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系統識別號 U0002-2505201215295700
中文論文名稱 流動性、作業及主權三種風險衡量之實證研究
英文論文名稱 The Empirical Research of Measurement on Liquidity Risk, Operational Risk and Sovereign Risk
校院名稱 淡江大學
系所名稱(中) 財務金融學系博士班
系所名稱(英) Department of Banking and Finance
學年度 100
學期 2
出版年 101
研究生中文姓名 方鏘傑
研究生英文姓名 Chiang-Jye Fang
學號 897530035
學位類別 博士
語文別 英文
口試日期 2012-05-05
論文頁數 68頁
口試委員 指導教授-李沃牆
委員-陳光華
委員-黃明達
委員-洪明欽
委員-王志誠
委員-黃啟瑞
委員-黃昱程
中文關鍵字 流動性風險  作業風險  主權風險  政府干預  外溢效果  大額支付系統  風險值  期望損失  利率平價說 
英文關鍵字 Liquidity risk  Operational risk  Sovereign risk  Official intervention  GARCH  Copula function  Spillover effect  GPD  LVPS  Value at risk  Expected shortfall  Interest rate parity  Copula- ARMAX-GJR-GARCH model 
學科別分類 學科別社會科學商學
中文摘要 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。
英文摘要 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.
論文目次 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

參考文獻 References
Allen, L., and T. G. Bali, (2007), “Cyclicality in Catastrophic and Operational Risk Measurements.” Journal of Banking and Finance, 31, pp. 1191–1235.
Allen, L., and A. Saunders, (2004), “Incorporating Systemic Influences into Risk Measurements’" A Survey of the Literature.” Journal of Financial Services Research, 26, pp. 161–191.
Akaike, H. (1974), “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control, 19(6), pp. 716-723.
Angelini P., and C. Giannini, (1993), “On the Economics of Interbank Payment Systems.” Banca d’Italia, Temidi Discoussione, No. 193.
Balassa, B. (1964), “The Purchasing Power Parity Doctrine: A Reappraisal.” Journal of Political Economy, pp. 584-596.
Balkema, A. A., and L. de Haan, (1974), “Residual Life Time at Great Age.” Annals of Probability, 2, pp. 792–804.
Basel Committee on Banking Supervision (2006), “Internal Convergence of Capital Measurement and Capital Standards: A Revised Framework – Comprehensive version.” Bank for International Settlements.
Basel Committee on Banking Supervision (2010), “International Framework for Liquidity Risk Measurement, Standards and Monitoring.” Bank for International Settlements, pp. 1-47.
Beine, M., A. Benassy-Quere, and C. Lecourt, (2002), “Central Bank Intervention and Foreign Exchange Rates: New Evidence from FIGARCH Estimations,” Journal of International Money and Finance, 21, pp. 115–144.
Bassi, F., P. Embrechts, and M. Kafetzaki, (1998), “Risk Management and Quantile Estimation.” Practical Guide to Heavy Tails, Adler, R. J., Feldman, F., and Taqqu, M. (Eds). Birkhauser, Boston, pp. 111–130.
Beirlant, J., and J. L. Teugels, (1992), “Modeling Large Claims in Non-life Insurance.” Insurance: Mathematics and Economics, 11, pp. 17–29.
Beirlant, J., P. Vynckier, and J. Teugels, (1996), “Excess Function and Estimation of the Extreme Values Index.” Bernoulli, 2(4), pp. 293–318.
Cebrian, A. C., M. Denuit, and P. Lambert, (2003), “Generalized Pareto Fit to the Society of Actuaries’ Large Claims Database.” North American Actuarial Journal, 7(3), pp. 18–26.
Ceske, R., and J. Hernandez, (1999), “Where Theory Meets Practice.” Operational risk special report. Risk Magazine, (November), pp. 17–20.
Chaudhury, M. (2009), “Issues in Operational Risk Capital Modeling.” Working Paper, Desautels Faculty of Management, McGill University.
Chava, S., C. Stefanescu, and S. Turnbull, (2008), “Modeling the Loss Distribution.” Working Paper, London Business School. URL: http://faculty.london.edu/cstefanescu/Chava_Stefanescu_Turnbull.pdf.
Chiou, S. C., and R. S. Tsay, (2008), “A Copula-based Approach to Option Pricing and Risk Assessment,” Journal of Data Science, 6, pp. 273-301.
Chow, G. C. (1960), “Tests of Equality between Sets of Coefficients in two Linear Regressions,” Econometrica, 28(3), pp. 591-605.
Davidson, A. C., and R. L. Smith, (1990), “Models for Exceedances over High Thresholds.” Journal of the Royal Statistical Society, B 52, pp. 393–442.
