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題名：名目匯率預測與股匯市之間的相關性
作者：王翊全
作者(外文)：Wang, Yi-Chiuan
校院名稱：國立中正大學
系所名稱：國際經濟研究所
指導教授：吳致寧
學位類別：博士
出版日期：2011
主題關鍵詞：組合預測；名目匯率；市場基要；經濟顯著；尾部(極端)相關；相關結構；馬可夫轉換；GARCH；Copulas；forecast combination；nominal exchange rates；economic significance；Copulas；tail dependence；dependence structure；Markov-switching；GARCH；fundamentals
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本論文研究有關於名目匯率的兩個主題，一是經濟模型建構的市場基要對名目匯率預測能力的實證研究，另一部分則探討股匯市的相關性結構。在第一部分，我們採用簡單平均加權結合四種不同市場基要去探索在不同期間下，預測名目匯率的經濟與統計顯著性。利用1973-2009的月資料我們得到下列幾個發現: 第一，利用泰勒法則建構的市場基要具有較佳的短、中期預測能力，十一個樣本國家分別有六個及五個國家擊敗隨機漫步模型，而利用貨幣學派及購買力評價學說所建構的市場基要則在中期對名目匯率有預測能力，然而，利用經濟模型所作的匯率預測則在長期較不具預測能力。其次，不論是何種期間的預測，結合不同市場基要所做的預測皆可增強單一市場基要擊敗隨機漫步模型的預測能力。第三、不論是何種期間的預測，隨著結合市場基要的個數增加，擊敗隨機漫步模型的比率也跟著提高。第四，結合市場基要的個數增加可以增強統計的顯著性。第五，無論是何種預測期間，結合預測都有較強的經濟顯著性。
本論文的第二部份結合Copula理論與Markov-switching模型探討股匯市之間的相關性結構。利用G6國家1990至2010年間的日資料，本文得到下列幾個重要的實證現象。第一、在多數期間大部分國家都處於匯率曝險效果或是資產重組效果。第二、股匯市四種狀態的組合呈現不同的相關性與尾部(極端)相關，對於全球性的投資人評估風險及衡量金融危機的市場風險有極大助益。第三、歐元引進之後，歐元國家及英國的股匯市之間的平滑相關係數時間趨勢極為相似。最後，股匯市的相關性與尾部(極端)相關具有不對稱性，意即股匯市同時崩盤的相關性強於兩個市場同時大漲的相關性，此與現存文獻的結論有明顯對比。
This dissertation is comprised of two topics related to nominal exchange rate, one is about the predictability of nominal exchange rate and the other is for the correlation between equity and foreign exchange markets.
The first part of this dissertation adopts a simple average weight to combine forecasts from different fundamentals-based forecasts and then examines the statistical and economic significance of forecast combination under different forecast horizons. Empirical investigation based on monthly data over 1973-2009 results in several interesting findings. First, TR-based forecasts reject RW forecasts in six and five out of eleven countries at short and medium forecast horizons. M- and PPP-based forecasts reveal evidence of beating random walk at medium horizons. Fundamentals-based forecasts reveal little evidence to beat RW forecasts at long horizons. Second, combining forecasts from different fundamentals that have predictability over a specific horizon is generally promising to enhance the evidence of beating random walks over that horizon. Third, the average percentage of beating random walks at a specific forecast horizon by forecast combination increases with the number of fundamentals-based forecasts being combined. Fourth, increasing the number of forecasts being combined increases statistical significance of forecast combination. Fifth, forecast combination results in stronger economic significance than random walk regardless of horizons.
The second part of this dissertation provides a dependence-switching copula model to describe the dependence structure between stock and foreign exchange markets and obtains the following interesting results. First, exchange-rate exposure effects or portfolio rebalancing effects is observed for most countries at most times. Second, dependence and tail dependence between the two markets differ among the four combinations of market status, which is essential for safety-first agents investing globally, for correctly evaluating value-at-risk and for measuring systematic risk in financial crisis. Third, the smoothing correlation between the above two markets are similar for euro countries and the United Kingdom throughout the post-euro period. Finally, the dependence and tail dependence between stock and foreign exchange markets when both markets are booming generally differ with those when both markets are crashing.
Ang, A. and G. Bekaert. (2002) “International Asset Allocation with Regime Switching.” Review of Financial Studies, 15, 1137-1187.
