系統識別號 | U0002-1606200914245400 |
---|---|
DOI | 10.6846/TKU.2009.00548 |
論文名稱(中文) | 波動度預測-GARCH類模型與類神經模型比較 |
論文名稱(英文) | Comparative Forecasting Volatility Performance of GARCH Family Models and Neural Networks |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 財務金融學系碩士班 |
系所名稱(英文) | Department of Banking and Finance |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 97 |
學期 | 2 |
出版年 | 98 |
研究生(中文) | 宋謹行 |
研究生(英文) | Ching-Hsin Sung |
學號 | 696530467 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | |
口試日期 | 2009-05-17 |
論文頁數 | 80頁 |
口試委員 |
指導教授
-
邱建良
指導教授 - 洪瑞成 委員 - 盧陽正 委員 - 邱哲修 委員 - 李命志 |
關鍵字(中) |
不對稱GARCH模型 類神經 Realized Range-Based Volatility SPA test |
關鍵字(英) |
asymmetric GARCH model neural-networks Realized Range-Based Volatility SPA test |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
鑑於波動度無論在金融衍生性商品訂價、風險值的估算、投資組合的選擇以及動態避險策略的推估上皆有密切的關係,因此本文在常態分配與一般化誤差分配(GED)兩種分配下,驗證GARCH、GJR-GARCH、E-GARCH、I-GARCH與Q-GARCH等五種傳統計量模型與其結合類神經模型之五種複合式模型在波動度預測上之能力,且為解決真實波動度(true volatility)之代理問題(volatility proxy),本文導入以日內60分鐘報酬最高與最低計算之Realized Range-Based Volatility,並分別以MAE、MSE、MME與VaRE做為預測能力衡量指標,另外並以預測之波動度代入Black-Sholes公式反推出選擇權理論價格與真實價格計算其MAE,最後加入SPA (Superior Predictive Ability)檢定,以改善大量模型比較所可能存在最佳模型選取錯誤之疑問,期望找出一個可以精準預測波動度之模型。實證結果為:台灣日資料的股票波動,以類神經結合Q-GARCH模型預測能力最佳,I-GARCH最差;而在選擇權驗證方面,其結果完全相反,以類神經結合I-GARCH模型預測能力最佳。顯示由統計之觀點與財務之觀點來看波動度預測能力會得到不同的結論,而唯一可以確定的就是無論在損失函數方面與選擇權驗證方面,類神經模型之導入皆可增進模型之預測能力。 |
英文摘要 |
We compare the predictive performance of various GARCH family models and Neural Networks. The models are compared out-of-sample using Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX)data. We substitute the Realized Range-Based Volatility for the latent true volatility and choose six statistical loss functions to compare the predictive performance. We also use the forecasting volatilities into Black-Scholes formula to evaluate the theoretical option prices and compare with real option prices. To control for the fact that as the number of models increase, so does the probability of finding superior predictive ability among the collection of models, we implement the Superior Predictive Ability Test of Hansen(2005). We find that, for four loss function, Neural Networks nested Q-GARCH model seems dominate. For two VaR based loss function, GJR-GARCH and GARCH models are preferred. For option pricing, Neural Networks nested I-GARCH model, which performs the worst in the six loss function, seems to be the best performer. |
第三語言摘要 | |
論文目次 |
表目錄 iv 圖目錄 v 第一章 緒 論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究架構 4 第二章 文獻回顧 6 第一節 使用GARCH類模型預測波動之文獻 6 第二節 使用類神經模型預測波動之文獻 13 第三章 研究方法 17 第一節 單根檢定 17 第二節 ARCH效果檢定 21 第三節 條件變異數不對稱檢定 24 第四節 誤差分配之理論基礎 26 第五節 Realized Range-Based Volatility 28 第六節 條件變異數模型 29 第七節 類神經網路(Artificial Neural Networks) 34 第八節 預測績效的評估標準 37 第九節 Black-Scholes 選擇權評價模型 40 第十節 Superior Predictive Ability Test 42 第四章 實證結果分析 44 第一節 研究對象 44 第二節 基本統計量分析 46 第三節 單根檢定 47 第四節 ARCH效果檢定 49 第五節 條件變異數不對稱檢定 50 第六節 各種模型之參數估計結果 51 第七節 預測績效之比較 53 第五章 結論 73 參 考 文 獻 75 一、國外文獻 75 二、國內文獻 79 表目錄 【表4 - 1 - 