臺灣博碩士論文加值系統

目前行政院已規劃36處污水下水道BOT(Build-Operate-Transfer)案，其具有投資金額龐大，建設期間長及回收緩慢等特性，本研究試圖以某一污水下水道BOT計畫為個案，引用財務領域中已被廣泛應用的風險管理工具”風險值(Value at Risk, VaR)”，希望將BOT計畫中的各種抽象而不確定的風險，量化成一實際的數據，以補強傳統BOT計畫中對風險評估不足的地方。首先利用蒙地卡羅模擬法模擬出計畫之現金流量分配，並嚐試導入超越門檻值法(Peak-over Threshold Model，POT)中的一般柏拉圖分配(GPD)模型，希望針對極端風險下計畫本身的財務指標尾端分配情形作探討，彌補一般風險值模型僅對現金流量母體分配作為估計的不足，以更精確的對計畫的投資風險做一描述。
實證的結果顯示，在大部份的情況下個案BOT計畫是可以自償與獲利的，應屬低度風險的投資案，經模擬10000次後的NVP與SLR平均結果較原始計畫書中之單點估值來得低，顯示原始的財務評估可能過於樂觀；而風險值部份的研究結果顯示，在95%和99%的信賴水準下個案的風險值將落在-4億與-8億元左右，且在一般的臨界水準下，蒙地卡羅模擬法與GPD模型的回溯測試結果並無太明顯的差異，但在面臨愈是極端的臨界水準下，GPD模型能較為有效的描述尾部分配，的確有相對穩定的回溯測試結果。

Executive Yuan has planned 36 sewage conduit BOT projects in Taiwan. The sewage conduit BOT projects have the characters of large amount of initial investments, long construction period and slow pay back. The study tries to employ VaR, which is widely applied in the financial field, to reinforce the inefficiency of traditional BOT project for the reference of sewage conduit BOT project. This study first uses Monte Carlo simulation to simulate the project cash flow and then introduces GPD model of POT to investigate the tail index distribution of project’s financial indexes. This study expects to make up the inefficiency of general VaR model to describe precisely the investment risk of project.
Empirical results show that under most of the scenario, the case of BOT project is self-liquidated and profitable, and is considered to be with low risk. After 10000 times simulation, the result shows that the average of NPV and SLR are lower than those of original value, which implies the possible over optimistic estimate under traditional method. These results of VaR also show that the VaR is -400 million in 95% confidence interval and -800 million in 99%. It also shows that there is no significant different between the backward test results of Monte Carlo simulation and GPD 10% and 5% significance level. However, it also indicates that GPD model is able to describe tail distribution under extreme significance level.

摘要 Ⅰ
Abstract Ⅱ
誌謝 Ⅲ
目錄 Ⅳ
圖目錄 Ⅵ
表目錄 Ⅶ
第壹章 緒論 .1
第一節 研究背景與動機 .1
第二節 研究目的 .5
第三節 研究流程 .6
第貳章 文獻探討 .7
第一節 BOT專案風險 .7
第二節 風險值(VaR)與現金流量風險值(CFaR)之介紹 .9
第三節 模擬方法與風險值模型於專案評估上之研究 .12
第四節 極端值理論之介紹 .16
第參章 BOT個案介紹 .20
第一節 計畫區域範圍與現況 .21
第二節 預估工程規模與進度 .24
第肆章 研究方法 .27
第一節 研究設計 .27
第二節 蒙地卡羅模擬法 .29
第三節 超越門檻值法— 一般柏拉圖分配(GPD)模型 .33
第伍章 實證結果與分析 .38
第一節 參數估計 .38
第二節 風險變數之模擬結果 .39
第三節 財務指標之模擬結果 .42
第四節 門檻值選取與GPD參數估計 .47
第五節 風險值估計結果與穩定性測試 .55第陸章 結論與建議 .70
第一節 研究結論 .70
第二節 後續研究建議 .72
參考文獻 .74
附錄一 牛頓法求解之估計值 .78
附錄二 蒙地卡羅模擬Matlab程式 .80
附錄三 GPD參數牛頓法Matlab程式 .85
作者簡介 .86

中文部份：
吳勉賢(1999)，「蒙地卡羅模擬法在動態隨機變異模型上的應用」，中山大學財務金融研究所碩士論文。
李健銘(2000)，「目標規劃與蒙地卡羅模擬在BOT/BOO財務評估之應用」，交通大學土木工程學系碩士論文。
林宗政、林秀柑(2001)，「運用自償能力分析風險值(SLR-at-Risk)之壓力測試方法於BOT 交通建設暨其聯合開發之投資決策分析」，台灣土地金融季刊，第三十八卷，第四期，第185-199頁。
許克偉(1997)，「專案融資的各種風險與因應對策」，能力雜誌，第501期，第23-25頁。
許慶文(1997)，「BOT專案之研究」，台北商專學報，十二月刊，第113-150頁。
黃凰綺(2002)，「應用風險值於休閒產業投資風險評估之研究－以開發休閒旅館為例」，朝陽科技大學建築及都市設計研究所碩士論文。
黃瓊蓉(2002)，「高雄捷運營運期風險值之探討」，中山大學財務管理研究所碩士論文。
劉芬美(1999)，投資專案評估，華泰文化事業公司。
蔡明恩(2000)，「衡量BOT專案融資財務風險之研究-運用蒙地卡羅模擬法分析」，銘傳大學金融研究所碩士論文。
鄭茗聰(2002)，「大宗穀物期貨投資組合風險值研究— 超越門檻值法之應用」，屏東科技大學農企業管理系碩士論文。
謝金星(2002)，「大宗穀物期貨投資組合風險值研究— 結合GARCH與極端值理論模型之應用」，屏東科技大學農企業管理系碩士論文。





英文部份：
Anthony, D.S., Diekmann, J., & Pecsok, R.S. (1997) “Risk Analysis for Revenue Dependent Infrastructure Projects,” Construction Management and Economics, 15, 377-382.
Balkema, A., & Haan, L. (1974) “Residual Life Time at Great Age,” Annals of Probability, 2, 792-804.
Beder, T.S. (1995) “VAR: Seductive but Dangerous,” Financial Analysts Journal, 51, 12-24.
Bystrom, H. (2001) “Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory,” Working Paper, Dep. Of Econometrics, Lund University, 151-184.
Dowd, K. (1998) Beyond Value at Risk: The New Science of Risk Management, John Wiley and Sons Ltd., England., 58-83.
Duffie, D., & Pan, J. (1997) “An Overview of Value at Risk,” Journal of Derivatives, 4, 7-49.
Embrechts, P., Kluppelberg, C., & Milosch, T. (1997) “Modeling Extremal Events for Insurance and Finance,” Stochastic Modeling and Applied Probability, 33, Springer-Verlag, Berlin, 59-86.
Finnerty, J.D. (1996) Project Financing：Asset-Based Financial Engineering, John Wiley ＆ Sons, New York, 136-184.
Hall, P. (1990) “Using the Bootstrap to Estimate Mean Squared Error and Select Smoothing Parameter in Nonparametric Problems,” Journal of Multivariate Analysis, 32, 177-203.
Hill, B.M. (1975) “A Simple General Approach to Inference about the Tail of a Distribution,” Annals of Statistics, 35, 1163-1174.
Hull, J., & White, A. (1998) “Incorporating Volatity Updating into the Historical Simulation Method for Value at Risk,” Journal of Risk, Fall 1998, 45-63.
Hull, J.C. (1998) Introduction to Futures and Options Markets, 3th Edit., Prentice-Hall, Inc., 528-550.
Jenkinson, A.F. (1995) “The Frequency Distribution of the Annual Maximum (or Minimum) Values of Meteorological Elements,” Quarterly Journal of the Royal Meteorology Society, 87, 145-158.
Jorion, P., & Khoury, S.J. (1996) Financial Risk Management: Domestic and International Dimensions, Cambridge, MA: Blackwell Publishers, 150-178.
Jorion, P. (1997) Value at Risk: The New Benchmark for Controlling Market Risk, Irwin, Chicago, 14-16, and 194.
Jorion, P. (2000) Value at Risk, McGraw-Hill, 138-202.
Kelliher, C.F., & Mahoney, L.S. (2000) “Using Monte Carlo Simulation to Improve Long-Term Investment Decisions,” The Appraisal Journal, 68, 44-56.
Kwak, Y.H. (2002) “Analyzing Asian Infrastructure Development Privatization Market,” Journal of Construction Engineering and Management, 128, 110-116.
Linsmeier, T.J., & Neil, D.P. (2000) “Value at Risk,” Financial Analysis Journal, 56, 47-63.
Longin, F.M. (2000) “From Value at Risk to Stress Testing: the Extreme Value Approach,” Journal of Baking and Finance, 24, 1097-1130.
Lu, Y.C., Wu, S., Chen, D.H., & Lin, Y.Y. (2000) “BOT Projects in Taiwan: Financial Modeling Risk, Term Structure of Net Cash Flow, and Project at Risk Analysis,” The Journal of Project Finance, 5, 53-63.
Luerhrman, T.A. (1998) “Investment Opportunities as Real Option：Getting Started on the Number,” Harvard Business Review, 76, 51-67.
McNeil, A.J. (2000) “Extreme Value Theory for Risk Mangers,” Extremes and Integrated Risk Management, Risk Book, 3-18.
McNeil, A.J., & Frey, R. (2000) “Estimation of Tail- Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach,” Journal of Empirical Finance, 7, 271-300.
McNeil, A.J., & Saladin, T. (1997) “Estimating the Tails of Loss Severity Distribution Using Extreme Value Theory,” ASTIN Mulletin, 27, 2-17.
Mello, A.S., & Parsons, J.E. (1999) “Strategic Hedging,” Journal of Applied Corporate Finance, 12, 43-54.
Pickands, J. (1975) “Statistical Inference Using Extreme Order Statistics,” Annals of Statistics, 3, 119-131.
Reiss, R., & Thomas, M. (1997) “Statistical Analysis of Extreme Values: with Applications to Insurance, Finance, Hydrology and Other Fields,” Birkhäuser, Basel, 50-93.
Stulz, R.M. (1996) “Rethinking Risk Management,” Journal of Applied Corporate Finance, 9, 8-24.
Turner, C. (1996) VaR as an Inturdustrial Tool: VaR Understanding and Applying Value-at-Risk, Risk Publications, London, 341-344.
Willium, B.B., & Jeffrey, D.F. (1993) Real Estate Finance and Investments, 9th Edit., IRWIN, 554.
Ye, S., & Tiong, R.L.K. (2000) “NPV-at-Risk Method in Infrastructure Project Investment Evaluation,” Journal of Construction Engineering and Management, 126, 227-233.