Dornbusch, R. (2004), “Monetary Policy under Exchange Rate Flexibility”, Massachusetts Institute of Technology (MIT), Working Paper.
Embrechts, P., C. Kluppelberg, and T. Mikosch, (1997), Modeling Extreme Events for Insurance and Finance, Springer, Berlin.
Embrechts, P., S. I. Resnick, and G. Samorodnitsky, (1999), “Extreme Value Theory as a Risk Management Tool.” North American Actuarial Journal 3(2), pp. 30–41.
Fleming, J. M. (1962), “Domestic Financial Policies under Fixed and Floating Exchange Rates,” IMF Staff papers, 9, pp. 369-379.
Gamal, M. E., H. Inanoglu, and M. Stengel, (2006), “Multivariate Estimation for Operational Risk with Judicious Use of Extreme Value Theory.” Working Paper, Office of the Comptroller of the Currency, US Department of the Treasury. URL: http://www.occ.treas.gov/ftp/workpaper/wp2006-3.pdf.
Gourier, E., W. Farkas, and D. Abbate, (2009), “Operational Risk Quantification Using Extreme Value Theory and Copulas: from Theory to Practice.” The Journal of Operational Risk, 4(3), pp. 3–26.
Grilli, V., and N. Roubini, (1992), “Liquidity and Exchange Rates,” Journal of International Economics, 32, pp. 339-352.
Guegan, D., and B. K. Hassani, (2009), “A Modified Panjer Algorithm for Operational Risk Capital Calculations.” The Journal of Operational Risk, 4(4), pp. 1–20.
Herwartz, H., and H. E. Reimers, (2002), “Empirical Modeling of the DEM/USD and DEM/JPY Foreign Exchange Rate: Structural Shifts in GARCH-models and Their Implications,” Allied Stochastic Models in Business and Industry, 18, pp. 3–22.
Hill, B. M. (1975), “A Simple General Approach to Inference about the Tail of a Distribution.” Annals of Statistics, 46, pp. 1163–1173.
Hoffmann, M., and R. MacDonald, (2009), “Real Exchange Rates and Real Interest Rate Differentials.” European Economic Review, 53(8), pp. 952-970
Hogg, R., and S. Klugman, (1984), Loss Distributions. Wiley, New York.
Hsing, Y. (2007), “Exchange Rate Fluctuations in Croatia: Test of Uncovered Interest Rate Parity and the Open Economy Model,” Applied Economics Letters, 14, pp. 785-788.
Hsu, C. C., C. P. Tseng, and Y. H. Wang, (2008), “Dynamic Hedging with Futures: a Copula-based GARCH Model,” Journal of Futures Markets, 28, pp. 1095-1116.
Hu, L. (2006), “Dependence Patterns across Financial Markets: A Mixed Copula Approach,” Applied Financial Economics, 16(10), pp. 717-729
Hull, J. C. (2010). Risk Management and Financial Institutions. Pearson, 2nd edn..
Humala, A., and G. Rodr’ıguez, (2009), “Foreign Exchange Intervention and Exchange Rate Volatility in Peru,” Central Reserve Bank of Peru Working Papers, No.2009-008.
Lee,Wo-Chiang and Chiang-Jye Fang., (2010), “The Measurement of Capital for Operational Risk of Taiwanese Commercial Banks.”The Journal of Operational Risk, 5(2), pp. 79-102.
Joe, H. (1997), “Multivariate Models and Dependence Concepts.” Book, Chapman & Hall, London.
Junker, M., A. Szimayer, and N. Wagner, (2006), “Nonlinear Term Structure Dependence: Copula Functions, Empirics, and Risk Implications.” Journal of Banking and Finance, 30, pp. 1171-1199.
Kim, S. J., and J. Sheen, (2006), “Interventions in the Yen-Dollar Spot Market: A Story of Price, Volatility and Volume.” Journal of Banking and Finance, 30, pp.3191–3214.
Kohler, M. (2010), “Exchange Rates during Financial Crises,” BIS Quarterly Reviews, March 2010, pp. 39-50.
Manner, H., and O. Reznikova, (2009), “Time-varying Copulas: a Survey”, Universite catholique de Louvain, Institut de statistique.
Martin, A. (2004), “Optimal Pricing of Intraday Liquidity.” Journal of Monetary Economics, 51, pp. 401-424.
Martin A., and J. McAndrews., (2010), “A Study of Competing Designs for a Liquidity-saving Mechanism.” Journal of Banking and Finance, 34, pp. 1818-1826.
McCauley, R., and P. McGuire, (2009), “Dollar Appreciation in 2008: Safe Haven, Carry Trades, Dollar Shortage and Overhedging.” BIS Quartley Review, December, pp. 85-93.
