臺灣博碩士論文加值系統

本論文涵蓋匯率預測與匯率平價假說檢定等兩篇論文。每篇論文包含理論推導、研究方法步驟詳述與實證結果說明。第一篇文章為透過證券市場所萃取之純價格通膨率，來檢定亞洲國家如香港、韓國、馬來西亞、新加玻、泰國、台灣與美國間之相對購買力平價假說是否成立。Chowdhry et al. (2005) 文章認為相對購買力平價假說不成立的原因，也許與官方通貨膨脹率 (official price inflation rates) 乃由一般商品價格所建構有關，而這關連起因於商品的名目價格具有僵固性的特質。因此，Chowdhry et al. (2005) 等精心且嚴謹地經由費雪方程式 (Fisher equation) 與Fama-French 三因子模型 (three factors model) 建構一個無風險性資產之名目報酬模型去萃取純通貨膨脹率 (pure price inflation rates) 。該模型所萃取之純價格通膨率應用在美國、日本、英國與德國之相對購買力平價假說的實證結果是成立的。但是，本文所興趣的目標是亞洲地區，是否該論述在亞洲地區是否成立呢? 實證結果顯示，最小平方法 (ordinary least squares, OLS) 和受限制近似無相關迴歸 (constrained seeming unrelated regression, SUR) 及 SUR系統方程式 (system equation) 都拒絕相對PPP假設。但是，利用Im et al. (2003), Maddala and Wu (1999), and Pesaran (2007) 等人的追蹤資料單跟檢定 (panel unite root test) 卻拒絕實質匯率單根假設。
本論文的第二篇文章的研究主題，驗證名目匯率的預測表現，利用Chowdhry et al. (2005) 文章的理論模型與實證方法計算出美國、日本、德國與英國之純價格通膨脹率，並利用Granger and Terasvirta (1993) 所提出的非線性平滑轉換之自我回歸 (STR，smooth transition autoregressive) 模型去建構包含純價格通膨率之非線性匯率預測模型，比較該模型與隨機漫遊 (random walk) 模型之匯率預測表現。Berkowitz and Giorgianni (2001) 建議，如果事先假設匯率與基要 (fundamental) 間之誤差修正項為恆定，則長期匯率預測能力較佳之實證結論會是錯誤的估計。主要理由是，即使匯率預測模型之母體係數實際上為零的情況下，估計係數的漸進分配還是會隨著估計長度的增加而提高。簡而言之，假設檢定臨界值 (critical values) 之小樣本模擬，會受匯率與基要間是否共整合假設條件成立的影響。因此，本文企圖建構一個匯率與基要間實證結果存在共整合關係下的非線性匯率預測模型，而非假設其共整合關係存在下的非線性匯率預測模型。實證結果顯示，德國與英國之相對 PPP 成立，日本之相對 PPP不成立。而本研究所建構之匯率測模型在英國與德國的預測表現，相對於隨機漫遊模型是更準確。日本則沒有上述表現。

This dissertation includes two chapters in the field of international finances about foreign exchange rate predictability and testing purchase power parity. In each chapter, we build the theory, methodology, and the empirical results to present the paper’s construction. The first chapter, we studies whether the pure price inflation rate which is extracted from stock return can help us to test the relative of purchasing power parity in where Asian countries include Malaysia, Korea, Taiwan, Thailand, Hong Kong, and Singapore against the United States. The paper of Chowdhry et al. (2005) argue that relative PPP may not hold for the official price inflation rates which is constructed from consumer price indices, since relative price changes and other frictions cause price to be sticky. Thus, they use the Fisher equation and Fama-French three factors elaborately to build up a model on the nominal return of real risk-free asset to extract the pure price inflation rates. Their argument is supported in the case of Japan, Germany, the United States, and the United Kingdom. We are interested in the case of some Asian countries. So, this chapter, we extend the model and methodology of Chowdhry et al. (2005) to test the relative PPP for Asian countries. If our empirical evidence is firmly supported, it will be a strongly reconfirmed the elaborated idea of Chowdhry et al. (2005).
In our study, the PPP rule is not supported for Asian countries since joint null hypothesis of a=0 and b=1 are rejected at all horizons except Taiwan at monthly horizon. The testing results by constrained seeming unrelated regression (SUR) and system equation in pooled data are similar to the tests of country-by-country. Therefore, we apply the methods of panel unite root from Im et al. (2003), Maddala and Wu (1999), and Pesaran (2007) to test the PPP doctrine, and it is strongly supported PPP for Asian countries.
The second chapter, we extract the estimated data of pure price inflation by Chowdhry et al. (2005), and use the data to build up a nonlinear STR (smooth transition autoregressive) model by Granger and Teräsvirta (1993), then compare the performance of linear or nonlinear model of exchange rate predictability with random walk model in the United States, the United Kingdom, Japan, and Germany. This study has presented evidences that the extracted inflation rates offer a good predictability on the prediction of exchange rate for the United Kingdom and Germany. Those extracted data in which are calculated from the industry portfolio returns of stock market. The issue of series correlation in regression error does matter the estimated coefficients βk, thus we estimate the simulation of Gaussian bootstrap distribution for testing variables with Newey West standard deviation in regression estimate. The empirical evidences show that the PPP doctrine affects the predictability performance of exchange rate change by the extracted inflation rates.

