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題名 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作者 蕭國緯
Hsiao, Justin K.W.貢獻者 蔡政憲
Tsai, Jason C.H.
蕭國緯
Hsiao, Justin K.W.關鍵詞 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)日期 2013 上傳時間 1-Jul-2014 12:07:24 (UTC+8) 摘要 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.參考文獻 English LiteratureArtzner, 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. 描述 碩士
國立政治大學
風險管理與保險研究所
101358001
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101358001 資料類型 thesis dc.contributor.advisor 蔡政憲 zh_TW dc.contributor.advisor Tsai, Jason C.H. en_US dc.contributor.author (Authors) 蕭國緯 zh_TW dc.contributor.author (Authors) Hsiao, Justin K.W. en_US dc.creator (作者) 蕭國緯 zh_TW dc.creator (作者) Hsiao, Justin K.W. en_US dc.date (日期) 2013 en_US dc.date.accessioned 1-Jul-2014 12:07:24 (UTC+8) - dc.date.available 1-Jul-2014 12:07:24 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2014 12:07:24 (UTC+8) - dc.identifier (Other Identifiers) G0101358001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/67106 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 風險管理與保險研究所 zh_TW dc.description (描述) 101358001 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents Keywords iAbstract iiTable of Contents iiiList of Figures ivList of Tables vAcknowledgements viCHAPTER 1:INTRODUCTION 1CHAPTER 2:LITERATURE REVIEW 32.1 The Rise of Value-at-Risk 32.2 Regulatory Approval of Proprietary VaR Measures 42.3 Application of VaR: Economic Capital 62.4 Limitation of Banks’ Model 72.5 Reduced-Form Method 8CHAPTER 3:DATA DESCRIPTION 103.1 Daily Trading Profit and Loss 103.2 Daily VaR 11CHAPTER 4:RESEARCH METHOD 154.1 Value-at-Risk (VaR) 154.2 Time Series Model 164.2.1 The ARMA Process 174.2.2 The ARCH/ GARCH Process 184.3 Model Selection 214.4 Back Testing 224.4.1 Kupiec 224.4.2 Christoffersen 23CHAPTER 5:RESULTS 25CHAPTER 6:CONCLUSIONS 36CHAPTER 7:SUGGESTIONS 397.1 More Observations 397.2 Crisis Test 397.3 VaR’s Drawback 397.4 Different Forecasting Method 39BIBLIOGRAPHY 40 zh_TW dc.format.extent 987081 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101358001 en_US dc.subject (關鍵詞) 市場風險 zh_TW dc.subject (關鍵詞) 簡化型模型 zh_TW dc.subject (關鍵詞) 單變數模型 zh_TW dc.subject (關鍵詞) 風險值 zh_TW dc.subject (關鍵詞) Market Risk en_US dc.subject (關鍵詞) Reduced-formed en_US dc.subject (關鍵詞) Univariate Method en_US dc.subject (關鍵詞) Value-at-Risk (VaR) en_US dc.title (題名) 風險值方法實證研究─以一壽險公司為例 zh_TW dc.title (題名) An Empirical Test on the Value-at-Risk Estimation of a Life Insurance Company en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) English LiteratureArtzner, 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. zh_TW
