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 U0002-0106200613081900
中文論文名稱 金融資產波動性預測–條件限制式模型實證研究
英文論文名稱 Financial Assets Volatility Forecasting ─ The Restricted Least Squares Model Estimation
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 94
學期 2
出版年 95
研究生中文姓名 林東虨
研究生英文姓名 TUNG-PING LIN
學號 792490038
學位類別 碩士
語文別 中文
口試日期 2006-05-01
論文頁數 49頁
口試委員 指導教授-李命志
委員-林卓民
委員-邱建良
委員-邱哲修
中文關鍵字 波動性預測  歷史標準差模型  指數加權移動平均模型  一般化自我回歸條件異質變異數模型  限制最小平方估計模型  平方根預測誤差 
英文關鍵字 volatility forecasting  STD(standard deviation)  EWMA(exponentially weighted moving average)  GARCH  RLS(restricted least squares)  RMSFE(root mean squared forecast error) 
學科別分類
中文摘要 金融市場波動性的預測,於理論上,一般皆認為其報酬的行為是隨機的,且通常假設為一常態分配及變異數為固定的情境下,然就實證結果而言,其報酬率常呈一高狹峰且變異數隨時間變動而改變。然近年來對波動性的研究所示,其不但是隨時間而改變,且金融資產價格波動具有可預測性的,因此許多預測波動的研究方法被提出。
本文以採用條件限制最小平方模型,來檢驗於金融市場商品價格波動性的預測能力,是否優於異質變異家族的模型。其研究樣本資料採用橫跨三個不同市場中,十二個市場標的的每日收市價為研究樣本,比較歷史標準差模型、指數加權移動平均模型、一般化自我回歸條件異質變異數模型及限制最小平方估計模型,共四種波動估計模型,以實證比較何者的預測績效最佳。此外,採用平方根預測誤差為評估預測績效的標準。透過上述實證分析,期待獲得一廣泛性預測較佳的模型,進一步提供投資決策者掌握金融資產報酬波動性的可行管道。
實證研究發現於比較採用一般化自我回歸條件異質變異數模型及歷史標準差模型所得之樣本預測值各據勝場,另指數加權移動平均模型所得預測值卻普遍優於一般化自我回歸條件異質變異數模型,而採用限制最小平方估計模型預測樣本市場標的結果,於所有樣本市場標的中所得預測值,均優於其他的預測模型。
英文摘要 Volatility forecasting in financial assets is important to traders, investors and risk managers. In theory,the return volatility of financial assets are random,normal distribution and variance is fixed. In fact,the returns is leptokurtosis,and variance changes from time to time. The forecasting volatility ability of time-series volatility forecasting models recent period in econometrics literature changes from time to time,and the price volatility of financial assets may forecast .
Using Ederington and Guan(2005)apply the RLS(Restricted Least Squares) volatility forecasting model,to estimates the ability of price volatility in financial assets,and compare the ability of GARCH model . We compare the ability of these four forecasting models for three financial markets in 12 financial assets price,to find the best ability in which model. We also choice Ederington and Guan(2005) apply the RMSFE(Root Mean Squared Forecast Error)to measure the forecasting ability in difference between actual and forecast annualized standard deviation of returns.
After compared the estimation results,we can not find the difference on the forecasting ability between GARCH(1,1) model and STD model,and the EWMA model is better than GARCH(1,1) model,the RLS model is better than other models in whole 12 financial assets.
論文目次 目 錄
第一章 緒論
第一節 研究背景與動機…………………………………………………1
第二節 研究目的 ………………………………………………………3
第三節 研究限制…………………………………………………………5
第四節 論文架構…………………………………………………………6
第五節 研究流程…………………………………………………………7
第二章 文獻回顧
第一節 波動性的特性……………………………………………………8
第二節 波動性估計模型的發展…………………………………………9
第三節 國內的研究實證 ………………………………………………14
第四節 國外的研究實證 ………………………………………………17
第三章 研究方法
第一節 資料來源與處理 ………………………………………………22
第二節 常態檢定 ………………………………………………………23
第三節 序列相關檢定 …………………………………………………24
第四節 實證模型介紹 …………………………………………………25
第五節 預測績效的評估標準 …………………………………………30
第四章 實證結果
第一節 實證步驟 ………………………………………………………31
第二節 資料分析 ………………………………………………………31
第三節 模型的參數估計 ………………………………………………37
第四節 樣本配適之比較 ………………………………………………40
第五章 結論 ………………………………………………………… 44
參考文獻 ………………………………………………………………45
表 目 錄
表4-1 每日收盤價之基本統計檢定量…………………………………35
表4-2 日報酬之基本統計檢定量………………………………………36
表4-3 GARCH(1,1)模型之參數估計值………………………………37
表4-4 RLS模型之參數估計值 …………………………………………39
表4-5 RMSFE 績效評估結果 …………………………………………42
圖 目 錄
圖1-1 研究流程圖………………………………………………………7
圖4-1 每日收盤價之走勢圖 …………………………………………32
圖4-2 日報酬之走勢圖 ………………………………………………33
圖4-3 道瓊工業指數之GARCH和RLS模型隱含係數圖 ………………39
參考文獻 一、中文
1.吳佳貞(1997),「波動度預測模型之探討」,國立政治大學金融研究所碩士論文.
