本論文內容涵蓋衡量流動性風險、作業風險及主權風險三部分的實證研究，分述如下：
第一部分、流動性風險的衡量實證研究係以蒐集台灣全體金融機構於2002年9月16日至2010年8月31日期間，參加大額支付系統的金融機構支付中央銀行之日間透支利息之成本資料，進行實證建模及估計銀行流動性成本風險尾部參數。本研究首先考量巴賽爾資本協定III有關改進衡量標準之概括性設計，是否有助於提升銀行部門吸收財務及經濟壓力帶來衝擊之能力，並藉以瞭解支付系統之流動性風險的特性與集中度，再者運用配適一般化柏瑞圖分配(Generalized Pareto Distribution, GPD)並透過標準參數模型比較，以拔靴法(Bootstrap)估計該參數之信賴區間。另計算風險值(Value at Risk, VaR)及期望損失(Expected Shortfall, ES)以有效衡量流動性風險。研究結果顯示在大額支付系統下銀行之流動性損失的尾部行為，能提供精準及有用資訊予金融監理主管機關及中央銀行當局參考。本研究亦成功地應用統計方法，藉由潛在監理行動衡量分析台灣銀行體系之流動性成本，以有效建立另一種監理方法。

第二部分、有關作業風險的衡量係先蒐集於1995年至2009年期間台灣地區全體商業銀行的共計323筆重要作業損失資料，以構建模型及估計銀行的作業風險分配之尾部參數。藉由三種Coupla函數計算各種損失事件間之相關性。在巴賽爾資本協定II下，檢定不同的損失型態分類間之獨立性，俾瞭解商業銀行作業風險的特性與集中度。再者，對一般化柏瑞圖分配(GPD)估計參數，並比較各種標準參數模型之配適情形，以拔靴法估計該參數之信賴區間。另計算風險值(VaR)及期望損失(ES)以有效衡量作業風險。研究結果能提供台灣金融機構作為執行作業風險管理之有用資訊，並賦予金融監理之參考，且有助於銀行衡量其作業風險資本計提參考。

第三部分、主權風險衡量之研究係利用Coupla-ARMAX-GJR-GARCH模型探討政府干預匯率市場下產生主權風險，藉由歐元與人民幣匯率波動關連，顯示中國人民幣匯率制度的政策干預現象。本研究使用2005年1月至2010年3月人民幣與歐元匯率日資料，先建立ARMAX-GJR-GARCH方程式模型以重新檢視利率平價理論，並發現人民幣匯率之結構轉變狀況。該研究結果提供一種驗證政府干預之主權風險與政治影響經濟現象之重要資訊，有助於金融機構風險管理者的檢驗歐元與人民幣之匯率制度間差異。

This dissertation includes three aspects of the empirical study of measurement on liquidity risk, operational risk and sovereign risk illustrate as follows:

First, using the intraday overdraft cost of all Taiwanese banks pay to Central Bank (CB) in the Large Value Payment System (LVPS) to measure the liquidity risk from Sep 16, 2002 to Aug 31, 2010, I collected the interest cost of overdraft to focus on modeling and estimating tail parameters of bank liquidity cost. I first take into account to illustrate whether the Basel III is a comprehensive set of reform measures, to improve the banking sector’s ability to absorb shocks arising from financial and economic stress and to understand the characteristic and concentration of liquidity risk of payment system. I further centralize the Generalized Pareto Distribution (GPD) and compare it with standard parametric modeling. Bootstrap method is used to estimate the confidence interval of parameters. In addition, Value at Risk (VaR) and expected shortfall (ES) calculations were performed. My results captured the tail behavior of banks’ liquidity losses from the LVPS, which provide the precise and useful information for the supervisory authorities and the central bank of Taiwan. I contributed to setting alternative oversight method by using potential supervisory action to measure liquidity cost of the banking system of Taiwan, and apply the statistic methodology successfully.

Secondly, from the loss data associated with significant operational risks of Taiwanese commercial banks over the period from 1995 to 2009, I collected a set of 323 observations to use for modeling and estimating tail parameters of bank’s operational distribution. By means of three copula functions, I calculated correlations between event pairs, test for independent between different classifications of risk types of the Basel II framework and seek to understand the characteristic and concentration of commercial banks’ operational risks. Further, I estimated parameters for the generalized Pareto distribution (GPD) and compare its fit with those of standard parametric models. A bootstrap method is used to estimate the confidence intervals for parameter values. In addition, value-at-risk (VaR) and expected shortfall (ES) calculations are performed. My result provides important information that financial supervisory authorities can use when accounting for operational losses of commercial banks. This research also contributes to the measurement of Taiwanese commercial banks operational risk capital for the banks.

Last, I provided new evidence regarding the shock effects of the PBC intervention in the FX market by comparing the volatility of exchange rate of Euro dollar (EUD) and Chinese Ranminbi (RMB) against the US dollar (USD) and showing policy interference of Chinese the exchange rate system. Firstly I modeled the ARMAX-GJR-GARCH equation to reexamine interest parity theory and find the structure break of the exchange rate of the RMB then I set up the new Copula-ARMAX-GJR-GARCH model by using daily exchange rate of EUD and RMB against USD during the period from January 2005 to March 2010. The result provides the very important information that I proved the sovereign risk of official intervention and political influence economy. My new research also contributes to examine a discrepancy between the exchange rate system of EU and China for risk managers in financial institutions.

