淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-1403200722451400
中文論文名稱 不對稱匯率波動模型的預測-以日本與新加坡匯率為例
英文論文名稱 Forecasting exchange rate with asymmetric volatility-example of JPY、SGD
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士在職專班
系所名稱(英) Department of Banking and Finance
學年度 95
學期 1
出版年 96
研究生中文姓名 劉西真
研究生英文姓名 Hsi-Chen Liu
學號 793490474
學位類別 碩士
語文別 中文
口試日期 2007-01-07
論文頁數 54頁
口試委員 指導教授-李命志
共同指導教授-陳玉瓏
委員-邱建良
委員-林卓民
委員-邱哲修
中文關鍵字 CARR  GARCH  變幅  波動性  Skew-t GARCH 
英文關鍵字 CARR  GARCH  Range  Volatility  Skew-t GARCH 
學科別分類
中文摘要 波動性在財務上扮演著關鍵的角色,若能適當的描述波動性模型,將有助於投資組合配置的最適化,進而能有效的控管風險。GARCH模型在波動性的預測上已被廣泛的應用,而且也能在實證上得到良好的成效。然而Chou (2005)將GARCH 模型結合變幅在波動性預測上的優勢進一步提出條件變幅自我相關(Conditional Auto-Regressive Range,CARR)模型,並且在S&P500 股價指數波動性預測實證上獲得優於GARCH模型的結論。本文中將介紹CARR 模型及其性質,並以日圓與星幣為研究對象,分別進行CARR 模型和Skew-t GARCH 模型在樣本內及樣本外波動性的預測能力比較;結果與Chou (2005)認為CARR模型對股價指數波動有較佳的預測結果相異。故認為CARR模型在預測波動性上並非具有完全的優勢,不同商品應適用不同的模型進行預測,以期得到最適的預測波動性,提升投資決策的效率。
英文摘要 In finance, volatility plays a key role in several sub-fields. Whether the construct
of portfolio is optimal or not, partly depends on the control of volatility. GARCH family models have been used in the forecast of volatilities, and have performed well in many empirical studies. Recently, Chou (2005) proposed the CARR
(Conditional Auto-Regressive Range) model. The main concept of the CARR model is to use a simple dynamic structure for range to characterize the volatility process. In Chou (2005), comparing the CARR model and traditional GARCH model, the former is better in the volatility forecasting based on the data of the S&P 500 index. We use both CARR and GARCH models to test JPY and SGD exchange rate. But we find that different data uses different models. In order to obtain the most accurate projection of volatility and improve the decision-making efficiency, it’s better to apply specific volatility forecast models to different products.
論文目次 第一章緒論 1
第一節研究背景與動機 1
第二節研究目的 2
第三節研究架構 3
第四節研究流程 5
第二章文獻探討與回顧 6
第一節波動性預測模型 6
第二節波動性模型發展與預測能力比較的文獻 6
第三章研究方法 11
第一節模型的介紹 11
一、GARCH 11
二、AR(1)-GARCH 12
三、EGARCH 13
四、GARCH-M 14
五、Skew-t GARCH 15
六、CARR模型 15
第二節預測能力的比較 21
一、樣本外預測能力的比較 21
二、樣本內預測能力的比較 23
第四章樣本資料分析 25
第一節樣本資料與取樣期間 25
第二節樣本資料的基本統計分析 25
第五章實證結果分析 31
第一節ECARR模型與WCARR模型的參數估計 31
一、ECARR模型的參數估計 31
二、WCARR模型的參數估計 34
第二節WCARR模型與Skew-t GARCH模型樣本外預測能力的比較 38
第三節Skew-t分配下的GARCH族模型的實證比較 43
第六章結論 50
參考文獻 51

表目錄
表1 日圓、星幣樣本資料 25
表2-1 日圓匯率週資料之報酬率、絕對報酬率、與變幅之基本統計量 27
表2-2 星幣匯率週資料之報酬率、絕對報酬率、與變幅之基本統計量 29
表3-1 日圓匯率的ECARR模型的參數估計 33
表3-2 星幣匯率的ECARR模型的參數估計 33