McNeil, A. J. (1997), “Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory.” ASTIN Bulletin 27(1), pp. 117–137.
McNeil, A. J., and T. Saladin, (1997), “The Peaks over Thresholds Method for Estimating High Quantiles of Loss Distributions.” Proceedings of 28th International ASTIN Colloquium.
Melvin, M., and M. P. Taylor, (2009), “The Crisis in Foreign Exchange Market.” CEPR Discussion Papers, no 7472, September 2009.
Miller, D. (2006), “Alternative Central Bank Credit Policies for Liquidity Provision in a Model of Payments.” Journal of Monetary Economics, 53, pp. 1593-1611.
Moscadelli, M. (2004), “The Modeling of Operational Risk: Experience with the Analysis of the data collected by the Basel Committee.” Working Paper, Economic Research Department, Bank of Italy.
Mundell, R. A. (1963), “Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rates.” Canadian Journal of Economics and Political Science, 29, pp. 475-485.
Mussa, M. (1984), “The Theory of Exchange Rate Determination, in Exchange Rate Theory and Practice.” NBER Conference Report (eds) J.F.O. Bilson and R.C. Marston, Chicago University Press.
Neely, C. J. (2006), “Central Bank Authorities’ Beliefs about Foreign Exchange Intervention.” Federal Reserve Bank of St. Louis, working Paper No.2006-045c.
Neftci, S. N. (2000), “Value at Risk Calculations, Extreme Events and Tail Estimation.” Journal of Derivatives, 7(3), pp. 23–38.
Nelsen, R. B. (1999), Introduction to Copulas. Springer Verlag, New York.
Nešlehova, J., P. Embrechts, and V. Chavez-Demoulin, (2006), “Infinite-mean Models and the LDA for Opperational risk.” The Journal of Operational Risk, 1(1), pp. 3–25.
Obstfeld, M., and K. Rogoff, (1996), “Foundations of International Macroeconomics.” MIT Press.
Palaro, H. P., and L. K. Hotta, (2006), “Using Conditional Copula to Estimate Value at Risk.” Journal of Data Science, 4, pp. 93-115.
Patrick, D. F., J. S. Jordan, and E. S. Rosengren, (2004), “Implications of Alternative Operational Risk Modeling Techniques.” Working Paper, No. 11103, National Bureau of Economic Research, February.
Pickands, J. (1975), “Statistical Inference Using Extreme Order Statistics.” Annals of Statistics, 3, pp. 119–131.
Rodriguez, J. C. (2007), “Measuring Financial Contagion: A Copula Approach.” Journal of Empirical Finance, 14(3), pp. 401-423.
Rootzen, H., and N. Tajvidi, (2000), “Extreme Value Statistics and Wind Storm Losses: A Case Study.”, P.(ed). Extremes and Integrated Risk Management, Embrechts, Risk Books, London.
Schwarz, G.. (1978), “Estimating the Dimension of a Model.” Annals of Statistics, 6, pp. 461-464.
Sklar, A. (1959), “Fonctions de Repartition a n Dimensions et leurs Marges.” Publications e l’Institut de Statistique de l’Universite de Paris, 8, pp. 229–231.
Stephens, M. A. (1974), “EDF Statistics for Goodness of Fit and Some Comparisons.” Journal of the American Statistical Association, 69, pp. 730–737.
Suardi, S. (2008), “Central Bank Intervention, Threshold Effects and Asymmetric Volatility: Evidence from the Japanese Yen-US Dollar Foreign Exchange Market.” Economic Modeling, 25, pp. 628-642.
Suliman, O. (2005), “Interest Rate Volatility, Exchange Rates and External contagion.” Applied Financial Economics, 15, pp. 883-94.
Thomas, L. B. (2006), Money, Banking and Financeial Markets. Thomson South-Western.
Tsui, A. K., and K. Y. Ho, (2004), “Conditional Heteroscedasicity of Exchange Rates: Further Results Based on the Fractionally Integrated Approach.” Journal of Applied Econometrics, 19, pp. 337–642.
Tyson-Quah, Kathleen (2004), “System for Reducing Payments Risk, Liquidity Risk and Systemic Risk.” Corperate Cashflow Magazine, 13(10), pp. 28-40.
Wee, G., and S. L. Ying, (2000), “Exchange Rate and Interest Rate Differential: the Case of the Malaysian Ringgit/US Dallar.” Applied Economics Letters, 7, pp. 95-97.
Zajdenweber, D. (1996), “Extreme Values in Business Interruption Insurance.” The Journal of Risk and Insurance, 63(1), pp. 95–110.
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