{"title":"Cash Flow Optimization on Synthetic CDOs","authors":"Timoth\u00e9e Bligny, Cl\u00e9ment Codron, Antoine Estruch, Nicolas Girodet, Cl\u00e9ment Ginet","volume":93,"journal":"International Journal of Economics and Management Engineering","pagesStart":2917,"pagesEnd":2922,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9999291","abstract":"
Collateralized Debt Obligations are not as widely used
\r\nnowadays as they were before 2007 Subprime crisis. Nonetheless
\r\nthere remains an enthralling challenge to optimize cash flows
\r\nassociated with synthetic CDOs. A Gaussian-based model is used
\r\nhere in which default correlation and unconditional probabilities of
\r\ndefault are highlighted. Then numerous simulations are performed
\r\nbased on this model for different scenarios in order to evaluate the
\r\nassociated cash flows given a specific number of defaults at different
\r\nperiods of time. Cash flows are not solely calculated on a single
\r\nbought or sold tranche but rather on a combination of bought and
\r\nsold tranches. With some assumptions, the simplex algorithm gives
\r\na way to find the maximum cash flow according to correlation of
\r\ndefaults and maturities. The used Gaussian model is not realistic in
\r\ncrisis situations. Besides present system does not handle buying or
\r\nselling a portion of a tranche but only the whole tranche. However the
\r\nwork provides the investor with relevant elements on how to know
\r\nwhat and when to buy and sell.<\/p>\r\n","references":"[1] A. Alfonsi, C. Labart and L Jerome, \"Stochastic Local Intensity\r\nLoss Models with Interacting Particle Systems\u201d, eprint arXiv:1302.2009\r\nMathematical Finance, pages 1\u201329, 2013.\r\n[2] J. Beumee, D. Brigo, D. Schiemert and D. Stoyle, \"Charting a Course\r\nThrough the CDS Big Bang\u201d, Global Special Report, 2009.\r\n[3] D Brigo, A. Pallavicini and R. Torresetti, \"Calibration of CDO Tranches\r\nwith the Dynamic Generalized-Poisson Loss Model\u201d, 2010.\r\n[4] R. Cont and Y. H. Kan, \"Dynamic hedging of portfolio credit derivatives\u201d,\r\n2009.\r\n[5] R. Cont and A. Minca, \"Recovering portfolio default intensities implied\r\nby CDO quotes\u201d, 2010.\r\n[6] R. Cont, R. Deguest and Y. H. Kan, \"Default intensities implied by CDO\r\nSpreads: inversion formula and model calibration\u201d, 2010.\r\n[7] A. Cousin, \"Analyse du Risque et Couverture des Tranches de CDO\r\nSynthetique\u201d, 2008.\r\n[8] X. L. David, \"On Default Correlation: A copula function approach\u201d,\r\nJournal of Fixed Income, 9, 43\u201354, 2000.\r\n[9] A. Elizalde, \"Credit Risk Models IV: Understanding and pricing CDOs\u201d,\r\n2005.\r\n[10] J. Hull and A. White, \"Valuing Credit Default Swaps I: No Counterparty\r\nDefault Risk\u201d, Journal of Derivatives, 8, 29\u201340.\r\n[11] P. Jorion, \"Financial Risk Manager Handbook\u201d, page 287, 2009.\r\n[12] R. Merton, \"On The Pricing of Corporate Debt: The Risk Structure of\r\nInterest Rates\u201d, Journal of Finance, 29, 449\u2013470, 1974.\r\n[13] D. O\u2019Kane and S. Turnbull, \"Valuation of Credit Default Swaps\u201d, Fixed\r\nIncome Quantitative Credit Research, 2003.\r\n[14] Y. Rakotondratsimba, \"Risque de credit et de contrepartie\u201d, 2013.\r\n[15] Y. Rakotondratsimba, \"Probabilites pour la Finance\u201d, 2013.\r\n[16] Y. Rakotondratsimba, \"Produits derives structures de credit :\r\nCollateralized Debt Obligations (CDOs)\u201d, 2012.\r\n[17] M. B. Walker, \"CDO Valuation: Term Structure, Tranche Structure, and\r\nLoss Distributions\u201d, page 26, 2007.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 93, 2014"}