1】依價內外程度分類之樣本個數表—買權 45 【表4 - 1 - 2】依價內外程度分類之樣本個數表—賣權 45 【表4 - 2 - 1】股價指數日報酬率之基本統計量 46 【表4 - 3 - 1】股價指數原始序列之單根檢定 48 【表4 - 3 - 2】股價指數日報酬率之單根檢定(差分項) 48 【表4 - 4 - 1】ARCH效果檢定分析 49 【表4 - 5 - 1】條件變異數不對稱檢定 50 【表4 - 6 - 1】NORMAL分配下各模型之估計結果 52 【表4 - 6 - 2】GED分配下各模型之估計結果 52 【表4 - 7 - 1】常態分配下預測結果MAE之SPA TEST 53 【表4 - 7 - 2】GED分配下之預測結果MAE之SPA TEST 54 【表4 - 7 - 3】常態分配下之預測結果之MSE之SPA TEST 55 【表4 - 7 - 4】GED分配下之預測結果之MSE之SPA TEST 55 【表4 - 7 - 5】常態分配下之預測結果MME(O)之SPA TEST 56 【表4 - 7 - 6】GED分配下之預測結果MME(O)之SPA TEST 57 【表4 - 7 - 7】常態分配下預測結果MME(U)之SPA TEST 58 【表4 - 7 - 8】GED分配下預測結果MME(U)之SPA TEST 58 【表4 - 7 - 9】常態分配下預測結果VaRED之SPA TEST 60 【表4 - 7 - 10】GED分配下預測結果VaRED之SPA TEST 60 【表4 - 7 - 11】常態分配下預測結果VaREC之SPA TEST 61 【表4 - 7 - 12】GED分配下預測結果VaREC之SPA TEST 62 【表4 - 8 - 1】常態分配下買權價平之SPA TEST 63 【表4 - 8 - 2】GED分配下買權價平之SPA TEST 64 【表4 - 8 - 3】常態分配下買權價內之SPA TEST 65 【表4 - 8 - 4】GED分配下買權價內之SPA TEST 65 【表4 - 8 - 5】常態分配下買權價外之SPA TEST 66 【表4 - 8 - 6】GED分配下買權價外之SPA TEST 67 【表4 - 8 - 7】常態分配下賣權價平之SPA TEST 68 【表4 - 8 - 8】GED分配下賣權價平之SPA TEST 68 【表4 - 8 - 9】常態分配下賣權價內之SPA TEST 69 【表4 - 8 - 10】GED分配下賣權價內之SPA TEST 70 【表4 - 8 - 11】常態分配下賣權價外之SPA TEST 71 【表4 - 8 - 12】GED分配下賣權價外之SPA TEST 71 圖目錄 【圖 1】研究流程圖 5 【圖 2】無隱藏層之類神經模型示意圖 35 【圖 3】含隱藏層之類神經模型示意圖 36 |
參考文獻 |
一、國外文獻 Awartani, B. M. A. and Corradi, V. (2005), Predicting the volatility of the S&P 500 stock index via GARCH models : The role of asymmetries, International Journal of Forecasting, 21, 167-183. Akigary, V. (1989), Conditional heteroscedasticity in time series of stock returns:Evidence and forecasts, Journal of Business , 62, 55-80. 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 Bakshi, G., C. Cao and Chen, Z .(1997), Empirical performance of alternative option pricing models, Journal of Finance, 52, NO.5, 2003-49. Blair, B. J., Ser-Hung, P. and Taylor, S.J. (2001), Forecasting S&P 100 volatility : The incremental information content of implied volatilities and high-frequency index returns, Journal of Econometrics, 105, 5-26. Bollerslev, T. (1986), Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics , 31, 307-327. Dunis, C. L. and Xuehuan, H. (2001), Forecasting and trading volatility :An application of recurrent neural regression and model combination, Journal of Forecasting , 5, 317-354. Donaldson, R. G. and Kamstra, M. (1996), Forecasts combined with neural networks, Journal of Forecasting, 15, 49-61. Engle, R. F. (1982), Autoregressive conditional heteroscedasticity with estimates of variance of UK inflation, Econometrica , 50, 987-1008. Engle, R. F. and V. K. Ng. (1993), Measuring and testing the impact of news on Volatility , Journal of Finance, 48, 1749-1778. Engle, R. F. and Yoo, B.S. (1987), Forecasting and testing in co-integrated systems, Journal of Econometrics, 35, 143-159 Franses, p. h. and Dijk, A.D. (1996), Forecasting stock market volatility using (non-linear) GARCH models, Journal of Forecasting, 15, 229-235. Glosten, L. R., R. Jagannathan 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. Gokcan, S. (2000), Forecasting volatility of emerging stock markets:Linear