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題名：應用狀態空間模型與基因類神經網路濾波技術於風險值預測之研究：以臺股指數與臺指期貨為例
書刊名：臺大管理論叢
作者：古永嘉 引用關係／許世璋
作者(外文)：Goo, Yeong-jia／Hsu, Shih-chang
出版日期：2010
卷期：20:2
頁次：頁307-342
主題關鍵詞：類神經網路；狀態空間模型；風險值；Artificial neural network；State space model；Value at risk
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
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  • 共同引用共同引用:8
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以往針對風險值的估計，大致以GARCH 模型估計波動度後，再以蒙地卡羅模擬法估計風險值績效。本研究延伸過去的方式，試圖以Bi-GARCH 模型估計波動度後，分別以狀態空間及基因類神經網路兩種模型，應用於波動度濾化(Filtering) 的處理，之後再與傳統GARCH 模型，藉由回溯測試以及Kupiec 概似比檢定對風險值估計模型的績效進行比較。本研究以台灣發行量加權股價指數與股價指數期貨結算價格為研究標的，取樣期間為2002 年1 月1 日至2004 年12 月31 日之日資料，共計745 筆。研究發現，無論在台股指數現貨或期貨，經由狀態空間與基因類神經網路濾化後之風險值的模式績效，不論短長期或多空階段之評比，皆遠優於傳統估算法。
Previous researchers usually use GARCH models in estimating volatility in evaluating value at risk (VaR) performance. In this study, Bi-GARCH models were adopted in estimating volatility. The estimated volatility is then filtered by using both State Space Models (SSM) and Generic Algorithm-Artificial Neural Network (GANN) models. The VaR performances of these models are compared using back-testings and Kupiec likelihood tests. A total of 745 daily data of Taiwan stock indexes of spot and futures ranging from Jan. 2, 2002 to Dec. 31, 2004 were collected. The results show that the filtered GANN and SSM models are better then traditional estimation methods for the evaluation of VaR for both stock spot and futures indexes.
期刊論文
1.Koutomos, G.、Tucker, M.(1996)。Temporal Relationship and Dynamic Interactions between Spot and Futures Stock Market。Journal of Futures Markets，16(1)，55-69。  new window
2.Jackson, P.、Maude, D. J.、Perraudin, W.(1997)。Bank Capital and Value at Risk。The Journal of Derivatives，4(3)，73-89。  new window
3.Kalman, R. E.(1960)。A New Approach to Linear Filtering and Prediction Problems。Journal of Basic Engineering, Transactions of the ASME Series D，82(1)，35-45。  new window
4.Green, T. C.、Figlewski, S.(1999)。Market Risk and Model Risk for a Financial Institution Writing Options。Journal of Finance，54(4)，1465-1499。  new window
5.Beder, Tanya Styblo(1995)。VAR: Seductive but Dangerous。Financial Analysts Journal，51(5)，12-24。  new window
6.Figlewski, S.(1997)。Forecasting Volatility。Financial Markets, Institutions, and Instruments，6(1)，1-88。  new window
7.Chu, S. H.、Freund, S.(1996)。Volatility Estimation for Stock Index Option: GARCH Approach。The Quarterly Review of Economics and Finance，36(4)，431-450。  new window
8.沈大白、柯瓊鳳、鄒武哲(19980900)。風險值衡量模式之探討--以臺灣上市公司權益證券為例。東吳經濟商學學報，22，57-76。new window  延伸查詢new window
9.Berndt, Ernst R.、Hall, Bronwyn H.、Hall, Robert E.、Hausman, Jerry A.(1974)。Estimation and Inference in Nonlinear Structural Models。Annals of Economic and Social Measurement，3(4)，653-665。  new window
10.Lam, Monica(2004)。Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis。Decision Support Systems，37(4)，567-581。  new window
11.Alexander, C. O.、Leigh, C. T.(1997)。On the Covariance Matrices Used in Value at Risk Models。Journal of Derivatives，4(3)，50-62。  new window
