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Credit derivatives have enjoyed explosive growth in the last decade, particularly synthetic Collateralised Debt Obligations (synthetic CDOs). This book describes the state-of-the-art in quantitative and computational modelling of CDOs. Beginning with an overview of the structured finance landscape, readers are introduced tothe basic modelling concepts necessary to model and value simple credit derivatives. The modelling, valuation and risk management of synthetic CDOs are described and a detailed picture of the behaviour of these complex instruments is built up. The final chapters introduce more advanced topics such as portfolio management of synthetic CDOs and hedging techniques. Detailing the latest models and techniques, this is essential reading for quantitative analysts, traders and risk managers working in investment banks, hedge funds and other financial institutions, and for graduates intending to enter the industry. It is also ideal for academics who need to keep informed with current best practice in the credit derivatives industry.
Mathematical Sciences --- General and Others --- Collateralized debt obligations. --- Finance. --- Funding --- Funds --- Economics --- Currency question --- CDOs (Collateralized debt obligations) --- Credit derivatives
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The author focuses on a method to price Collateralized Debt Obligations (CDO) tranches. The original method is developed by Castagna, Mercurio and Mosconi in 2012. The Thesis provides an extension of the original work by generalizing the Gaussian dependence in terms of Copula functions. In particular the model is rewritten for the specific case of the Clayton copula. The method is applied to price the tranches of a CDX. By comparing the tranches prices, it is possible to notice that the Clayton approach leads to smaller equity and mezzanine tranches. The senior and super senior tranches levels are higher when the dependence is modeled by a Clayton copula. Contents CDO: General Characteristics Credit Risk Modeling Copula Functions and Dependency Concepts Moment Matching Approximation Extensions to the Model Implementation Target Groups Researchers in the field of Finance Practitioners of Financial Institutions The Author Enrico Marcantoni obtained his Master Degree in Quantitative Finance at the University of Bologna (Italy) taking part in a Double Degree Program in collaboration with the Master in Quantitative Asset and Risk Management at the University of Applied Sciences (bfi) Vienna (Austria).
Asset-backed financing. --- Debt. --- Mortgage-backed securities -- United States. --- Management --- Commerce --- Business & Economics --- Management Theory --- Local Commerce --- Collateralized debt obligations --- Copulas (Mathematical statistics) --- Mathematical models. --- CDOs (Collateralized debt obligations) --- Business. --- Management science. --- Finance. --- Business and Management. --- Business and Management, general. --- Finance, general. --- Distribution (Probability theory) --- Credit derivatives --- Funding --- Funds --- Economics --- Currency question --- Trade --- Industrial management --- Quantitative business analysis --- Problem solving --- Operations research --- Statistical decision
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This book considers the one-factor copula model for credit portfolios that are used for pricing synthetic CDO structures as well as for risk management and measurement applications involving the generation of scenarios for the complete universe of risk factors and the inclusion of CDO structures in a portfolio context. For this objective, it is especially important to have a computationally fast model that can also be used in a scenario simulation framework. The well known Gaussian copula model is extended in various ways in order to improve its drawbacks of correlation smile and time inconsistency. Also the application of the large homogeneous cell assumption, that allows to differentiate between rating classes, makes the model convenient and powerful for practical applications. The Crash-NIG extension introduces an important regime-switching feature allowing the possibility of a market crash that is characterized by a high-correlation regime.
Asset-backed financing. --- Collateralized debt obligations. --- Credit -- Mathematical models. --- Investment analysis. --- Finance --- Business & Economics --- Investment & Speculation --- Finance - General --- Banking --- Credit --- Mathematical models. --- CDOs (Collateralized debt obligations) --- Finance. --- Applied mathematics. --- Engineering mathematics. --- Economics, Mathematical. --- Finance, general. --- Quantitative Finance. --- Applications of Mathematics. --- Credit derivatives --- Mathematics. --- Math --- Science --- Funding --- Funds --- Economics --- Currency question --- Economics, Mathematical . --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematical economics --- Econometrics --- Mathematics --- Methodology --- Social sciences --- Financial Economics. --- Mathematics in Business, Economics and Finance.
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