 年至2005 年期間，應用
Teräsvirta and Anderson (1992) 與Granger and Teräsvirta (1993) 提出的STAR 族模
型線性檢定法，分別檢定貨幣基要 (monetary fundamentals; MF) 模型與時間序列
AR 模型是否存在STR 與STAR 的調整行為，並評估其預測誤差與經濟價值。實
證結果顯示，英鎊與日元等報酬率皆存在ST(A)R 模型的型式，分別為logistic 函
數與exponential 函數，且能提升線性模型的樣本內配適度，其中LSTAR 模型與
ESTAR 模型分別為90 年代期間英鎊與日元的最佳估計模型。樣本外預測結果則
顯示，ST(A)R 模型的預測誤差雖然無法完全優於Random Walk 模型或線性模型，
卻可提供較佳的擇時能力；對於Mean-Variance 投資者而言，ST(A)R 模型亦提供
相較於線性模型較佳的資產配置能力，且優於Random Walk 模型。其中STAR 模
型具有最佳的擇時能力與資產配置能力，並提供高報酬低風險的效率投資組合，
此結論隱含考量報酬率估計參數存在平滑式的狀態轉換，對於經濟價值具有提升
且較重要的影響程度。

關鍵字

平滑轉換迴歸 ； 貨幣基要 ； 平滑轉換自我迴歸 ； 資產配置 ； 狀態轉換模型 ； 擇時能力 ； 名目匯率

並列摘要

Most regime switching models give good in-sample fits to exchange rate data but are usually outperformed by random walks model when out-of-sample forecasts. Prior research on the ability of exchange rate models to forecast exchange rates relies on statistical measures of forecast accuracy, but lower forecast error does not necessarily imply higher profitability or economic value.
The aim of this study is to test for and model nonlinearities in the USD/GBP and USD/JPY exchange rate returns. We apply the STAR family models (STAR; STR) used by Ter鱷virta and Anderson (1992); Granger and Ter鱷virta (1993), to test nonlinearities of monetary fundamentals (MF) model and AR model, and measure the economic value of predicting USD/GBP and USD/JPY exchange rate returns. Using monthly data from 1990 to 2005. Our tests rejects the linearity hypothesis for the USD/GBP and USD/JPY exchange rate returns during the 1990s, are classified as logistic ST(A)R and as exponential ST(A)R respectively. ST(A)R models all provide better good in-sample fits than linear models. We also compare forecast error, market timing ability, and asset allocation performance of out-of-sample forecasts. ST(A)R models can not beat random walk model and linear models of USD/JPY exchange rate returns MAE or RMSE forecasting, but they can provide more market timing ability and Mean-Variance asset allocation performance than linear models of USD/GBP and USD/JPY exchange rate returns forecasts. Furthermore, STAR models are best market timing and asset allocation models, and also provided most efficiency portfolio. These findings confirm the economic value importance of accounting for the presence of regimes in exchange rate returns.

並列關鍵字

Monetary fundamentals ； Market timing ； Nominal exchange rates ； STR ； STAR ； Asset allocation ； Regime switching model

參考文獻

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