12.Hendricks, Darryll(1996)。Evaluation of Value-at-Risk Models Using Historical Data。Federal Reserve Bank of New York Economic Policy Review，2(1)，39-69。  new window
13.Akgiray, Vedat(1989)。Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts。The Journal of Business，62(1)，55-80。  new window
14.Bollerslev, Tim(1986)。Generalized Autoregressive Conditional Heteroskedasticity。Journal of Econometrics，31(3)，307-327。  new window
15.Andersen, Torben Gustav、Bollerslev, Tim(1998)。Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts。International Economic Review，39(4)，885-905。  new window
16.Kupiec, Paul H.(1995)。Techniques for Verifying the Accuracy of Risk Measurement Models。Journal of Derivatives，3(2)，73-84。  new window
17.古永嘉、萬文隆(2002)。「兩岸三地連動之研究：狀態空問模型之應用」。證券櫃檯月刊，70 期，頁48 一65。  延伸查詢new window
18.Bachman, D., Choi, J. J., Jeon, B. N.,、Kopecky, K. J.(1996)。Common factor ininternational stock prices: Evidence from a cointegration study。International Review of Financial Analysis，5 (1)，39-53。  new window
19.Ball, C. A.、Torous, W. N.(2000)。Stochastic correlation across international stock markets。Journal of Empirical Finance，7(3/4)，373-388。  new window
20.Brooks, C.、Persand, G.(2003)。Volatility forecasting for risk management。Journal of Forecasting，22 (1)，1-22。  new window
21.Chang, K. H.,、Wu, C. S.(1998)。A gray time series model on forecasting the Chinese NewYear effect in the Taiwan stock market。Journal of Gray System，1 (1)，55-63。  new window
22.Gloria, G. R., Lee, T. H.,、Mishra, S.(2004)。Forecasting volatility: A reality check based onoption pricing, utility function, value-at-risk, and predictive likelihood。International Journal of Forecasting,，20 (4)，629-645。  new window
23.Granger, C.,、Poon, S.(2003)。Forecasting volatility: A survey。Journal of Economic Literature，41 (2)，478-539。  new window
24.Lee, W. C.(2006)。Forecasting high-frequency financial data volatility via nonparametric algorithms-evidence from Taiwan financial markets。New Mathematics and Natural Computation，2 (3)，345-359。  new window
25.Tam, K. Y.,、Kiang, M. Y.(1992)。Managerial application of neural networks: The case ofbank failure predictions。Management Science，38 (7)，926-947。  new window
26.Venkatarman, S.(1997)。Value at risk for mixture of normal distribution: The use of quasi-bayesian estimation techniques。Economic Perspectives，23 (1)，2-13。  new window
學位論文
1.陳宜玫(2000)。風險值估測模型之研究：以台灣股票市場為例(碩士論文)。義守大學。  延伸查詢new window
2.王倩茵(2003)。金控公司市場風險值之研究(碩士論文)。國立臺灣大學。  延伸查詢new window
3.林保霖(2002)。增進蒙地卡羅模擬法評估風險值之績效研究。  延伸查詢new window
4.翁勝彬(1996)。認購權證發行人市場風險衡量與評估。  延伸查詢new window
5.張簡彰程(2001)。增進模擬法估計風險值之績效研究：以台股票市場為例。  延伸查詢new window
圖書
1.Morgan, J. P.(1996)。Riskmetrics Technical Document。New York, NY：Morgan Guaranty Trust Company。  new window
2.Akaike, H.(1976)。Canonical correlations analysis of time series and the use of aninformation criterion。New York, NY:。  new window
圖書論文
1.Rumelhart, D. E.、Hinton, G. E.、Williams, R. J.(1986)。Learning Internal Representations by Error Propagation。Parallel distributed processing: Explorations in microstructure of cognition, Vol. 1: Foundations。Cambridge, MA：MIT Press。  new window
 
 
 
 
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