On the Design of Collateralized Debt Obligation-Transactions

2007 ◽  
Author(s):  
Guenter Franke ◽  
Markus Herrmann ◽  
Thomas Weber
Keyword(s):  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Debt Obligation

scholarly journals How financial products organize spatial networks: Analyzing collateralized debt obligations and collateralized loan obligations as “networked products”

2021 ◽  
pp. 0308518X2110296
Author(s):  
Jonathan Beaverstock ◽  
Adam Leaver ◽  
Daniel Tischer
Keyword(s):  
Financial Networks ◽  
Regulatory Regime ◽  
Collateralized Debt Obligations ◽  
The Us ◽  
Collateralized Debt Obligation ◽  
Role Based ◽  
Collateralized Debt ◽  
Collateralized Loan Obligation ◽  
Debt Obligation ◽  
Peak Value

During the 2010s, collateralized loan obligations rapidly became a trillion-dollar industry, mirroring the growth profile and peak value of its cousin—collateralized debt obligations—in the 2000s. Yet, despite similarities in product form and growth trajectory, surprisingly little is known about how these markets evolved spatially and relationally. This paper fills that knowledge gap by asking two questions: how did each network adapt to achieve scale at speed across different jurisdictions; and to what extent does the spatial and relational organization of today's collateralized loan obligation structuration network, mirror that of collateralized debt obligations pre-crisis? To answer those questions, we draw on the global financial networks approach, developing our own concept of the networked product to explore the agentic qualities of collateralized debt obligations and collateralized loan obligations—specifically how their technical and regulatory “needs” shape the roles and jurisdictions enrolled in a global financial network. We use social network analysis to map and analyze the evolving spatial and relational organization that nurtured this growth, drawing on data harvested from offering circulars. We find that collateralized debt obligations spread from the US to Europe through a process of transduplication—that similar role-based network relations were reproduced from one regulatory regime to another. We also find a strong correlation between pre-crisis collateralized debt obligation- and post-crisis collateralized loan obligation-global financial networks in both US$- and €-denominations, with often the same network participants involved in each. We conclude by reflecting on the prosaic way financial markets for ostensibly complex products reproduce and the capacity for network stabilities to produce market instabilities.

ON DEFAULT CORRELATION AND PRICING OF COLLATERALIZED DEBT OBLIGATION BY COPULA FUNCTIONS

2006 ◽  
Vol 05 (03) ◽  
pp. 483-493 ◽  
Author(s):  
PING LI ◽  
HOUSHENG CHEN ◽  
XIAOTIE DENG ◽  
SHUNMING ZHANG
Keyword(s):  
Credit Derivatives ◽  
Copula Function ◽  
Dependence Structure ◽  
Default Correlation ◽  
Specific Kind ◽  
Copula Functions ◽  
Recovery Rates ◽  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Debt Obligation

Default correlation is the key point for the pricing of multi-name credit derivatives. In this paper, we apply copulas to characterize the dependence structure of defaults, determine the joint default distribution, and give the price for a specific kind of multi-name credit derivative — collateralized debt obligation (CDO). We also analyze two important factors influencing the pricing of multi-name credit derivatives, recovery rates and copula function. Finally, we apply Clayton copula, in a numerical example, to simulate default times taking specific underlying recovery rates and average recovery rates, then price the tranches of a given CDO and then analyze the results.

Dynamic alpha-stable method for CDO pricing

2014 ◽  
Vol 01 (03) ◽  
pp. 1450028
Author(s):  
Hua Li ◽  
George Yuan ◽  
Weina Chen ◽  
Li Guo ◽  
Jianbin Zhao
Keyword(s):  
Risk Management ◽  
Stable Model ◽  
Stable Method ◽  
Copula Method ◽  
Static Factor ◽  
Cdo Pricing ◽  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Credit Portfolio ◽  
Debt Obligation

The goal of this paper is to develop the dynamic alpha (α)-stable method for collateralized debt obligation (CDO) pricing based on the α-stable distributions, which will resolve the two issues caused by using traditional static factor copula method in the practice, which means when pricing CDOs, the traditional static factor Copula method does not only exhibit the correlation smile phenomenon which is not inconsistent with the model's assumption, but also cannot be used in pricing CDOs or credit portfolio derivatives for the underlying portfolio with different maturities. As the applications, we present calibration and empirical numerical results for iTraxx Europe Tranches quotes from the market data on March 30, 2007. Thus, our new method under the framework of the dynamic α-stable model is the way for CDO pricing in the practice, and should be useful for the risk management in the practice too.

