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題名 市場風險資本計提標準法與預期損失各模型之比較分析: GARCH、T-GARCH、AP-ARCH、POT與類神經網路模型
The Standardized Approach to Market Risk Capital Requirement and the Comparative Analysis of Models in Expected Shortfall Methods: GARCH、T-GARCH、AP-ARCH、POT and Neural Network作者 林朝陽
Lin, Chao-Yang貢獻者 林士貴
Lin, Shih-Kuei
林朝陽
Lin, Chao-Yang關鍵詞 交易簿基礎原則審視(FRTB)
預期損失
風險值
類神經網路
循環類神經網路
Fundamental Review of the Trading Book(FRTB)
Expected Shortfall
Value at Risk
GARCH
T-GARCH
AP-ARCH
POT
Neural Networks
Recurrent Neural Networks日期 2020 上傳時間 2-Sep-2020 11:48:42 (UTC+8) 摘要 2023年1月1日交易簿基礎原則審視(FRTB)實行日，此新計提方法對全球金融機構資本適足率將造成衝擊，標準法或內部模型法都有重大改變，本研究首先將台灣的銀行市場風險與資本計提資料進行整理分析，結果高市場風險資產不一定有高投資績效，並實例試算標準法；內部模型法則用GARCH、T-GARCH、AP-ARCH和極值理論POT各預期損失模型分析，最後將模型資料導入機器學習估計預期損失。結果可得2006年以前匯率、權益和利率因子，有多個信賴水準下可用一般風險值(VaR)估計，且預期損失有過於保守的問題，使實際低於理論失敗率過多無法通過檢定，但到了次貸風暴之後，僅有匯率因子可用一般風險值估計外，權益和利率因子多適用預期損失模型或條件後風險值，表示近幾年的各種金融資產報酬率分配需考慮厚尾、偏態和極端值情形，若用風險值模型需再考慮各條件的厚尾和偏斜分配，亦或採用預期損失模型。另在金融事件期間中，條件預期損失和風險值，以AP-ARCH為最適模型條件，考慮分配的模型，則是搭配歷史(HS)分配和POT為最適次數最多。最後RNN可結合各模型優缺點，訓練出更為精準的預期損失模型，以解決傳統模型須作分配假設和非線性估計的問題。
On January 1, 2023 is the Fundamental Review of the Trading Book (FRTB) implementation date. This New Basel Capital Accord will impact the capital adequacy ratio of global financial institutions. Both the standardized and the Internal Model Approach have major changes. This study will first introduce Taiwan bank`s capital accord data. As a result, high market risk assets do not necessarily have high investment performance.Then we trial to calculate new Standardized Method; Expected Shortfall of the Internal Model Approach are analyzed with GARCH, T-GARCH, AP-ARCH and POT models, and finally the model data is imported into machine learning to estimate Expected Shortfall.As a result, we can obtain foreign exchange, equity and interest rate factors before 2006. They can be used to estimate the Value at Risk (VaR), and the Expected Shortfall is too conservative, but after the Financial Crisis, only the foreign exchange factor can be used VaR. The equity and interest rate factors mostly apply the Expected Shortfall or condition VaR, indicating that the distribution of various financial asset returns in recent years needs to consider fat-tailed、skewness and extreme values, if the VaR is used, the fat-tailed and skewed distribution of each condition must be considered, or the Expected Shortfall may be used. In addition, during the period of Financial Crisis, AP-ARCH is the most suitable model for Expected Shortfall and conditional VaR. Considering the allocation model, the history (HS) and POT are the most suitable allocation. Finally, RNN can combine the advantages and disadvantages of each model to train a more accurate Expected Shortfall to solve the problem that the traditional model must make allocation assumptions and nonlinear estimation.