Ang, A. and J. Chen. (2002) “Asymmetric Correlations of Equity Portfolios.” Journal of Financial Economics, 63, 443-494.
Bates, J. M. and C. W. J. Granger. (1969) “The Combination of Forecasts.” Operations Research Quarterly, 20, 451–468.
Berben, R. B. and D. J. van Dijik. (1998) “Does the Absence of Cointegration Explain the Typical Findings in Long Horizon Regressions.” Report 9814. Econometrics Institute, Erasmus University of Rotterdam.
Berkowitz, J. and L. Giorgianni. (2001) “Long-Horizon Exchange Rate Predictability?” Reviews of Economics and Statistics, 83 (1), 81–91.new window
Bollerslev, T. (1987) “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return.” Review of Economics and Statistics, 69(3), 542-547.
Cherubini, U., E. Luciano, and W. Vecchiato. (2004), Copula Methods in Finance. John Wiley and Sons, England.
Cheung, Y.-W., M. D. Chinn, and A. G. Pascual. (2005) “Empirical Exchange Rate Models of the Nineties: Are They Fit to Survive?” Journal of International Money and Finance, 24 (7), 1150–1175.
Chinn, M. and R. Meese. (1995) “Banking on Currency Forecasts: How Predictable is Change in Money?” Journal of International Economics, 38 (1–2), 161–178.
Chollete, L., A. Heinen, and A. Valdesogo. (2008) “Modeling International Financial Returns with a Multivariate Regime Switching Copula.” MPRA Paper 8114, University Library of Munich, Germany.
Chow, E. H., W. Y. Lee, and M. E. Solt. (1997) “The Exchange-Rate Risk Exposure of Asset Returns.” Journal of Business, 70(1), 105-123.new window
Clarida, R., J. Gali, and M. Gertler. (1997) “Monetary Policy Rules and Macroeconomic Stability: Theory and Some Evidence.” Mimeo. New York University.
Clark, T. E. and K. D. West. (2006) “Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis.” Journal of Econometrics, 135 (1–2), 155–186.
Clark, T. E. and K. D. West. (2007) “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics, 138 (1), 291–311.new window
Clark, T. E. and M. W. McCracken. (2006) “The Predictive Content of the Output Gap for Inflation: Resolving In–Sample and Out–of–Sample Evidence.” Journal of Money, Credit, and Banking, 38, 1127–1148.
Clement, M. and D. Hendry. (2004) “Pooling of Forecast.” Econometrics Journal, 7 (1), 1–31.new window
Costinot, A., T. Roncalli, and J. Teiletche. (2000) “Revisiting the Dependence between Financial Markets with Copulas.” Working Paper.
Cumperayot P., T. Keijzer, and R. Kouwenberg. (2006) “Linkages between Extreme Stock Market and Currency Returns.” Journal of International Money and Finance, 25, 528-550.
Diebold, F. X. and R. S. Mariano. (1995) “Comparing Predictive Accuracy.” Journal of Business and Economic Statistics, 13 (3), 253–262.
Diebold, F. X., T. Schuermann, and J. Stroughair. (2000) “Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management.” Journal of Risk Finance, 1, 30–36.new window
Dominguez, K. M. E. and L. L. Tesar. (2001) “A Reexamination of Exchange-Rate Exposure.” American Economic Review, 91(2), 396-399.
Engel, C., N. C. Mark, and K. D. West. (2007) “Exchange Rate Models Are Not as Bad as You Think.” NBER Macroeconomics Annual, 22, 381–441.
Garcia, R. and G. Tsafack. (2008) “Dependence Structure and Extreme Comovements in International Equity and Bond Markets with Portfolio Diversification effects.” Technical Report, CIRANO.
Hartman, P., S. Straetmans, and C. de Vries. (2004) “Asset Market Linkages in Crisis Periods.” Review of Economics and Statistics, 86, 313-326.
Hau H. and H. Rey. (2006) “Exchange Rates, Equity Prices, and Capital Flows.” Review of Financial Studies, 19(1), 273-317.new window
Hodrick, R. J. and E. C. Prescott. (1997) “Postwar U.S. Business Cycles: An Empirical Investigation.” Journal of Money, Credit, and Banking, 29, 1–16.
Hu, L. (2006) “Dependence Patterns across Financial Markets: A Mixed Copula Approach.” Applied Financial Economics, 16, 717-729.
