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研究生:蘇虹朵
論文名稱:風險值在台灣股市之衡量與驗證
論文名稱(外文):The Measurement and Inspection of Value at Risk on the Stock Market
指導教授:劉 淑 鶯
指導教授(外文):Su-In, Liu
學位類別:碩士
校院名稱:世新大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:49
中文關鍵詞:風險值風險管理條件自我相關風險值指數型商品
外文關鍵詞:VaRRisk ManagementCAViaRIndex Products
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隨著金融商品的不斷創新,雖然提供企業充裕且分散的管道,但是迎面而來的風險也不容忽視,例如國內外金融市場皆發生因衍生性金融市場操作不當而蒙受重大損失之實例。在企業面臨變化莫測的金融市場風險下,風險管理已成為企業的重要管理方針。
風險值模型的概念在1993年被提出後,相關文獻的研究隨著金融市場的日益創新下而不斷地發展。風險值模型以簡單的概念,將暴露於市場下之風險量化,儼然成為風險管裡的新指標。
指數型商品目前已成為國內金融市場主流,未來證交所也將持續推出類股指數相關金融商品。在台灣金融市場不斷擴大下,金融商品所暴露於外之風險將趨高,因此本研究評估各風險值模型應用在國內類股指數與加權指數的狀況。
本研究應用了Engle and Manganelli(2000)所提出的CAViaR模型,利用CAViaR模型對歷史模擬法做動態調整,並與歷史模擬法、均等權重移動平均法及RiskMetrics模型進行比較。
實證結果發現,RiskMetrics並非一定均等權重移動平均法佳,隨著信賴水準增加,RiskMetrics低估實際損失的情況越多。因CAViaR模型考慮前期的風險值與報酬率的訊息,由結果顯示,其評估結果普遍較歷史模擬法佳。
With unceasing creations of financial derivatives, it offers enterprise sufficient and dispersible pipelines, but we can’t neglect the risks what we would face. Take our internal financial institutions for example, they have huge casualties because of unsuitable operation I derivative financial market, same events also happened in abroad countries. In the changeable risks of financial markets what the enterprise suffered, risk management has become the most important administrable guidance.
The concept of Value at Risk model has been lifted in 1993, related researches have uninterrupted developed with more creations of financial market. Value at Risk model quantifies risk exposure into a single number by simple concept, which is a new target of risk management.
Index Products have become main current in internal financial institutions, security companies will keep thrusting out financial products related to sector index in the future. Taiwan’s financial market has expanded continuous, the risk will get much higher. The research would evaluate how all kinds of Value at Risk models have been utilized in internal sector index and TAIEX
The research uses the CAViaR model, which is offered, by Engle and Mananelli in 2000, and it makes dynamic regulation by using CAViaR model to Historical Simulation. In this research paper, I compared with Historical Simulation, Equally Weighted Moving Average and RiskMetrics Model.
Finally, I found that Equally Weighted Moving Average is not always worse than RiskMetrics. With confidence level getting higher, RiskMetrics make more mistakes on underestimating actual casualties. As a result, the evaluation of CAViaR Model is better than one of Historical Simulation, which is according to the information of the former’s consideration about Value at Risk and portfolio return.
第一章、緒論 1.1 研究背景與動機……………………………………………….........1
1.2 研究目的……………………………………………………….........4
1.3 研究架構與流程……………………………………………….........5
第二章、文獻回顧
2.1 風險值定義………...…………………………………………………..6
2.2 風險值之功能與限制………...………………………………....…..8
2.3 文獻探討……………………………...……………………………...10
2.4 風險值模型比較….…………………………...………………………14
第三章、研究方法
3.1 報酬率估計法………………………...……………………………….15
3.2 風險值模型………………………………………...………………….15
3.3 風險值模型檢定方法…………………………………...…………….20
第四章、實證分析
4.1 資料選取與型態…………...……….…………...………………….22
4.2 指數報酬之敘述統計…………...….…………………...………….23
4.3 CAViaR模型之實證步驟……………………….……………...………24
4.4 實證結果………………………………....…………………………..25
第五章、結論與建議………………………………………………...…….28
參考文獻…………………………………………………………………....30
附錄…………………………………………………...……………….....32
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2. Bollerslev T., “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, Vol. 31, 1986, pp. 307-327.
3.Danielsson J. & C. G. de Vries, “Beyond the Sample: Extreme Quantile and Probability Estimation,” London School of Economics, Discussion, 1998, pp. 298.
4. Engle R. F., “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation,” Econometrica, Vol. 50, 1982, pp. 987-1007.
5. Engle R. F. & S. Manganelli, “CAViaR:Conditonal Autoregressive Value at Risk by Regression Quantiles,” Working paper, NBER, 2000.
6. Gourieroux, C. & J. Jasiak, “Truncated Maximum Likelihood, Goodness of Fit Tests and Tail Analysis,” mimeo, 1998.
7.Hamilton J., “A Quasi-Bayesian Approach to Estimating Parameters for Mixtures of Normal Distributions,” Journal of Business & EconomicStatistics, Vol. 9, No. 1, 1991, pp. 27—39.
8.Hendricks D., “Evaluation of Value-at-Risk Models Using Historical Data,” Economic Policy Review, Federal Reserve Bank of New York, Vol. 2, April, 1996, pp.39-69.
9. Jorion P., “Risk: Measuring the Risk in Value at Risk,” Financial Analysts Journal, November/December, 1996, pp. 47-56.
10. J.P. Morgan & Reuters, “RiskMetrics-Technical Document,” 4th edtion, 1996.
11.Koenker R. & G. Bassett, “Regression Quantiles,” Econometrica, Vol. 46, 1978, pp. 33-50.
12.Kupiec P. H.,“Techniques for Verifying the Accuracy of Risk Measurement Models,” The Journal of Derivatives, Vol. 3, 1995, pp. 73-84.
13. Ramazan G. , Frank S. & Abdurrahman,“High Volatility,thick Tails and Extreme Value Theory in Value-at-Risk Estimation,”Insurance Mathematics & Economics, Vol.33, No. 2, 2003, pp. 337-356.
14. Venkataraman, S., “Value at Risk for a Mixture of Normal Distributions: the use of Quasi-Bayesian Estimation Techniques,” Economic Perspectives, Mar, 1997, pp. 2-13.
15. Zangari, P., “An improved methodology for measuring VAR,” RiskMetrics Monitor, Reuters/JP Morgan, 1996.
1.洪明欽、王德仁,2001「台股加權指數風險值評估─分位數迴歸法之探討」東吳經濟商學學報 第三十三期。
2.蒲建亨,2001,「整合VaR法之衡量與驗證~以台灣金融市場投資組合為例」國立政治大學國際貿易研究所碩士碩文。
3.吳欣桐,2001,「風險值(Value at Risk)在台灣股市的應用 ---股票與認購權證投資組合之實證分析」國立中正大學國際經濟研究所碩士碩文。
4.劉志勇,2001,「選擇權風險值之衡量」東吳大學經濟學研究所碩士碩文。
5.楊宗翰,2002,「風險衡量系統之架構及建立」國立政治大學財務管理研究所碩士碩文。
6.陳佩鈴,2002,「匯率條件風險值之估計與比較」中原大學國際貿易研究所碩士碩文。
7.周大慶、沈太白、張大成、敬永康、柯瓊鳳合著,2002,「風險管理新標竿-風險值理論與應用」,智勝文化。
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