參考文獻 中文部分：1.巴曙松、劉曉依、朱元倩，(2018)。巴塞爾III：金融監管的十年重構，中國：中國金融出版社。2.朱君亞，(2018)。金融壓力事件預警模型類神經網路、支援向量機與羅吉斯迴歸之比較，國立政治大學，金融學系研究所，台北。3.李佩蓮，(2012)。銀行資本管理，國立中山大學，財務管理學系研究所，高雄。4.林昆立、吳庭斌，(2012)。新巴塞爾資本協定三大支柱與銀行風險的關係：全球實證。管理學報，29(2)，121-153。5.林金鈴，(2004)。銀行資本風險之評估─風險值與期望尾端損失值(ETL)之應用，國立中興大學，企業管理學系研究所，台中。6.林靖宜，(2014)。運用關連結構模型及重點抽樣法估計投資組合的期望損失，國立中山大學，應用數學系研究所，高雄。7.林楚雄、高子荃、邱瓊儀，(2005)。結合GARCH模型與極值理論的風險值模型，管理學報，22(1)，133-154。8.林朝陽，(2019)。新市場風險資本計提-交易簿基礎原則審視(FRTB)之方法說明與整理，貨幣觀測與信用評等139期。9.柯博倫、雷立芬，(2011)。GARCH估測風險值(VaR)績效之探討，台灣銀行季刊，62(4)，234-243。10.洪儒瑤、古永嘉、康健廷，(2006)。ARMA-GARCH風險值模型預測績效實證，中華技術學院學報，(34)，13-35。11.郭照榮、李宜熹、陳勤明，(2013)。Basel Ⅲ對金融穩定及貨幣政策之影響。中央銀行季刊，35(2)，11-59。12.許郁甄，(2009)。新巴塞爾資本協定與衍生性金融商品操作影響本國銀行業經營效率之實證研究-應用資料包絡分析法，國立政治大學，商管專業學院碩士學位學程，台北。13.許力夫，(2018)。以類神經網路建構風險值模型，國立政治大學，應用數學系研究所，台北。14.黃朝熙、鍾經樊、謝依珊和周卉敏，(2018)。本國銀行業資本結構分析--跨越循環期的槓桿比率與資本適足率比較，中央銀行季刊，40(3)，15-50。15.黃御綸，(2004)。極值理論與整合風險衡量，國立政治大學，金融學系研究所，台北16.黃瑞峰，(2016)。銀行風險管理與資本管理之技術與運用，台灣土地銀行出國報告。17.陳學華、楊輝耀，(2003)。應用極值理論和APARCH模型估測股市風險，中華管理評論國際學報，6(5)。18.陳嘉敏，(2007)。衡量銀行市場風險-VaR與ETL模型的應用，國立政治大學，金融學系研究所，台北。19.楊奕農，(2009)。時間序列分析經濟與財務上之應用，第二版，台北：雙葉書廊。20.葉怡成，(2003)。類神經網路模式應用與實作，台北：儒林圖書有限公司。21.葉家易、林朝陽，(2020)。新市場風險FRTB標準法試算與說明，貨幣觀測與信用評等143期。22.廖偉真、雷立芬，（2010）。不同樣本頻率之股市波動性探討-GARCH、T-GARCH與EGARCH之比較，台灣銀行季刊，61(4)，294-307。23.鄭政秉、梁連文、陳仁屏，(2015)。模擬BASEL III新資本監管對台灣銀行業成本效率之影響，管理與系統（TSSCI期刊），22(2)，175-203。英文部分：1.Artzner, P., Delbaen, F., Eber, J. 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Confidence Intervals for ARMA-GARCH Value-at-Risk: The Case of Heavy Tails and Skewness, Computational Statistics and Data Analysis, 100, 545–559.53.Wang, C.P., Lin, S. H., Huang, H. H., Wu, P. C., (2012). Using neural network for forecasting TXO price under different volatility models, Expert Systems with Applications, 39, 5025-5032.54.Yamai, Y. & Yoshiba, T., (2002). Comparative Analyses of Expected Shortfall and Value-at-Risk: Their Estimation Error, Decomposition, and Optimization, Monetary and Economic Studies, 20(1), 87-121. 描述 博士
國立政治大學
金融學系
100352508資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100352508 資料類型 thesis dc.contributor.advisor 林士貴 zh_TW dc.contributor.advisor Lin, Shih-Kuei en_US dc.contributor.author (Authors) 林朝陽 zh_TW dc.contributor.author (Authors) Lin, Chao-Yang en_US dc.creator (作者) 林朝陽 zh_TW dc.creator (作者) Lin, Chao-Yang en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:48:42 (UTC+8) - dc.date.available 2-Sep-2020 11:48:42 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:48:42 (UTC+8) - dc.identifier (Other Identifiers) G0100352508 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131504 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 100352508 zh_TW dc.description.abstract (摘要) 2023年1月1日交易簿基礎原則審視(FRTB)實行日，此新計提方法對全球金融機構資本適足率將造成衝擊，標準法或內部模型法都有重大改變，本研究首先將台灣的銀行市場風險與資本計提資料進行整理分析，結果高市場風險資產不一定有高投資績效，並實例試算標準法；內部模型法則用GARCH、T-GARCH、AP-ARCH和極值理論POT各預期損失模型分析，最後將模型資料導入機器學習估計預期損失。結果可得2006年以前匯率、權益和利率因子，有多個信賴水準下可用一般風險值(VaR)估計，且預期損失有過於保守的問題，使實際低於理論失敗率過多無法通過檢定，但到了次貸風暴之後，僅有匯率因子可用一般風險值估計外，權益和利率因子多適用預期損失模型或條件後風險值，表示近幾年的各種金融資產報酬率分配需考慮厚尾、偏態和極端值情形，若用風險值模型需再考慮各條件的厚尾和偏斜分配，亦或採用預期損失模型。另在金融事件期間中，條件預期損失和風險值，以AP-ARCH為最適模型條件，考慮分配的模型，則是搭配歷史(HS)分配和POT為最適次數最多。最後RNN可結合各模型優缺點，訓練出更為精準的預期損失模型，以解決傳統模型須作分配假設和非線性估計的問題。 zh_TW dc.description.abstract (摘要) On January 1, 2023 is the Fundamental Review of the Trading Book (FRTB) implementation date. This New Basel Capital Accord will impact the capital adequacy ratio of global financial institutions. Both the standardized and the Internal Model Approach have major changes. This study will first introduce Taiwan bank`s capital accord data. As a result, high market risk assets do not necessarily have high investment performance.Then we trial to calculate new Standardized Method; Expected Shortfall of the Internal Model Approach are analyzed with GARCH, T-GARCH, AP-ARCH and POT models, and finally the model data is imported into machine learning to estimate Expected Shortfall.As a result, we can obtain foreign exchange, equity and interest rate factors before 2006. They can be used to estimate the Value at Risk (VaR), and the Expected Shortfall is too conservative, but after the Financial Crisis, only the foreign exchange factor can be used VaR. The equity and interest rate factors mostly apply the Expected Shortfall or condition VaR, indicating that the distribution of various financial asset returns in recent years needs to consider fat-tailed、skewness and extreme values, if the VaR is used, the fat-tailed and skewed distribution of each condition must be considered, or the Expected Shortfall may be used. In addition, during the period of Financial Crisis, AP-ARCH is the most suitable model for Expected Shortfall and conditional VaR. Considering the allocation model, the history (HS) and POT are the most suitable allocation. Finally, RNN can combine the advantages and disadvantages of each model to train a more accurate Expected Shortfall to solve the problem that the traditional model must make allocation assumptions and nonlinear estimation. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與目的 1第二節 論文架構與流程 7第二章 文獻探討 9第一節 巴塞爾資本協定（Basel Accord） 9第二節 風險值(Value at Risk)文獻 16第三節 預期損失(Expected Shortfall)文獻 19第四節 極值理論(Extreme Value Theory)文獻 22第五節 機器學習(Machine Learning)文獻 24第六節 研究範圍與貢獻 27第三章 研究方法與模型 28第一節 標準法資本計提方法 28第二節 內部模型法資本計提方法 32第三節 預期損失模型 36第四節 機器學習類神經網路模型 42第四章 實證研究 47第一節 台灣之銀行市場風險性資產和資本計提分析 47第二節 標準法資本計提實例 53第三節 各預期損失模型計算 73第五章 結論與建議 96第一節 結論 96第二節 未來建議 98參考文獻 100 zh_TW dc.format.extent 13484615 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100352508 en_US dc.subject (關鍵詞) 交易簿基礎原則審視(FRTB) zh_TW dc.subject (關鍵詞) 預期損失 zh_TW dc.subject (關鍵詞) 風險值 zh_TW dc.subject (關鍵詞) 類神經網路 zh_TW dc.subject (關鍵詞) 循環類神經網路 zh_TW dc.subject (關鍵詞) Fundamental Review of the Trading Book(FRTB) en_US dc.subject (關鍵詞) Expected Shortfall en_US dc.subject (關鍵詞) Value at Risk en_US dc.subject (關鍵詞) GARCH en_US dc.subject (關鍵詞) T-GARCH en_US dc.subject (關鍵詞) AP-ARCH en_US dc.subject (關鍵詞) POT en_US dc.subject (關鍵詞) Neural Networks en_US dc.subject (關鍵詞) Recurrent Neural Networks en_US dc.title (題名) 市場風險資本計提標準法與預期損失各模型之比較分析: GARCH、T-GARCH、AP-ARCH、POT與類神經網路模型 zh_TW dc.title (題名) The Standardized Approach to Market Risk Capital Requirement and the Comparative Analysis of Models in Expected Shortfall Methods: GARCH、T-GARCH、AP-ARCH、POT and Neural Network en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部分：1.巴曙松、劉曉依、朱元倩，(2018)。巴塞爾III：金融監管的十年重構，中國：中國金融出版社。2.朱君亞，(2018)。金融壓力事件預警模型類神經網路、支援向量機與羅吉斯迴歸之比較，國立政治大學，金融學系研究所，台北。3.李佩蓮，(2012)。銀行資本管理，國立中山大學，財務管理學系研究所，高雄。4.林昆立、吳庭斌，(2012)。新巴塞爾資本協定三大支柱與銀行風險的關係：全球實證。管理學報，29(2)，121-153。5.林金鈴，(2004)。銀行資本風險之評估─風險值與期望尾端損失值(ETL)之應用，國立中興大學，企業管理學系研究所，台中。6.林靖宜，(2014)。運用關連結構模型及重點抽樣法估計投資組合的期望損失，國立中山大學，應用數學系研究所，高雄。7.林楚雄、高子荃、邱瓊儀，(2005)。結合GARCH模型與極值理論的風險值模型，管理學報，22(1)，133-154。8.林朝陽，(2019)。新市場風險資本計提-交易簿基礎原則審視(FRTB)之方法說明與整理，貨幣觀測與信用評等139期。9.柯博倫、雷立芬，(2011)。GARCH估測風險值(VaR)績效之探討，台灣銀行季刊，62(4)，234-243。10.洪儒瑤、古永嘉、康健廷，(2006)。ARMA-GARCH風險值模型預測績效實證，中華技術學院學報，(34)，13-35。11.郭照榮、李宜熹、陳勤明，(2013)。Basel Ⅲ對金融穩定及貨幣政策之影響。中央銀行季刊，35(2)，11-59。12.許郁甄，(2009)。新巴塞爾資本協定與衍生性金融商品操作影響本國銀行業經營效率之實證研究-應用資料包絡分析法，國立政治大學，商管專業學院碩士學位學程，台北。13.許力夫，(2018)。以類神經網路建構風險值模型，國立政治大學，應用數學系研究所，台北。14.黃朝熙、鍾經樊、謝依珊和周卉敏，(2018)。本國銀行業資本結構分析--跨越循環期的槓桿比率與資本適足率比較，中央銀行季刊，40(3)，15-50。15.黃御綸，(2004)。極值理論與整合風險衡量，國立政治大學，金融學系研究所，台北16.黃瑞峰，(2016)。銀行風險管理與資本管理之技術與運用，台灣土地銀行出國報告。17.陳學華、楊輝耀，(2003)。應用極值理論和APARCH模型估測股市風險，中華管理評論國際學報，6(5)。18.陳嘉敏，(2007)。衡量銀行市場風險-VaR與ETL模型的應用，國立政治大學，金融學系研究所，台北。19.楊奕農，(2009)。時間序列分析經濟與財務上之應用，第二版，台北：雙葉書廊。20.葉怡成，(2003)。類神經網路模式應用與實作，台北：儒林圖書有限公司。21.葉家易、林朝陽，(2020)。新市場風險FRTB標準法試算與說明，貨幣觀測與信用評等143期。22.廖偉真、雷立芬，（2010）。不同樣本頻率之股市波動性探討-GARCH、T-GARCH與EGARCH之比較，台灣銀行季刊，61(4)，294-307。23.鄭政秉、梁連文、陳仁屏，(2015)。模擬BASEL III新資本監管對台灣銀行業成本效率之影響，管理與系統（TSSCI期刊），22(2)，175-203。英文部分：1.Artzner, P., Delbaen, F., Eber, J. 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