Ince, O. (2010) “Forecasting Exchange Rates Out-of-Sample with Panel Methods and Real-Time Data.” Working Paper.
Joe, H. (1997), Multivariate Models and Dependence Concepts. London: Chapman & Hall.
Joe, H. and J. J. Xu. (1996) “The Estimation Method of Inference Functions for the Margins for Multivariate Models.” Technical Report 166, Department of Statistics, University of British Columbia.
Jorion, P. (1990) “The Exchange-Rate Exposure of U.S. Multinationals.” Journal of Business, 60(3), 331-345.
Kilian, L. (1999) “Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?” Journal of Applied Econometrics, 14 (5), 491–510.
Kilian, L. and M. P. Taylor. (2003) “Why is it So Difficult to Beat the Random Walk Forecast of Exchange Rates?” Journal of International Economics, 60 (1), 85–107.new window
Lam, L., L. Fung, and Ip-wing Yu. (2008) “Comparing Forecast Performance of Exchange Rate Model.” Working Paper.
Longin, F. and B. Solnik. (2001) “Extreme Correlation of International Equity Markets.” Journal of Finance, 56, 649-676.
Mark, N. C. (1995) “Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability.” American Economic Review, 85 (1), 201–218.new window
McCloskey, D. N. and S. T. Ziliak. (1996) “The Standard Error of Regressions.” Journal of Economic Literature, 34, 97–114.
Meese, R. A. and K. Rogoff. (1983) “Empirical Exchange Rate Models of Seventies: Do They Fit Out-of-Sample?” Journal of International Economics, 14 (1–2), 3–24.
Molodtsova, T. and D. H. Papell. (2009) “Out-of-Sample Exchange Rate Predictability with Taylor Rule Fundamentals.” Journal of International Economics, 77, 167–180.
Nelsen, R. B. (1999), An Introduction to Copulas. New York: Springer-Verlag.
Ning, C. (2010) “Dependence Structure between the Equity Market and the Foreign Exchange Market–A Copula Approach.” Journal of International Money and Finance, forthcoming.
Obstfeld, M. and K. Rogoff. (2000) “The Six Major Puzzles in International Macroeconomics: Is There a Common Cause?” NBER Macroeconomics Annual, vol. 15.
Okimoto, T. (2008) “New Evidence of Asymmetric Dependence Structures in International Equity Markets.” Journal of Financial and Quantitative Analysis, 43(3), 787-816.
Patton, A. (2004) “On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation.” Journal of Financial Econometrics, 2, 130-168.
Patton, A. (2006) “Modelling Asymmetric Exchange Rate Dependence.” International Economic Review, 47, 527-556.
Pelletier, D. (2006) “Regime-Switching for Dynamic Correlation.” Journal of Econometrics, 131, 445-473.
Poon, S.-H., M. Rockinger, and J. Tawn. (2004) “Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications.” Review of Financial Studies, 17(2), 581-610.
Rodriguez, J. C. (2007) “Measuring Financial Contagion: A Copula Approach.” Journal of Empirical Finance, 14-3, 401-423.
Rogoff, K. and V. Stavrakeva. (2008) “The Continuing Puzzle of Short Horizon Exchange Rate Forecasting.” NBER working paper, 13882.
Rossi, B. (2005) “Testing Long-Horizon Predictive Ability with High Persistence, and the Meese–Rogoff Puzzle.” International Economic Review, 46 (1), 61–92.new window
Sklar, A. (1959) “Fonctions de Répartition á n Dimensions et Leurs Marges.” Publications de l’ Institut Statistique de l’Université de Paris, 8, 229-231.
Stock, J. H. and M. W. Watson. (2001) “Forecasting Output and Inflation: the Role of Asset Prices.” Mimeo.
Stock, J. H. and M. W. Watson. (2002) “Combination Forecasts of Output Growth in a Seven Country Dataset.” Mimeo.
Susmel, R. (2001) “Extreme Observations and Diversification in Latin American Emerging Equity Markets.” Journal of International Money and Finance 20, 971-986.
Tastan, H. (2006) “Estimating Time-varying Conditional Correlations between Stock and Foreign Exchange Markets.” Physica A 360, 445-458.
Wright, J. H. (2008) “Bayesian Model Averaging and Exchange Rate Forecasts.” Journal of Econometrics, 146, 329–341.

 
 
 
 
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