CONTENTS
List of Tables                                            vi
List of Figures                                          vii
Chapter 1 Introduction                                     1
1.1 Motivations                                            2
1.2 Objectives                                             2
1.3 The structure of this dissertation                     3
1.4 The measurement of liquidity risk for Taiwanese banks from the LVPS                                              4
1.5 The measurement of capital for operational risk in Taiwanese commercial banks                                 7
1.6 The measurement for sovereign risk of official intervention by the volatility relationship between EUD and RMB                                                        9

Chapter 2 Methodology, theory and models                  12
2.1 The measurement methodology of liquidity risk and operational risk                                          13
2.1.1 Extreme value theory                                13
2.1.2 Generalized Pareto distribution                     13
2.1.3 Steps in applying GPD to exploratory data analysis  14
2.1.4 Threshold selection                                 14
2.1.5 Calculation of VaR                                  15
2.1.6 Expected shortfall                                  16
2.1.7 Goodness-of-fit                                     17
2.2 Copula function-a brief review                        18
2.3 The theory model for sovereign risk of official intervention                                              20
2.3.1 Applying the interest rate parity                   20
2.3.2 Theoretical models                                  21
2.3.3 Copula-ARMAX-GJR-GARCH model                        21

Chapter 3 Empirical results and analysis                  25
3.1 The empirical results for measurement of liquidity risk                                                      26
3.1.1 Data description                                    26
3.1.2 Empirical results analysis                          26
3.2 The empirical results for measurement of operational risk                                                      33
3.2.1 Data description                                    33
3.2.2 Empirical results analysis                          33
3.3 The empirical results in sovereign risk of official intervention                                              50
3.3.1 Data description                                    50
3.3.2 Empirical results analysis                          50

Chapter 4 Conclusion                                      57
4.1 The conclusion for measurement of liquidity risk      58
4.2 The conclusion for measurement of operational risk    60
4.3 The conclusion for sovereign risk of official intervention by the volatility 
relationship between EUD and RMB                          62

References                                                63
List of Tables
Table 1 Frequencies of liquidity loss data                27
Table 2 Summary statistics for liquidity loss data        27
Table 3 Parametric estimations for fitted functions       28
Table 4 The VaR and ES of GPD                             31
Table 5 Goodness-of-fit for GPD model                     32
Table 6 Bootstrap confidence intervals for GPD            32
Table 7 Basel II operational risk factors & event type classification                                            34
Table 8 Statistics for operational risk event types       34
Table 9 Results of copula Kendall tau for event-pairs     36
Table 10 Test of independent for event-pairs              40
Table 11 Summary statistics                               41
Table 12 Parametric estimations for fitted functions      42
Table 13 The VaR and ES of GPD                            47
Table 14 Goodness-of-fit for GPD model                    48
Table 15 Bootstrap confidence intervals for GPD           48
Table 16 The results of Chow test                         51
Table 17 Results from the ARMAX–GJR-GARCH(1,1)model-EUD  52
Table 18 Results from the ARMAX–GJR-GARCH(1,1)model-RMB  53
Table 19 The Kendall’s tau of copula functions-innovations between EUD and RMB                                       56
Table 20 The Kendall’s tau of copula functions-between EUD and RMB exchange rate                                     56

List of Figures
Figure 1(a). 1-F(x) on logarithmic scaling in X-axis      27
Figure 1(b). 1-F(x) for empirical distribution of a sample27
Figure 1(c). P-P plot for liquidity loss data             27
Figure 1(d). Q-Q plot for liquidity loss data             27
Figure 2(a). The lognormalpdfplot of liquidity loss amount29
Figure 2(b). The exponential pdf plot of liquidity loss amount                                                    29
Figure 2(c). The Gamma pdf plot of liquidity loss amount  29
Figure 2(d). The GPD pdf plot of liquidity loss amount    29
Figure 3. The mean excess function (MEF) of liquidity loss amount                                                    30
Figure 4. Hill plot, the mean excess function of liquidity loss amount                                               30
Figure 5. The bootstrap estimate of parameter (threshold=13,050)                                        32
Figure 6(a). The scatter plot of original data and copula for E1-E2                                                 37
Figure 6(b). The scatter plot of original data and copula for E1-E6                                                 37
Figure 6(c). The scatter plot of original data and copula for E1-E7                                                 38
Figure 6(d). The scatter plot of original data and copula for E2-E6                                                 38
Figure 6(e). The scatter plot of original data and copula for E2-E7                                                 39
Figure 6(f). The scatter plot of original data and copula for E6-E7                                                 39
Figure 7. The box plots of event-pairs                    40
Figure 8. The empirical distribution of operational losses41
Figure 9(a). The exponential pdf plot of loss amount      43
Figure 9(b). The gamma pdf plot of loss amount            43
Figure 9(c). The weibull pdf plot of loss amount          43
Figure 9(d). The GPD pdf plot of loss amount              43
Figure 10(a). The exponential CDF plot of loss amount     44
Figure 10(b). The gamma CDF plot of loss amount           44
Figure 10(c). The weibull CDF plot of loss amount         44
Figure 10(d). The GPD CDF plot of loss amount             45
Figure 11. The mean excess function of loss amount        46
Figure 12. The hill plot of loss amount                   46
Figure 13. The QQ-plot and PP-plot of loss amount to exponential distribution                                  46
Figure 14. Box plot to identify outliers for all loss amounts                                                   47
Figure 15. Histogram of bootstrap for parameter ξ and σ at different thresholds                                      49
Figure 16. The return of EUD and RMB exchange rate        51
Figure 17. Time varying normal copula Kendall’s tau EUD and RMB exchange rate                                     54
Figure 18. The dynamic dependence of panel A, panel B and panel C                                                   54
Figure 19. The scatter plot of two series                 55

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