CARR模型之實證研究---以台股指數為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：15

、訪客IP：3.145.40.222

姓名

劉炳麟(Bin-Lin Liu) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

CARR模型之實證研究---以台股指數為例
(An empirical study of the CARR model: an axample of the Taiwan Stock Index)

相關論文

★ 避險基金之績效評估	★ 展望理論與共同基金績效
★ 生命週期基金 :行為財務學觀點	★ 行為特性與投資績效相關性之研究
★ 專業投資人行為特性探討	★ 中小型企業融資缺口與資訊不對稱之探討
★ 九型人格特質與理財偏誤行為之相關性研究	★ 銀行通路轉型策略個案研究
★ 銀行財富管理行銷策略分析-以兩家在台外商銀行為例	★ 羅盤玫瑰可預測型態之探討
★ 巢式與非巢式資產定價理論之比較與檢定	★ 投資者情緒與市場報酬
★ 資產定價模型樣本外績效之檢定	★ 規模效果和元月效應之微觀
★ 因子或特徵:全球觀點	★ 特徵與因子：日本證據

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。ARCH/GARCH族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(2002)將GARCH模型結合變幅在波動性預測上的優勢進一步提出CARR(Conditional Auto-Regressive Range)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，本文想驗證是否在台股指數上也能得到相同的結論。
本文中將簡單的介紹CARR模型及其性質，並以台股指數為研究對象，分別進行CARR模型和GARCH模型在樣本內及樣本外波動性的預測能力比較。本文的實證結果可推論CARR模型在刻畫波動性方面優於GARCH模型，此與Chou(2002)的論述一致。另外，本文隨機選取了10檔個股資料，並比較其樣本內波動性預測能力用以強化論證的完整性。除此之外，本研究並推廣CARR模型的應用層面，考慮財務槓桿效應及漲跌幅限制的影響，並探討其背後所隱含的經濟意義。

摘要(英)

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. Since 1982, ARCH/GARCH family models have been used in the forecast of volatilities, and have performed well in many empirical studies. Recently, Chou(2002) proposed the CARR (Conditional Auto-Regressive Range) model as an alternative volatility model. The main concept of the CARR model is to use a simple dynamic structure for range to characterize the volatility process. In Chou(2002), 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. The main motivation in this paper is to explore the forecasting power of the CARR model based on the trading data of the Taiwan Stock Exchange Capitalization Weighted Stock Index. Our emperical results show that in both the in-sample forecast and the out-of-sample forecast the CARR model is preferable to the GARCH model in the volatility forecasting, supporting the claims of Chou(2002). In order to strenghten the completeness of our demonstration, we arbitrarily choose 10 stocks in Taiwan to compare the two models again in the in-sample volatilities forecasting. Moreover, we also consider the economic implications of financial leverage effect and price limit by utilizing the CARR model.

關鍵字(中)

★ CARR
★ GARCH
★ 變幅
★ 波動性
★ 財務槓桿效應
★ 漲跌幅限制

關鍵字(英)

★ CARR
★ GARCH
★ Range
★ Volatility
★ Leverage Effect
★ Price Limit

論文目次

目錄
頁數
第一章前言 1
第二章文獻回顧 2
第三章研究方法 4
第一節 CARR模型的介紹 4
第二節預測能力的比較 7
第三節考量漲跌幅限制的模型 10
第四章資料分析 11
第一節台灣股票指數週資料的樣本敘述統計量 12
第二節台灣股票指數日資料樣本敘述統計量 14
第三節個股日資料及週資料樣本的敘述統計量 16
第五章實證分析 19
第一節 CARR模型及GARCH模型樣本內預測能力的比較 19
一、以台灣發行量加權股價指數週資料為研究標的 19
二、以台灣發行量加權股價指數日資料為研究標的 25
三、以台灣集中市場個股週資料及日資料為研究標的 27
第二節 CARR模型及GARCH模型樣本外預測能力的比較 30
第三節財務槓桿效應 35
第四節漲跌幅限制的影響 36
第六章結論 39
參考文獻 41
表次
頁次
表1：台灣股價指數週資料之統計特性分析 13
表2：台灣股價指數日資料之統計特性分析 15
表3：個股股價日變幅之統計特性分析 17
表4：個股股價週變幅之統計特性分析 18
表5：CARR模型比較（以CARR(1,1)、CARR(1,2)及CARR(2,1)為例） 20
表6：GARCH模型的比較（以GARCH(1,1)、GARCH(1,1)及GARCH(2,1)為例） 21
表7：CARR(1,1)及GARCH(1,1)在大盤指數週資料的預測能力比較 22
表8：CARR(1,1)及GARCH(1,1)在大盤指數日資料的預測能力比較 26
表9：CARR(1,1)及GARCH(1,1)在個股週資料的預測能力比較 27
表10：CARR(1,1)及GARCH(1,1)在個股日資料的預測能力比較 29
表11：CARR模型及GARCH模型在大盤指數週資料的樣本外預測(SSDR) 31
表12：CARR模型及GARCH模型在大盤指數週資料的樣本外預測(WRSQ) 32
表13：CARR模型及GARCH模型在大盤指數週資料的樣本外預測(WRNG) 33
表14：CARR模型及GARCH模型在大盤指數週資料的樣本外預測(AWRET) 34
表15：CARR(1,1)模型與CARRX(1,1)模型大盤指數日資料參數估計上比較 36
表16：漲跌幅更動表（1985年1月1日起至2001年4月30日） 37
表17：考慮漲跌幅限制的CARR模型表現情形 38
表18：隨著漲跌幅限制改變CARRX(1,1)模型係數的表現情形 39
圖次
頁次
圖1：台灣股價指數週變幅及週報酬率走勢圖 14
圖2：台灣股價指數日變幅及日報酬率走勢圖 15
圖3：SSDR、FV(CARR)及FV(GARCH)樣本內預估圖形 24
圖4：WRSQ、FV(CARR)及FV(GARCH)樣本內預估圖形 24
圖5：WRNG、FV(CARR)及FV(GARCH)樣本內預估圖形 25
圖6：AWRET、FV(CARR)及FV(GARCH)樣本內預估圖形 25
圖7：CARR模型及GARCH模型在大盤指數週資料的樣本外預測比較圖(SSDR) 32
圖8：CARR模型及GARCH模型在大盤指數週資料的樣本外預測比較圖(WRSQ) 33
圖9：CARR模型及GARCH模型在大盤指數週資料的樣本外預測比較圖(WRNG) 34
圖10：CARR模型及GARCH模型在大盤指數週資料的樣本外預測比較圖(AWRET) 35