2.呂文正(1998),「股票報酬率的波動性研究─ARCH-family、SWARCH模型之應用」,國立台灣大學經濟研究所碩士論文.
3.李命志與趙其琳(2001),「波動性預測能力比較─臺灣認購權證之實證研究」,臺灣銀行季刊,第五十二卷第二期,頁101-127.
4.李進生、鍾惠民與陳煒朋(2001),「不同波動性模型預測能力之比較:臺灣與香港認購權證市場實證」,證券金融,第四十四期,頁57-89.
5.林楚雄、劉維琪與吳欽杉(1999),「不對稱GARCH模型的研究」,管理學報,第十六卷第三期,頁479-515.
6.陳斐紋(1995),「台灣股票市場報酬率與波動性預測之研究-ARCH family 模型之運用」,國立台灣大學財務金融研究所碩士論文.
7.蔡麗茹與葉銀華(1998),「不對稱GARCH族模型預測能力良比較研究」,輔仁管理評論,第七卷第一期,頁183-196.
8.鄧淑芬(2001),「波動性估計模型預測能力之比較–美國及亞洲各國股價指數之實證研究」,國立高雄第一科技大學財務管理系碩士論文.
二、英文
1.Akgiray, V.,(1989). Conditional heteroscedasticity in time series of stock returns:Evidence and forecasts. Journal of Business, 62, 55-80.
2.Andersen, T.G., and Bollerslev, T.,(1998). Answering the skeptics:Yes standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
3.Andersen, T.G., Bollerslev, T., Diebold, F. X., and Labys, P.,(2003). Modeling and forecasting realized volatility.:Econometrica, 71(2), 579-625.
4.Andersen, T.G., Bollerslev, T., Diebold, F.X., and Labys, P.,(1999). Forecasting financial market volatililty:Sample frequency vis-a-vis forecast horizon. Journal of Empirical Finance, 6, 457-477.
5.Baillie, R.T., Bollerslev, T., and Mikkelsen, H.O.,(1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74, 3-30.
6.Black, F. and Scholes, M.S.,(1973).The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 3, 637-654.
7.Bollerslev, T.,(1986),Generalized Autoregssive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.
8.Bollerslev, T. and Engle.,(1986),Modeling the Persistence of Condition variance. 5, 1-50.
9.Bollerslev, T. and Mikkelsen, H.O.,(1996). Modeling and pricing long memory in stock market volatility. Journal of Econometrics, 73, 151-184.
10.Box and Jenkin.,(1976).Time Series Analysis : Forecasting and Control. Holden-Day Inc.
11.Brailsford, T., and Faff, R.,(1996). An evaluation of volatility forecasting techniques. Journal of Banking and Finance, 20, 419-438.
12.Brooks, C.,(1986). Predicting stock index volatility:Can market volume help. Journal of Forecasting, 17, 59-80.
13.Cao, CQ. and Tsay RS.,(1992).Nonlinear time series analysis of stock volatilities. Journal of Applied Econometrics, 7, 165-185.
14.Chong, C.W., Ahmad,M.I., and Abdulah, M.Y.,(1999).Performance of GARCH models in forecasting stock market volatility. Journal of Forecasting, 18, 333-343
15.Chou, R.Y.,(1988).Volatility persistence and stock valuations : Some empirical evidence using GARCH. Journal of Applied Econometrics, 3, 279-294.
16.Christie, A.A.,(1982).The stochastic behavior of common stock variances : Value, leverage, and interest rate effect. Journal of Financial Economics, 10, 407-432.
17.Diebold, F.X., and Mariano, R.S.,(1995).Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253-263.
18.Ding, Z., and Granger, C.,(1996). Modeling volatility persistence of speculative returns:A new approach. Journal of Econometrics, 73, 185-215.
19.Ding, Z., Granger, C. and Engle R.F.,(1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 11, 83-106.
20.Ederington, L.H. and Guan, W.,(1999). Forecasting Volatility. Working paper, http://www.ssm.com.
21.Ederington, L.H. and Guan, W.,(2005). Forecasting Volatility. The Journal of Future Markets , 20, 465-490.
22.Engle, R.F.,(1982).Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation. Econometrica, 50, 987-1008.
23.Engle, R.F.,(1990).Discussion:Stock Market Volatility and The Crash of `87. Review of Financial Studies, 3, 103-106.
