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| Title: | 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 |
| Authors: | 林朝陽
Lin, Chao-Yang |
| Contributors: | 林士貴
Lin, Shih-Kuei
林朝陽
Lin, Chao-Yang |
| Keywords: | 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 |
| Date: | 2020 |
| Issue Date: | 2020-09-02 11:48:42 (UTC+8) |
| Abstract: | 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. |
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| Description: | 博士
國立政治大學
金融學系
100352508 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100352508 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001230 |
| Appears in Collections: | [金融學系] 學位論文
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