參考文獻

參考文獻
中文部分：
杜金龍 (1999) 基本分析在台灣股市運用的訣竅金錢文化出版社
英文部分：
Andersen, T., and T. Bollerslev (1997), “Heterogeneous information arivals and return volatility dynamics: Uncovering the long run in high frequency returns,” Journal of Finance ,52, 975-1005.
Alizadeh S., M. Brandt, and F. Diebold (1999) “Range-based estimation of stochastic volatility models or exchange rate dynamics are more interesting than you think,” Working paper, University of Pennsylvania, Wharton School.
Ballie, R. T., and R. P. DeGennaro (1990), “Stock Returns and Volatility,” Journal of Finance and Quantitative analysis, 25, 203-214.
Black, F., and M. Scholes(1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economics, 81, 637-659.
Black, F. (1976), “Studies of Stock Price Volatility Changes,” Proceeding of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 177-181.
Bollerslev, T., R. Chou, and K. Kroner (1992), “ARCH modeling in finance: a review of the theory and empirical evidence,” Journal of Econometrics ,52, 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
Bollersleve, T.(1986), “Generalized Autoregressive Conditional Heteroskedasticity, ”Journal of Econometrics, 31, 307-327.
Bollerslev, T., R. Engel, and J. Wooldridge (1988), “A Capital Asset Pricing Model with Timing-vary Covariance,” Journal of Political Economy, 96, 116-131.
Bollerslev, T., and J. Wooldridge (1992), “Quasi maximum likelihood estimation and inference in dynamic models with time varying covariances,” Econometric Reviews, 11, 143-172.
Cassuto, A.E. (1995), “Non-Normal Error Patterns: How to Handle Them,” The Journal of Business Forecasting: Methods and Systems, 14, 15-16.
Chou, R. (2002), “Forecasting financial volatilities with extreme values: the Conditional AutoRegressive Range (CARR) Model,” Working paper, The Institute of Economics Academia Sinica, Taiwan.
Christie, A.A., (1982), “The Stochastic Behavior of Common Stock Variances: Value,Leverage, and Interest Rate Effects,” Journal of Financial Economics , 10, 407-432.
Engle, R. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation,” Econometrica, 50, 987-1008.
Engle, R., and J. Russell (1998) “Autoregressive conditional duration: a new model for irregular spaced transaction data,” Econometrica, 66, 1127-1162.
Fama, E. F. (1965), “The Behavior of Stock Market Prices,” Journal of Business, 38, 34-105.
French, K., W. Schwert, and R. Stambaugh (1987), “Expected stock returns and volatility,” Journal of Financial Economics, 19, 3-29.
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, 48, 1779-1801.
Galant, A., C. Hsu, and G. Tauchen (1999), “Calculating volatility diffusions and extracting integrated volatility,” Review of Economics and Statistics, 81, 617-631.
Hull, J. and A. White (1987), “The pricing of options on assets with stochastic volatilities,” Journal of Finance, 42, 281-300.
Ljung, G.M., and G.E.P. Box (1978), “On a Measure of Lack of Fit in Time Series Models,” Biometrika, 65, 297-303.
Mandelbrot, B. (1963), “The Variation of Certain Speculative Prices,” Journal of Business, 36, 294-419.
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 Price and Heteroskedasticity,” Journal of Business, 49, 496-508.
Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns : A New Approach,” Econometrica, 59, 2347-370.
Parkinson, M. (1980), “The extreme value method for estimating the variance of the rate of return,” Journal of Business, 53, 61-65.
Poterba, J., and L. Summers (1986), “The persistence of volatility and stock market fluctuations,” American Economic Review, 76, 1142-1151.
Zakoian, J.M. (1994), ”Threshold Heteroskedastic Models,” Journal of Economic Dynamics and Control, 18, 931-955.

指導教授

周雨田、周賓凰
(Ray Y. Chou、Pin-Huang Chou)

審核日期

2003-1-8

推文