表4-1 日圓匯率的WCARR模型的參數估計 35
表4-2 星幣匯率的WCARR模型的參數估計 35
表6-1 日圓匯率的WCARR與Skew-t GARCH之樣本外預測 39
表6-2 星幣匯率的WCARR與Skew-t GARCH之樣本外預測 40
表7-1 日圓匯率週資料的WCARR與Skew-t GARCH之樣本外預測能力比較 42
表7-2 星幣匯率週資料的WCARR與Skew-t GARCH之樣本外預測能力比較 43
表8-1 日圓匯率Skew-t 分配下的GARCH、GARCH-M與AR(1)-GARCH之樣本外預測 44
表8-2 星幣匯率Skew-t 分配下的GARCH、GARCH-M與AR(1)-GARCH之樣本外預測 45
表9-1 日圓匯率週資料的Skew-t 分配下的GARCH、GARCH-M、AR(1)-GARCH樣本外預測能力比較 47
表9-2 星幣匯率週資料的Skew-t 分配下的GARCH、GARCH-M、AR(1)-GARCH樣本外預測能力比較 48

圖目錄
圖1 研究流程 5
圖2-1 日圓匯率變幅與報酬率的走勢圖 28
圖2-2 星幣匯率變幅與報酬率的走勢圖 30
圖3-1 日圓匯率之ECARR(1,1) 與WCARR(1,1)的殘差機率密度圖 36
圖3-2 星幣匯率之ECARR(1,1) 與WCARR(1,1)的殘差機率密度圖 37

參考文獻 一、中文部份:
王甡,「報酬衝擊對條件波動所造成之不對稱效果-台灣股票市場之實證分析」,證券市場發展季刊,第七卷,第一期,民國八十四年一月。
古欣卉,「預測財務波動性:CARR模型的應用」,淡江大學財務金融研究所碩士論文,民國九十五年。
李憲杰,「 一般化自迴歸條件異質性變異數模型參數之選定、估計與檢定」,國立成功大學工業管理研究所碩士論文,民國八十三年。
林楚雄,劉維琪,吳欽杉,「台灣股票市場報酬的期望值與條件波動之關係」,交大管理學報,第十七卷,第三期,頁103-124,民國八十六年。
邱建良,李命志,徐泰瑋,「臺灣股市報酬率波動性行為之探討」,臺灣經濟金融月刊,第三十五卷,第六期,頁43-53,民國八十八年六月。
周雨田,巫春洲,劉炳麟,「動態波動模型預測能力之比較與實證」,財務金融學刊,第十二卷,第一期,頁1-25,民國九十三年四月。
周恆志和陳勝源(2004),「 期貨價格漲跌幅限制與極值理論於保證金設定之應用」,風險管理學報,第六卷,第二期,頁207-228。
郭祥兆,李憲杰,「一般化自迴歸條件異質性變異數模型參數之選定、估計與檢定-以台灣加權股價指數為例」,成功大學學報,第三十卷,人文社會篇,頁53-71,民國八十四年。
張景瑋,「期貨的極端價格行為與保證金設定:CARR模型與極端值理論之應用」,銘傳大學財務金融研究所碩士論文,民國九十四年。
鄭亦妏,「在Black-Scholes評價模型下台指選擇權最是波動性估計方法之研究」,淡江大學管理科學研究所碩士論文,民國九十二年。
鄭婉秀,鄒易凭,蘇欣玫(2006),「商品期貨波動性的預測~CARR模型之應用」,朝陽商管評論,第五卷,第二期,頁 115-132。
薛吉延,「隱含波動性預測品質之解析:台灣及美國市場之實證」,淡江大學財務金融研究所碩士論文,民國八十八年。

二、英文部分:
Akgiray, V. ( 1989 ), “Conditional heteroscedasticity in time series of stock returns:evidence and forecasts”, Journal of Business, Vol. 62, pp. 55-80.
Alizadeh S., M. Brandt, and F. Diebold (2002), “Range-based estimation of stochastic volatility models or exchange rate dynamics are more interesting than you think”, Journal of Finance, Vol. 57, pp. 1047-1091.
Beckers, S. (1983 ), “Variance of security price return based on high low and closing prices”, Journal of Business, Vol. 56, pp. 97-112.
Black, F. (1976), “Studies in stock price volatility changes, Proceedings of the Business and Economics Statistics”, Section. American Statistical Association, pp. 177–181.
Bollerslev, T. (1986), “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics, Vol. 31, pp. 307-327.
Bollerslev, T., J. Wooldridge (1992), “Quasi maximum likelihood estimation and inference in dynamic models with time varying covariances”, Econometric Reviews, Vol. 11, pp. 143-172.
Bollerslev, T., R. Chou, and K. Kroner (1992), “ARCH modeling in finance: a review of the theory and empirical evidence”, Journal of Econometrics , Vol. 52, pp. 5-59.