versus non-linear GARCH models, Journal of Forecasting, 19, 499-504. González-Rivera, G., Tae-Hey, L. and Mishra, S. (2004), Forecasting volatility :A reality check based on option pricing, utility function, value-at-risk and predictive likelihood, International journal of forecasting, 20, 629-645 Hansen, B. E. (1994), Autoregressive conditional density estimation, International Economic Review, 35, 705-730. Harvey, C. R. and Siddique. A. (1999), Autoregressive conditional skewness , Journal of Financial and Quantitative Analysis, 34(4), 465-487. Hansen, P. R. and Lunde, A. A. (2005), A forecast comparison of volatility models:Does anything beat GARCH(1,1)?, Journal of Applied Econometrics , 20, 873-889. Hansen, P. R. (2005), A test for superior predictive ability, Journal of Business & Economic Statistics, 4, 365-380. Hamid, S. A. and Iqbal, Z. (2004),Using neural network for forecasting volatility of S&P 500 index futures price, Journal of Business Research,57,1116-1125. Jingtao, Y., Yili, L. and Tan, C. L. (2000), Option price forecasting using neural networks , International Journal of Management Science, 28,455-466. Koopman, S. J., Jungbacker, B. and Hol, E. (2000), Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements , Journal of Empirical Finance, 12, 445-475. Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin (1992), Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we the economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178. Michael, Y. H. and Christos, T. (1999), Combining conditional volatility forecast using neural networks: An application to the EMS exchange rate , Journal of International Financial Markets Institutions & Money, 9, 407-422. Martens, M. and Dijk, D.V. (2007), Measuring volatility with the realized range. Journal of Econometrics, 138, 181-207. Nelson, D. B. (1991), Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. Phillips, P. C. B. and Perron, P. (1988), Testing for unit in time series regression, Biometrika, 75, 335-346. Schwert, G. W. (1989), Tests for a unit roots in time series regression, Biometrika, 75, 335-346. Terasvirta, T., Dijk, D.V. and Medeiros, M.C.(2005), Linear models, smooth autoregressions , and neural networks for forecasting macroeconomic time series: A re-exammination, International Journal of Forecasting, 21, 755-774. Theodossiou, P. (1998), Financial data and skewed generalized t distribution, Management Science, 44, 1650-1661. Wang, K. L., C. Fawson , Barrett C.B. and McDonald, J.B.(2001), A flexible parametric GARCH model with an application to exchange rates, Journal of Applied Econometrics, 16(4), 521-536 二、國內文獻 王凱立(2001),一個新的參數化GARCH模型在亞洲股市上的應用,財務金融學刊,第九捲第三期,頁21-52。 李沃牆、張克群(2006),比較不同波動率模型下台灣股票選擇權之評估績效, 真理財經學報,第十四期,頁71-96。 李命志、洪瑞成、劉洪鈞(2007),厚尾GARCH模型之波動性預測能力比較,輔仁管理評論,第十四卷第二期,頁47-72。 林美蓮 (2000),高頻率股市報酬波動性之ANN-GARCH MODEL ,國立交通大學經營管理研究所之論文。 陳昶均 (2004),不同波動性估計模型下台指選擇權評價績效之比較,東吳大學商學院企業管理學系碩士班碩士論文。 蔡麗茹、葉銀華(2000),不對稱GARCH族模型預測能力之比較研究,輔仁管理評論,第七卷第一期,183-196。 |
論文全文使用權限 |
如有問題,歡迎洽詢!
圖書館數位資訊組 (02)2621-5656 轉 2487 或 來信