24.Engle, R.F., Lilien, D.M., and Robins, R.P.,(1987).Estimating Time Varying Risk Premia in the Term Structure:the ARCH-M Model. Econometrica, 55, 391-407.
25.Fama, E.F.,(1965).The Behaviour Stock Market Prices. Journal of Business, 38, 34-105.
26.Figlewski, S.,(1997).Forcasting volatility. Financial Markets, Institutions, and Instruments 6, 1-88.
27.Franses, P.H. and Dijk, D. Van.,(1996). Forecasting stock market volatility using (Non-linear)GARCH models. Journal of Forecasting, 15,229-235.
28.Glosten, L.R., Jagannathan, R., and Runkle, D.E.,(1993).On the Relation between the Expected Value and Volatility on the Nominal Excess Returns of Stocks. Journal of Finance,48, 1779-1801.
29.Granger, C.W.J., and Sin Chor-Yiu.,(2000).Modeling the Absolute Returns of Different Stock Indices:Exploring the Forecastability of an Alternative Measure of Risk. Journal of Forecasting, 19,227-298.
30.Granger, C.W.J.,and Ding, Z.,(1995).Some properties of absolute return, an alternative measure of risk. Annales d’Economie et de Statistique, 40, 91-97.
31.Hansen, P.R., and Lunde, A.,(2001). A forecast comparison of volatility models:Does anything beat a GARCH(1,1)? Working paper 01-04, Brown University Department of Economics.
32.Harvey, A., Ruiz, E., and Sentana, E.,(1992).Unobserved Component Time Series Models with ARCH Disturbances. Journal of Econometrics, 52, 129-157.
33.Heynen, R.C., and Kar, G.M.,(1994).Volatility Prediction: A Comparison of the Stochastic volatility, GARCH(1,1)and EGARCH(1,1)Models. Journal of Derivatives,50-65.
34.Huber, Peter.,(1996).Robust Statistical Procedures, ed., Society for Industrial and Applied Mathematics:Philadelhia.
35.Kariya, T., Tsukuda, Y. and Maru, J.,(1990).Testing the random walk hypothesis for Japanese stock prices in S. Taylor’s model. Hitotsubashi University Discussion Paper.
36.Jorion, P.,(1995).Predicting volatility in the foreign exchange market. Journal of Finance, 50 , 507-528.
37.Lopze, J.,(2001). Evaluating the predictive accuracy of volatility models. Journal of Forecasting, 20, 87-109.
38.Loudon, G.F., Watt, W.H., and Yadav, P.K.,(2000). An empirical analysis of alternative parametric ARCH models. Journal of Applied Econometrics, 15, 117-136.
39.Mandelbrot, B.,(1963).The Variation of Certain Speculative Prices. Journal of Business, 36, 394-419.
40.Mcmillan, D., Speight, A. and Apgwilym, O.,(2000).Forecasting UK stock market volatility. Applied Financial Economics, 10, 435-448.
41.Nelson, D.B.,(1991). Conditional heteroskedasticity in asset returns:A new approach. Econometrica, 59, 347-370.
42.Pagan, A.R., and Schwert, G.W.,(1990). Alternative models of conditional stock volatilities. Journal of Econometrics, 45, 267-290.
43.Poon, S. H., and Granger, C.,(2003). Forecasting volatility in financial markets:A Review. Journal of Economic Literature, 41, 478-539.
44.Riskmetrics.,(1996). Riskmetrics Technical Document. Available at Riskmetrics.com.
45.Schmidt, P.,(1974). A modification of the Almon distributed lag. Journal of the American Statistical Association, 69, 679-681.
46.Schwert, G.W.,(1990).Stock volatility and the crash of `87.Review of Financial Studies, 3, 77-102.
47.Staudte, Robert., and Simon, Sheather.,(1990).Robust Estimation and Testing, John Wiley and Sons:New York.
48.Taylor, S.,(1986).Modeling Financial Time Series, John Wiley:New York.
49.Tasy, R.S.,(2005).Analysis of Financial Time Series. ed. Wiley Intersciencw.
50.Tse, Y.K.,(1991). Stock returns volatility in the Tokyo Stock Exchange. Japan and the World Economy, 3, 285-298.
51.Walsh, David. M., and Tsou, Glenn, Yu-Gen.,(1998). Forecasting index volatility:Sampling integral and non-trading effects. Applied Financial Economics, 8, 477-485.
52.West, K., and Cho, D.,(1995). The predictive ability of several models of exchange rate volatility. Journal of Econometrics, 69, 367-391.
53.Zumbach, G.,(2003). Volatility processes and volatility forecast with long memory. Working paper, Consulting in Finanic
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2006-06-07公開。
  • 同意授權瀏覽/列印電子全文服務,於2006-06-07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