Bollerslev, T., R. Engle, and D. Nelson (1994), “ARCH models, in Handbook of Econometrics”, IV, 2959-3038, ed.Engle,R.F., and McFadden,D.C., Amsterdam: North-Holland.
Brandt, Michael and Christofer Jones (2002), “Volatility forecasting with range-based EGARCH models”, Manuscript, Wharton School, University of Pennsylvania.
Cassuto, A.E. (1995), “Non-normal error patterns: how to handle them”, Journal of Business Forecasting: Methods and Systems, Vol. 14, pp. 15-16.
Chou, Ray Yeu-Tien(2005), “Forecasting financial volatilities with extreme values: The Conditional Autoregressive Range (CARR) Model”, Journal of Money, Credit and Banking, Vol. 37, pp. 561-582.
Christie, A. A. (1982), “The stochastic behavior of common stock variances”, Journal of Financial Economics, Vol. 10, pp. 407-432.
Davidian, M. and Carroll, R. J. (1987), “Variance function estimation.”, Journal of the American Statistical Association ,Vol. 82, pp. 1079–1091.
Day, T. E. and C. M. Lewis (1992), “Stock market volatility and the information content of stock index options”, Journal of Econometrics, Vol. 52, pp. 267–287.
Duan, J., Ritchken, P., and Sun, Z. (2004), “Jump Starting GARCH: Pricing and Hedging Options with Jumps in Returns and Volatilities”, Forthcoming in Journal of Finance.
Engle, R. F. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation”, Econometrica, Vol. 50, pp. 987-1008.
Engle, R., and J. Russell (1998), “Autoregressive conditional duration: a new model for irregular spaced transaction data”, Econometrica, Vol. 66, pp. 1127-1162.
Fama, E. F. (1965), “The behavior of stock market prices”, Journal of Business, Vol. 38, pp. 34-105.
Fleming, J. (1998), “The quality of market volatility forecasts implied by S&P100 index options prices”, Journal of Empirical Finance, Vol. 5, pp. 317-345.
Garman M. and M. Klass (1980), “On the estimation of security price volatilities from historical data”, Journal of Business, Vol. 56, pp. 97-112.
Glosten, L. R., R. Jagannathan, and D. Runkle (1993), “On the relation between the expected value and the volatility of the nominal excess return on Stocks”, Journal of Finance, Vol. 48, pp. 1779-1801.
Hansen, Burce E. (1994), “Autoregressive conditional density estimation”, International Economic Review, Vol.35, pp. 705-730.
Hull, J. and A. White (1987), “The pricing of options on assets with stochastic volatilities”, Journal of Finance, Vol. 42, pp. 281-300.
Heynen, Ronald C. and M. Kat. Harry (1994) ,”Volatility prediction: A comparison of the stochastic volatility, GARCH(1,1) and EGARCH(1,1) models”, Journal of Derivatives, Vol. 2, pp. 50-65.
Jorion, P. (1995), “Predicting volatility in foreign exchange market”, Journal of Finance, Vol. 50, pp. 507-528.
Latané H. and R. J. Rendleman (1976), “Standard deviation of stock price ratios implied by option premia”, Journal of Finance, Vol. 31, pp. 369-382.
Mandelbrot, B. (1963), “The variation of certain speculative prices”, Journal of Business, Vol. 26, pp. 394-419.
Mincer, J., and V. Zarnowitz (1969), “The Evaluation of Economic Forecasts, in J. Mincer (ed.)”, Economic Forecasts and Expectations, N.Y.: National Bureau of Economic Research.
Morgan, I. G. (1976), “Stock prices and heteroscedasticity”, Journal of Business, Vol. 49, pp. 496-508.
Nelson, D. B.(1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, pp. 347-370.
Parkinson, M. (1980), “The extreme value method for estimating the variance of the rate of return”, Journal of Business, Vol. 53, pp. 61-65.
Sundaresan, S. (2000), “Continuous-Time Methods in Finance: a Review and an Assessment”, Journal of Finance, Vol. 55, pp. 1569-1622.
Taylor, Stephen (1986), “Modelling financial time series”, Chichester, UK: John Wiley and Sons.
Zakoian, J.M. (1994), “Threshold heteroskedastic models”, Journal of Economic Dynamics and Control, Vol. 18, pp. 931-955.
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2012-03-16公開。
  • 同意授權瀏覽/列印電子全文服務,於2012-03-16起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2281 或 來信