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研究生:張瑋珊
研究生(外文):Chang, Wei-Shan
論文名稱:運用極值理論模型探討流動性對風險值之影響
論文名稱(外文):The Application of Extreme Value Theory Model to Examine the Impact of Liquidity on Value-at-Risk Estimation
指導教授:洪瑞成洪瑞成引用關係
指導教授(外文):Hung, Jui-Cheng
口試委員:李彥賢王偉權
口試委員(外文):Li, Yen-HsienWang, Wei-Chuan
口試日期:2017-06-20
學位類別:碩士
校院名稱:中國文化大學
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:2017
畢業學年度:105
語文別:中文
論文頁數:74
中文關鍵詞:風險值極值理論流動性
外文關鍵詞:Value at Riskextreme value theoryliquidity
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風險值的觀念近年來逐漸被金融機構級投資人所接受,目前已是眾所皆知的風險控管工具。極值理論(EVT)的運用非常廣泛,財務風險的衡量亦是其中一項。極值理論主要分為Block Maxima模型,即一般化極值理論(generalized extreme value, GEV),和POT (peak-over-threshold) 模型。Jorino (1996)指出極值理論的優點在於評估厚尾現象的風險值。
本篇論文主要選取100家股票依流動性高低排序,分成10組投資組合,利用極值理論估計風險值。並與歷史模擬法...等其他衡量風險值模型相比較,探討流動性對於風險值評估的影響,預期極值理論在高流動性與低流動性的投資組合中,評估風險值最為精準。


關鍵字:風險值(Value at Risk),極值理論(extreme value theory),
流動性(liquidity)

Within these few years, the concept of Value-at-Risk (VaR) has been accepted by financial institutions even investors, and it become a very famous risk control tool. And Extreme Value Theory (EVT) by Gnedenko is the very popular evaluation to calcu-late financial risk in the world.
There are two methods of Extreme Value Theory to calculate VaR, one is Block Maxima model, i.e. Generalized Extreme Value (GEV); the other is Peak-Over-Threshold (POT). Even, the Extreme Value Theory is the best way to eval-uate the risk of fat-tail phenomenon was proved by Jorino in 1996.
You will find 10 parties of portfolio selected from 100 stocks of financial market have been chosen based on different liquidity from low to high per this paper. They are evaluated by quite different ways of Value-at-Risk, of course Extreme Value Theory (EVT) included. Believe, Extreme Value Theory will be the best choice of evaluated VaR in the extreme events, and explore the impact of liquidity on VaR estimation as well.


Key Words: Value at Risk, extreme value theory, liquidity

內容目錄
中文摘要...................... iii
英文摘要...................... iv
致謝辭....................... v
內容目錄...................... vi
表目錄....................... viii
圖目錄....................... ix
第一章  緒論................... 1
第一節  研究背景與動機.............. 1
第二節  研究問題與目的.............. 4
第三節  研究架構與流程.............. 6
第二章  文獻探討................. 8
第一節  巴塞爾資本協定.............. 8
第二節  模型回顧與極值理論相關應用........ 10
第三節  流動性對風險值之影響........... 15
第三章  研究方法................. 17
第一節  研究樣本與資料選取............ 17
第二節  風險值模型介紹.............. 19
第三節  回溯測試................. 23
第四章  實證結果與分析.............. 27
第一節  流動性對風險值估計之影響......... 27
第二節  風險值估計之準確性分析.......... 28
第三節  風險值估計之效率性分析.......... 46
第四節  多變量風險值估計之準確性分析....... 52
第五節  多變量風險值估計之效率性分析....... 61
第五章  結論................... 66
第一節  研究結論................. 66
第二節  研究建議................. 69
參考文獻 ..................... 70

參 考 文 獻
一、中文部分

李志銘(2001),漲跌幅限制對市場之影響─以台灣股市為例,國立中正大學會計學研究所碩士論文。

蔡垂君,李存修(2015),重新評估台灣指數期貨之流動性調整風險值,期貨與選擇權學刊,8(1),1-40。

二、英文部分

Al Janabi, M. A. (2008). Integrating liquidity risk factor into a parametric value at risk method. Journal of Trading, 3(3), 76-87.

