鑑於國際金融市場衍生性金融商品交易蓬勃發展，交易商品種類日趨複雜，交易對手信用風險規模與影響範圍隨衍生性金融交易激增而快速膨脹，2007年國際金融海嘯爆發，過去被國際信評公司與金融同業認定為信評極佳大型金融機構如Bear Stearns、Lehman Brothers及AIG等相繼接連發生破產保護或要求母國政府紓困援助，相關交易對手信用風險管理更為國際金融市場參與者與監理機關重視，故本研究主旨在於瞭解一般金融機構交易對手信用風險管理模式，分析交易對手信用風險曝險值評量方法並建立一有效率且貼近金融市場變化的交易對手信用風險曝險值管理機制。

本研究先蒐集整理各類OTC衍生性金融商品風險因子報價歷史資料，採用蒙地卡羅模擬法(Monte Carlo Simulation)結合各類OTC衍生性金融產品風險因子變動機率分配，模擬推估各類OTC衍生性金融商品在交易契約期間任一時點可能發生最大潛在曝險值(Maximum Potential Future Exposure, Maximum PFE)，再依交易契約天期遠近將各類OTC衍生性金融商品未來最大潛在曝險值排序整理成可供交易單位或風險管理單位對應查詢之信用轉換係數(Credit Convention Factor ,CCF)。

本研究所模擬各類OTC衍生性金融商品信用轉換係數資料共計有1.匯率類衍生性金融商品：13組不同匯率遠期外匯交易、15組不同匯率外匯選擇權買權與外匯選擇權賣權，2.利率類衍生性金融商品：6種幣別利率交換交易與3組不同匯率換匯換利交易，3.股權類衍生性金融商品則為全球8個主要股權商品集中交易市場指數。

本研究最後舉例說明與特定交易對手承作OTC衍生性金融商品曝險值計算與風險額度管理方式，並以BaselⅡ信用轉換係數與本研究模擬出信用轉換係數於使用當前曝險法(CEM)計算特定交易對手信用風險曝險值兩者之比較。
本研究結果提供金融機構於OTC衍生性金融商品交易對手信用風險曝險值管理一快速簡易且較為準確評量機制，加上若能定期更新(每季/每半年)衍生性金融商品市場參數(market data)重新模擬各類OTC衍生性金融商品信用轉換係數，可使金融機構交易對手信用風險曝險值評量更貼近當下金融市場趨勢變化。

Due to the blooming development in the international financial derivatives market and the more complex transactions, the range of the influence from the counterparty credit risk became more extensive. In 2007~2008 the global financial crisis wreaked havoc through financial markets worldwide, and many well-known large financial institutions such as Bear Stearns, Lehman Brothers and AIG with the investment rating also have had to file for bankruptcy or asked for the bailout. And let international financial market participants and supervisory authorities gradually pay more attention on counterparty credit risk management issues. This study focuses on understanding the method of the counterparty credit risk management and analyzing the evaluation of the counterparty credit risk exposure as well as setting a more efficient mechanism to manage the counterparty credit risk.

First, we collect the historical market data of the risk factors that will influence the exposure of the OTC derivatives and make choice of models for the risk factors. We use Monte Carlo simulation method and choose an appropriate probability distribution of the risk factor to quantify the worst exposure (The worst-case gain) of the OTC derivatives (quantify maximum potential future exposure, Maximum PFE). Then, according to the maturity of the contracts, we summarize the outcome of the Maximum PFE simulation and compile the credit convention factor tables which are typically generic tables that can be used for derivatives trading purpose or risk controlling purpose.

In this study, we have simulated the Maximum PFE of several types of OTC derivatives and compiled the credit convention factor tables listed as below：
1.Foreign exchange rate derivatives：(1) FX Forward：13 different currency pairs, (2) FX Option Call / Put (at the money)：15 different currency pairs.
2.Interest rate derivatives：(1) Interest Rate Swap：6 different currency interest rate indexes, (2) Cross Currency Swap：3 different currency pairs.
3. Equity derivatives：8 major market indices.

Finally, we illustrate the add-on method for estimating the contract-level PFE and exposure using examples of the OTC derivatives contracts and demonstrate how to manage and control counterparty credit risk exposure with a specific counterparty. Then, we compare the exposure estimates under the CCF tables of the BaselⅡ clause and the compiled CCF tables of this study to the same contract of derivatives in current exposure method (CEM). The results do however give some insight into the dynamics of the two different CCF tables for calculating exposure at default (EAD).