Investigation for Synthetic Collateralized Debt Obligation

2006 ◽  
Vol 14 (1) ◽  
pp. 127-168
Author(s):  
Mi Ae Kim
Keyword(s):  
Default Probability ◽  
Gaussian Copula ◽  
Copula Model ◽  
Pricing Models ◽  
Hedging Strategy ◽  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Non Gaussian ◽  
Asset Correlation ◽  
Debt Obligation

Recently, domestic market participants have a growing interest in synthetic Collateralized Debt Obligation (CDO) as a security to reduce credit risk and create new profit. Therefore, the valuation method and hedging strategy for synthetic CDO become an important issue. However, there is no won-denominated credit default swap transactions, which are essential for activating synthetic CDO transaction‘ In addition, there is no transparent market information for the default probability, asset correlation, and recovery rate, which are critical variables determining the price of synthetic CDO. This study first investigates the method of estimating the default probability, asset correlation coefficient, and recovery rate. Next, using five synthetiC CDO pricing models‘ widely used OFGC (One-Factor Non-Gaussian Copula) model. OFNGC (One-Factor Non-Gaussian Copula) model such as OFDTC (One-Factor Double T-distribution Copula) model of Hull and White (2004) or NIGC (Normal Inverse Gaussian Copula) model of Kalemanova et al.(2005), SC<Stochastic Correlation) model of Burtschell et al.(2005), and FL (Forward Loss) model of Bennani (2005), I Investigate and compare three points: 1) appropriateness for portfolio loss distribution, 2) explanation for standardized tranche spread, 3) sensitivity for delta-neutral hedging strategy. To compare pricing models, parameter estimation for each model is preceded by using the term structure of iTraxx Europe index spread and the tranch spreads with different maturities and exercise prices Remarkable results of this study are as follows. First, the probability for loss interval determining mezzanine tranche spread is lower in all models except SC model than OFGC model. This result shows that all mαdels except SC model in some degree solve the implied correlation smile phenomenon, where the correlation coefficient of mezzanine tranche must be lower than other tranches when OFGC model is used. Second, in explaining standardized tranche spread, NIGC model is the best among various models with respect to relative error. When OFGC model is compared with OFDTC model, OFOTC model is better than OFGC model in explaining 5-year tranche spreads. But for 7-year or 10-year tranches, OFDTC model is better with respect to absolute error while OFGC model is better with respect to relative error. Third, the sensitivity sign of senior tranctle spread with respect to asset correlation is sometime negative in NIG model while it is positive in other models. This result implies that a long position may be taken by the issuers of synthet.ic COO as a correlation delta-neutral hedging strategy when OFGC model is used, while a short position may be taken when NIGC model is used.

Financial Engineering and Portfolio Design Problem

2020 ◽  
pp. 1-28
Keyword(s):  
Financial Engineering ◽  
Financial Institution ◽  
Risky Assets ◽  
Collateralized Debt Obligation ◽  
Financial Portfolio ◽  
Collateralized Debt ◽  
Portfolio Optimization Problem ◽  
Portfolio Structure ◽  
Portfolio Design ◽  
Debt Obligation

Any financial institution is in charge of assigning to a client's portfolio a set of assets in a reliable way by minimizing the risk of loss and maximizing gain. All portfolios should share in an equitable way risky assets and safe assets with respect to a financial structured product such as CDO2 (collateralized debt obligation squared). Realizing a balanced portfolio requires a good level of diversification on the chosen assets. Many works have made proposals for modeling and solving the (OPD) problem, but each one has taken into account specific types of risks and cases. However, in this chapter, the authors introduce the problem in a general way by using the CDO2 structure. The authors focus in this chapter on the basic and useful notions of financial engineering, followed by a description of the financial portfolio structure CDO2, the most used and structured financial product. The chapter introduces the financial portfolio optimization problem through the CDO2 structure, the effect of the diversification on the efficiency of the financial portfolio.

Sign in / Sign up

Export Citation Format

Share Document