Contents

Chapter 1: Relative Purchasing Power Parity Test by Extracting Inflation Rates from Stock Market for Asian Countries

1.1 Introduction…………………………………............... 1
1.2
Theoretical Framework and Empirical Methodology…………………….. 12
1.2.1 Two-Parameter Portfolio Model…………………………………….. 12
1.2.2 Extracting Inflation from Stock Market…………………………….. 13
1.2.3 Relative PPP Test…………………………………………………… 16
1.3 Data and Empirical Results………………………………………………. 19
1.3.1 The Risk-Free Industry Return Rate……………………………….. 20
1.3.2 PPP Test……………………………………………………………... 21
1.3.3 Results from Pooled Regressions…………………………………… 24
1.3.4 Panel Unit Root Tests……………………………………………….. 26
1.4 Concluding Remarks………………………………………………………... 29
1.5 References……………………………………………………………………. 31
Chapter 2: Exchange Rate Forecasting by Extracting Inflation Rates From Stock Returns


2.1 Introduction………………………………………………………………… 50
2.2 Theoretical Framework……………………………………………………. 54
2.3 Empirical Methodology…………………………………………………… 59
2.3.1 Use Two-Stage Regression Methodology to Obtain Rft and Rft* ..
60
2.3.2 Testing Linearity…………………………………………………….. 61
2.3.3 Exchange Rate Prediction…………………………………………… 65
(A) A Linear Case……………………………………………………….. 66
(B) A Nonlinear Case……………………………………………………. 72
2.4 Data and Empirical Results………………………………………………... 73
2.4.1 Results under the Fama-French Three Factors Model………………. 74
2.4.2 Consequences of Linearity Test…………………………………….. 77
2.5 Conclusions…………………………………………………………………. 80
2.6 References…………………………………………………………………... 84

 
Tables and Figures
Table 1.1 Data Summarized Statistics…………………………………………. 37
Table 1.2 Time Series Regressions of Excess Industry Return on the Fama-French Three Factors (The United States)…………………………. 38
Table 1.3 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Taiwan)………………………… 39
Table 1.4 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Hong Kong)…………………. 40
Table 1.5 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Korea)…………………………. 41
Table 1.6 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Malaysia)………………………… 42
Table 1.7 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Thailand)………………………….. 43
Table 1.8 Time Series Regressions of Excess Industry Return from the Fama-French Three Factors Model (Singapore)………………………… 44
Table 1.9 Summary Statistic for Extracted Risk-rate Differentials, CPI Inflation Rate Differentials, and Foreign Exchange Rate Changes………………………………………………………... 45
Table 1.10 The Empirical Evidences of PPP Regression Results by Extracted Risk-Free Rate from Fama-French Three Factors Model…………... 46
Table 1.11 Constrained Seeming Unrelated PPP Regressions Testing Results…. 47
Table 1.12 Constrained Seeming Unrelated PPP Regressions Testing Results
(System of Equation)………………………………………………... 48
Table 1.13 Panel Unit-Root Testing Results……………………………………. 49
Table 2.1 Time Series Regressions of Excess Industry Return on the Fama-French Three Factors (The United States)………………………….. 87
Table 2.2 Time Series Regressions of Excess Industry Return on the Fama-French Three Factors (The United Kingdom)……………………… 88
Table 2.3 Time Series Regressions of Excess Industry Return on the Fama-French Three Factors (Japan)……………………………………….. 89
Table 2.4 Time Series Regressions of Excess Industry Return on the Fama-French Three Factors (Germany)…………………………………… 90
Table 2.5 PPP Test Evidences Using Extracted Risk-Free Rate by Fama-French Three Factors ………………………… 91
Table 2.6 Empirical Results of Linearity Test for Fundamental Zt ………92
Table 2.7 Regression Estimates and t Ratio Bootstrap Distributions………….. 93
Table 2.8 Root Mean Square Error (RMSE) Estimates………………………... 94
Table 2.9 Diebold-Mariano (DM) for Comparing Predictive Accuracy………. 95
Figure 2.1 Transition Function of United Kingdom on LSTR2 model………… 96
Figure 2.2 Transition Function of Japan on LSTR1 model…………………….. 96

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