The conclusion of the study can provide the financial institutions which trades in the OTC derivatives market with a simple approximation in an attempt to measure their exposure to counterparty credit risk more easily and efficiently. In addition, by updating the market data and simulating the PFE of the OTC derivatives frequently (quarterly or semi-yearly), the financial institutions which have adopted the mechanism can enhance their assessment of counterparty credit risk exposure much closer to the current financial market trends.

一、 中文文獻

1、王芝芳，「從財務指標建構銀行設定額度的計算模式」，國立台灣大學管理學院財務金融學系碩士論文，2004年7月。
2、陳松男，利率金融工程學：理論模型及實務應用，初版，新陸書局，2006年1月。
3、陳威光，選擇權：理論、實務與風險管理，再版，智勝文化事業有限公司，2010年9月。
4、鄭琇霙，「店頭衍生性商品交易集中結算之研究 – 以美國2010金融改革法案為中心」，國立政治大學國際經營與貿易研究所未出版之碩士論文，2011年7月。

二、 英文文獻

1、BCBS. (2005). The Application of Basel II to Trading Activities and the Treatment of Double Default Effects, Basel Committee on Banking Supervision. Available at www.bis.org
2、BCBS. (2006). International Convergence of Capital Management and Capital Standards: A Revised Framework – Comprehensive Version, Basel Committee on Banking Supervision ,June. Available at www.bis.org
3、BIS, Monetary and Economic Department, OTC derivatives market activity in the first half of 2011, Nov. 2011. Available at http://www.bis.org/publ/otc_hy1111.pdf
4、Brigo, D. & Mercurio, F. (2006), Interest Rate Models: Theory and Practice - with Smile, Inflation and Credit, Second Edition, Heidelberg: Springer Verlag.
5、De Prisco. B., and Rosen, D. (2005), “Modelling Stochastic Counterparty Credit Risk Exposure for Derivatives Portfolios,” in M. Pykhtin (Ed.), Counterparty Credit Risk Modelling, London: Risk Books.

6、Fleck, M., and Schmidt, A. (2005), ”Analysis of Basel II Treatment of Counterparty Credit Risk,” in M. Pykhtin (Ed.), Counterparty Credit Risk Modelling, London: Risk Books.
7、Gibson, M. S. (2005), ”Measuring Counterparty Credit Exposure to a Margined Counterparty,” in M. Pykhtin (Ed.), Counterparty Credit Risk Modelling, London: Risk Books.
8、Gregory, J. (2010), Counterparty Credit Risk – The New Challenge for Global Financial Markets, First Edition, West Sussex: John Wiley & Sons, Inc.
9、Hull, J. and A. White (1990), ”Pricing interest rate derivative securities,” Review of Financial Studies, 3(4), 573-592.
10、Hull, J. C. (2000), Options, Futures, and Other Derivatives, Fourth Edition, New Jersey: Prentice Hall, Inc.
11、Pykhtin, M. (2005), Counterparty Credit Risk Modelling, London: Risk Books.
12、Pykhtin, M. (2011), Counterparty Credit Risk Modeling Counterparty Credit Exposure , IAFE Workshop, Federal Reserve Board.
13、Samuel Jacques le Roux (2008), Measuring Counterparty Credit Risk: An Overview of the Theory and Practice. Pretoria: University of Pretoria.
14、Vasicek,O. A. (1977), “An Equilibrium Characterization of the Term Structure,” Journal of Financial Economics, 5, 177-88.
15、Viral V. Acharya, Robert F. Engle, Stephen Figlewski, Anthony W. Lynch, and Marti G. Subrahmanyam. (2009), “Centralized Clearing for Credit Derivatives,” in Viral V. Acharya and Matthew Richardson (Ed.), Restoring Financial Stability: How to Repair a Failed System, New Jersey: John Wiley & Sons, Inc.
16、Viral V. Acharya, Paul Wachtel, and Ingo Walter. (2009), “ International Alignment of Financial Sector Regulation,” in Viral V. Acharya and Matthew Richardson (Ed.), Restoring Financial Stability: How to Repair a Failed System, New Jersey: John Wiley & Sons, Inc.