English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23482617      Online Users : 238
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/67106


    Title: 風險值方法實證研究─以一壽險公司為例
    An Empirical Test on the Value-at-Risk Estimation of a Life Insurance Company
    Authors: 蕭國緯
    Hsiao, Justin K.W.
    Contributors: 蔡政憲
    Tsai, Jason C.H.
    蕭國緯
    Hsiao, Justin K.W.
    Keywords: 市場風險
    簡化型模型
    單變數模型
    風險值
    Market Risk
    Reduced-formed
    Univariate Method
    Value-at-Risk (VaR)
    Date: 2013
    Issue Date: 2014-07-01 12:07:24 (UTC+8)
    Abstract: 風險值(VaR)目前是金融機構計算市場風險最常使用的方法。雖然這個方法這麼頻繁地被使用,它仍然有一些缺陷。近年來,金融機構的投資活動成長相當快速,其投資的商品也越來越多元和複雜,在這樣的情況下,公司內部複雜的結構型模型無法在99%信賴水準下,比簡單的單變數模型有更好的準確性和預測能力。因此,單變數模型對於公司內部的結構性模型至少是一個相當有用的參考和輔助。本篇論文是第一篇使用單變數模型並採用一家台灣壽險公司歷史資料的實證論文,且有比較單變數模型和公司內部多變數結構模型的表現。
    Value-at-Risk (VaR), nowadays, is the most widely adopted risk management method for measuring market risk in financial institutions, like banks, securities companies, and insurance companies etc. Although this measure is so widespread, it has some setbacks. In recent year, trading activities in financial institutions have grown substantially and became progressively more diverse and complex. In this situation, the complicate structural models were not able to outperform a simple univariate model in terms of accuracy and forecasting ability in 99th percentile. Univariate models, therefore, are at least a useful complement to large structural models and might even be sufficient for forecasting VaR. This paper is the first article adopts univariate methods with historical data from a life insurance company in Taiwan and provides a comparison of the performance between the univariate one and the models actually in use within firm.
    Reference: English Literature
    Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3), 203-228.
    Berkowitz, J., & O’Brien, J. (2002). How Accurate Are Value‐at‐Risk Models at Commercial Banks? The Journal of Finance, 57(3), 1093-1111.
    Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
    Christoffersen, P. F. (1998). Evaluating interval forecasts. International economic review, 841-862.
    Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk management? Review of Economics and Statistics, 82(1), 12-22.
    Duffie, D., & Pan, J. (1997). An overview of value at risk. The Journal of derivatives, 4(3), 7-49.
    Enders, W. (2008). Applied econometric time series. John Wiley & Sons.
    Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22(4), 367-381.
    Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Federal Reserve Bank of New York Economic Policy Review, 2(1), 39-69.
    Holton, G. A. (2002). History of Value-at-Risk: Working paper. Contingency Analysis, Boston.
    Ian Farr, H. M., Mark Scanlon, Simon Stronkhorst. (February 2008). Economic Capital for Life Insurance Companies: Towers Perrin.
    Jorion, P. (1997). Value at risk: the new benchmark for controlling market risk (Vol. 2): McGraw-Hill New York.
    Jorion, P. (2002). How informative are value-at-risk disclosures? The Accounting Review, 77(4), 911-931.
    Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. THE J. OF DERIVATIVES, 3(2).
    Lopez, J. A., & Walter, C. A. (2000). Evaluating covariance matrix forecasts in a value-at-risk framework.
    Marshall, C., & Siegel, M. (1997). Value at risk: Implementing a risk measurement standard. The Journal of derivatives, 4(3), 91-111.
    Zangari, P. (1997). Streamlining the market risk measurement process. RiskMetrics Monitor, 1, 29-35.

    Chinese Literature
    楊奕農, & 經濟. (2009). 時間序列分析: 經濟與財務上之應用. 雙葉書廊.
    Description: 碩士
    國立政治大學
    風險管理與保險研究所
    101358001
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101358001
    Data Type: thesis
    Appears in Collections:[風險管理與保險學系 ] 學位論文

    Files in This Item:

    File SizeFormat
    800101.pdf963KbAdobe PDF175View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback