政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/76165

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/76165

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题名:	市場風險值模型與應用 Market Risk Value-at-Risk Models and Applications
作者:	廖偉成 Liao, Wei Cheng
贡献者:	謝明華陳春龍 Hsieh, Ming Hua Chen, Chung Lung 廖偉成 Liao, Wei Cheng
关键词:	巴塞爾協定市場風險風險值調查系統實務 The Basel Accord Market Risk Value-at-Risk Survey System Practices
日期:	2014
上传时间:	2015-07-01 14:44:07 (UTC+8)
摘要:	銀行的存續有賴於能正確的評估有利的交易，以及能在經濟環境逆勢的時候仍然能夠有效的經營獲利。資本市場中的企業信用評級，影響著股票和債券的的價值，同時唯有完善的風險管理機制和資本，信評機構才可以正確的評價信用。金融產品的市場價值決定了預期損益。在市價衡量法的基礎之上，銀行可以決定是否要持有該部位或是使用該部位建立一個避險的投資組合。也因此，銀行面臨了許多抉擇，包括怎麼轉換市場風險到不同的資本市場，以及有關市場風險的所有決策。基於以上的原因，銀行也已經被要求需要回應巴塞爾協定的要求，必須揭露相關的風險測度予金融市場的監督機構。在1993年，G30建議銀行可以使用風險值系統來衡量風險。依據1996年的BaselⅡ，銀行則被要求使用內部模型法來測量資本充足率。然而，計算風險值包括許多工作，例如選擇合適的風險因子、產生零息曲線、金融產品的評價、敏感度分析、損失分配的估計、投資組合管理以及風險報告等。在過去幾年，更因為避險、套利的目的，銀行累積了巨大的投資在衍生性商品商場，也使得風險管理更加的困難。在2008年的金融風暴之後，BaselⅢ指出，金融機構必須強化其交易簿內信用衍生性商品的風險管理，並同時揭露壓力風險值。綜合以上原因，銀行通常會建置風險管理系統來滿足這所有的需求和報告。也因為這些工作的複雜性，銀行一般會採用系統供應商的解決方案來實施一個市場風險管理系統。此論文從市場風險管理的歷史發展角度，完整回顧風險值理論及實務應用的相關文獻，涵蓋parametric及non-parametric 風險值模型。同時，對於市場風險管理系統以及實務建置的流程也有完整的介紹和探討，著重在趨勢、方法論及系統實務理論應用上。 The existence of a bank involves evaluating the advantages of potential trade and with the bank’s ability to survive under adverse economic cycles, which causes market pressure. The credit rating of corporations in the market affects the market value of shares and bonds, and the rating agency requires high-risk management standards and the capitalization of the corporation to assess the proper credit rating. The market price of a financial product determines the expected profit and loss for a bank. Based on the market price, a bank may make a decision to hold the position for a while or to build a well-diversified portfolio for hedging purposes. Banks therefore face the challenges of having many choices that they can transfer their market risk into different capital markets, and all decisions are associated with the market risk. For these reasons, the bank has been responded to disclose the risk metrics that have been set by the financial system supervisor. In 1993, G30 advised that banks should evaluate the financial risk of derivatives financial instruments by the Value-at-Risk (VaR) system. According to Basel Ⅱ in 1996, banks were required to have an internal model to measure sufficient capital using VaR. However, the calculation of VaR involves many tasks, such as the selection of a large number of risk factors, the methodologies of generating zero curves, the valuation of financial instruments, sensitivity parameters, loss distribution estimations, portfolio management and risk management reports for compliance purposes. In recent years, because of hedging, arbitrage and speculation purposes, banks leverage a huge sum of money in the derivatives market and make the difficult for the risk management. After the 2008 global financial crisis, BaselⅢ was introduced which asked for financial institutions to strengthen credit derivatives in trading books and disclose the stressed VaR etc. It is common that a bank has set up a risk management system to fulfill the requirements of the regulatory compliance, governance and reporting. Usually, banks adopt the provider’s solution for the implementation of a market risk management system. This dissertation surveys the literature on VaR theory and practices from a historical perspective for market risk. An overall survey of parametric and non-parametric VaR models is provided. The market risk management system and its implementation practices were also surveyed. Emphasis is placed on recent trends and developments in methodologies and system practices.
參考文獻:	Acerbi, C., Tasche, D. “On the Coherence of Expected Shortfall.” Journal of Banking and Finance, 26 (2002), pp. 1487-1503. Alexander, C. Market Risk Analysis, value-at-risk Models: Wiley 2009. Artzner, P., F. Delbaen, J. M. Eber, D. Heath. “Think Coherently.” Risk, 10 (1997), pp. 68-71. Artzner, P., F. Delbaen, J. M. Eber, D. Heath. “Coherent Measure of Risk.” Mathematical Finance, 9 (1999), pp. 203-228. Asmussen, S., Glynn, P. Stochastic Simulation: Algorithms and Analysis: Springer 2007. Barone-Adesi, G. Bourgoin, F. and Giannopoulos, K., `Don`t look back`, Risk, Vol. 11, August 1998, pp. 100-104. Barone-Adesi, G., Giannopoulos, K. and Vosper. L., “VaR without correlations for non-linear portfolios.” Journal of Futures Markets, Vol. 19, August 1999, pp. 583-602. Basel Committee on Banking Supervision “International convergence of capital measurement and capital standards.” 1988. Bank for International Settlements, Basel. Basel Committee on Banking Supervision “Amendment to the capital accord to incorporate market risks.” 1996. Available from http://www.bis.org (accessed August 2008). Basel Committee on Banking Supervision “Supervisory Framework for the Use of ‘Backtesting’ in Conjunction with the Internal Models Approach for Market Risk.” 1996. Bank for International Settlements, Basel, Switzerland. Basel Committee on Banking Supervision “Amendment to the capital accord to incorporate market risks (Updated).” 2005. Available from http://www.bis.org (accessed August 2008). Basel Committee on Banking Supervision “International convergence of capital measurement and capital standards: A revised framework (Comprehensive version).” 2006. Available from http://www.bis.org (accessed August 2008). Basel Committee on Banking Supervision “Guidelines for computing capital for incremental risk in the trading book.” 2008. Available from http://www.bis.org (accessed August 2008). Basel Committee on Banking Supervision. “Revisions to the Basel II market risk framework.” 2011. Available at:http://www.bis.org (accessed November 2013). Basel Committee on Banking Supervision, Basel III: A global regulatory framework for more resilient banks and banking systems, 2011, access in 2014. http://www.bis.org/publ/bcbs189.pdf. Basel Committee on Banking Supervision “Fundamental review of the trading book.” 2012. Basel Committee on Banking Supervision, Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools, January 2013. Beder, T. S. VaR: Seductive but dangerous. Financial Analysts Journal, (1995), pp. 12-24. Boudoukh, J., Richardson, M., Whitelaw, R. “The best of both worlds.” Risk Magazine, Vol. 11, No. 4(1998), pp. 64-67. Britten-Jones, M., Schaefer, S.M. “Non-linear Value-at-Risk.” European Finance Re- view 2, (1999), pp. 161-187. Cardenas, J, Fruchard, E., Picron, J.-F., Reyes, C., Walters, K., Yang, W. Monte Carlo Within a Day Risk 12, No. 2 (Feb.)(1999) , pp. 55-59. Chen, Z., Glasserman, P. “Fast Pricing of Basket Default Swaps.” Operations Research, 56 (2008), pp. 286-303. Chiang, M.-H., Yueh, M.-L., Hsieh, M.-H. “An efficient algorithm for basket default swap valuation.” Journal of Derivatives, 15, 2 (2007), pp. 8-19. Cumming, C. M.,Hirthe, B. J. “The Challenges of Risk Management in Diversified Financial Companies.” Economic Policy Review, Federal Reserve Bank of New York, 7 (2001), pp.1-17. Duffie, D., Pan, J. “An overview of value at risk.” The Journal of derivatives, 4(3) (1997), pp. 7-49. Duffie, D., Pan, J. Analytical Value-at-Risk with Jumps and Credit Risk, working paper, Graduate School of Business, Stanford University. 1999. Gibson, Michael, “Information Systems for Risk Management.” In The measurement of aggregate market risk. Basle: Bank for International Settlements, 1997. Glasserman, P., Heidelberger, P., Shahabuddin, P. “Variance reduction techniques for estimating value-at-risk.” Management Science, 46 (2000), pp. 1349-1364. Glasserman, P., Heidelberger, P., Shahabuddin, P. “Portfolio Value-At-Risk with Heavy-Tailed Risk Factors.” Mathematical Finance, 12 (2002), pp. 239-269. Glasserman, P. Monte Carlo Methods in Financial Engineering. Springer Verlag, New York, 2004. Glynn, P., Iglehart, D. L. “Importance sampling for stochastic simulations.” Management Science, 35 (1989), pp. 1367-1392. Hammersley, J., Handscomb, D. Monte Carlo Methods 1964. Heidelberger, P. “Fast simulation of rare events in queueing and reliability models.” ACM Trans. Model. Comput. Simul., 5, 1 (1995), pp. 43-85. Hoogerheide, Lennart, and Herman K. van Dijk. “Bayesian forecasting of value at risk and expected shortfall using adaptive importance sampling.” International Journal of Forecasting, 26, 2 (2010), pp. 231-247. Hsieh, M.-H., Liao, W.-C. ,Chen, C.-L. “A fast Monte Carlo algorithm for estimating value at risk and expected Shortfall.” Journal of Derivatives, Vol.22 (2014), pp.50-66. Huisman, R., Koedijk, K., Kool, C., Palm, F. The Fat-Tailedness of FX Returns, work- ing paper, Limburg Institute of Financial Economics, Maastricht University, The Netherlands. Available at www.ssrn.com., 1998. Hull, J., White, A. “Value at Risk when daily changes are not normally distributed.”Journal of Derivatives, 5 (1998), pp. 9-19. Hull, J. “Options, Futures and other Derivatives.” 8th Edition. Prentice Hall 2011 Hull, J., Risk Management and Financial Institutions, 3rd Ed. Wiley Finance 2012. Hull, J. Risk Management and Financial Institutions, 3rd Ed John Wiley & Sons, 2012. Jamshidian, F., Zhu, Y. “Scenario Simulation Model: Theory and Methodology.” Finance and Stochastics 1 (1997): 43–67. Jorion, P. Value at Risk: The new benchmark for controlling market risk; New York, McGraw-Hill, 1996. Jorion, P. Value at Risk, 3rd Ed.: The New Benchmark for Managing Financial Risk, New York: McGraw-Hill 2007. Joshi, M., Kainth ,D. “Rapid and accurate development of prices and Greeks for nth to default credit swaps in the Li model.” Quantitative Finance, 4, 3 (2004), pp. 266-275. Judd, K.L., Numerical methods in economics, Cambridge, Massachussets: MIT Press, 1998. Lelyveld, I. van and A. Schilder, “Risk in Financial Conglomerates: Management and Supervision,” Paper to be presented at the Joint US Netherlands Roundtable on Financial Services Conglomerates, Washington D.C., Oct 24-25, 2002. Linsmeier, T. J., Pearson, N. D. Value at risk. Financial Analysts Journal, (2000), pp. 47-67. Liao, W.-C., Hsieh, M.-H., Chen, C.-L. “Market Risk Management Information System Implementation Methodology- A Case Study For Bank of Taiwan.” Information Management Doctoral Consortium (2012). Linsmeier, T. J., Pearson, N. D. Value at risk. Financial Analysts Journal, (2000), pp. 47-67. Matten, C., Managing Bank Capital: Capital Allocation and Performance Measurement, John Wiley & Sons, New York, 2000. Morgan, J. P., RiskMetrics Technical Document, 1996. Owen, A., Tavella, D. Scrambled Nets for Value-at-Risk Calculations, 255-263, in Monte Carlo: Methodologies and Applications for Pricing and Risk Management, B. Dupire, ed., Risk Publications, London, 1999. Picoult, E. Calculating Value-at-Risk with Monte Carlo Simulation, 209-229, in Monte Carlo: Methodologies and Applications for Pricing and Risk Management, B. Dupire, ed., Risk Publications, London, 1999. Rouvinez, C. “Going Greek with VAR.” Risk, 10, 2 (1997), pp. 57-65. Saita, F. Value at Risk and Bank Capital Management: Risk Adjusted Performances, Capital Management and Capital Allocation Decision Making, Academic Press 2010. Shaw, J. Beyond VAR and Stress Testing, 231-244, in Monte Carlo: Methodologies and Applications for Pricing and Risk Management, B. Dupire, ed., Risk Publications, London, 1999. Wilmott,P., Top Derivaties Expert Estimates, http://www.dailyfinance.com/2010/06/09/risk-quadrillion-derivatives-market-gdp/, 2010. Wilson, T. Value at Risk-Risk management and Analysis,Vol.1: Measuring and Modeling Financial Risk, John Wiley &Sons, 1998. Yueh, M.-L., Wong, M. C. W. “Analytical VaR and Expected Shortfall for Quadratic Portfolios.” Journal of Derivatives, 2010, 1 (2010), pp. 1-12. Zangari, P. “Market Risk Methodology.” Risk MetricsTM – Technical Document, 4th edition (1996), pp. 105-148. Zangari, P. “How accurate is the Delta Gamma Methodology?.” Risk MetricsTM – Monitor, 3nd quarter(1996), pp. 12-29.
描述:	博士國立政治大學資訊管理研究所 97356505 103
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0973565053
数据类型:	thesis
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