Al Janabi, M. A. (2011). A generalized theoretical modelling approach for the assessment of economic-capital under asset market liquidity risk constraints. The Service Industries Journal, 31(13), 2193-2221.

Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.

Balkema, A. A., & De Haan, L. (1974). Residual life time at great age. The Annals of Probability, 2(5), 792-804.

Bali, T. G., & Theodossiou, P. (2007). A conditional-SGT-VaR approach with alternative GARCH models. Annals of Operations Research, 151(1), 241-267.

Bangia, A., Diebold, F. X., Schuermann, T., & Stroughair, J. D.
(2002). Modeling liquidity risk, with implications for tradi-tional market risk measurement and management. Working paper for Wharton Financial Institutions Center.

Barone-Adesi, G., Bourgoin, F., & Giannopoulos, K. (1998). Don’t look back. Working paper, University of Neapolis at Paphos.

Bauwens, L. & Laurent, S. (2002). A new class of multivariate skew densities, with application to garch models. Journal of Business & Economic Statistics, 23(3), 346-354

Bollerslev, T. (1990). Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72, 498-505.

Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39(4), 841-862.

Danielsson, J., James, K. R., Valenzuela, M., & Zer, I. (2016). Model risk of risk models. Journal of Financial Stability, 23, 79-91.

De Jesús, R., Ortiz, E., & Cabello, A. (2013). Long run peso/dollar exchange rates and extreme value behavior: Value at risk modeling. The North American Journal of Economics and Finance, 24, 139-152.

DuMouchel, W. H. (1983). Estimating the stable index α in order to measure tail thickness: A critique. The Annals of Statistics, 11(4), 1019-1031.

Engel, J., & Gizycki, M. (1999). Conservatism, accuracy and efficiency: Comparing value-at-risk models. Working paper for Australian Prodential Regulation Authority.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Journal of the Econometric Society, 50(4) 987-1007.

Engle, R. (2002). Dynamic conditional correlation: A simple cla-ss of multi¬variate generalized autoregressive conditional he-teroskedasticity models. Journal of Business & Economic Sttistics, 20(3), 339-350.

Fernandez, V. (2005). Risk management under extreme events. International Review of Financial Analysis, 14(2), 113-148

Gnedenko, B. (1943). Sur la distribution limite du terme maximum d’une s´erie al´eatoire. Annals of Mathematics, 44, 423-453.

Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. The Annals of Statistics, 3(5), 1163-1174.

Jorion, P. (1996). 〖"Risk" 〗^"2" : Measuring the risk in value at risk. Financi¬a¬l Analysts Journal, 52(6), 47-56.

Karmakar, M., & Paul, S. (2016). Intraday risk management in approach. International Review of Financial Analysis, 44(3), 34-55.

Karolyi, G. A., Lee, K. H., & van Dijk, M. A. (2012). Understanding commonality in liquidity around the world. Journal of Financial Economics, 105(1), 82-112.

Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3(2).

Marimoutou, V., Raggad, B., & Trabelsi, A. (2009). Extreme value theory and value at risk: Application to oil market. Energy Economics, 31(4), 519-530.

McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach. Journal of Empirical Finance, 7(3-4), 271-300.

Pickands III, J. (1975). Statistical inference using extreme order statistics. The Annals of Statistics, 3(1) 119-131.

Rossignolo, A. F., Fethi, M. D., & Shaban, M. (2012). Value-at-risk models and basel capital charges: Evidence from emerging and frontier stock markets. Journal of Financial Stability, 8(4), 303-319.

Suaiso, J. O. Q., & Mapa, D. S. (2010). Measuring market risk using extreme value theory. Philippine Review of Economics, 46(2).


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