Finance Project

In recent years, the market for collateral debt obligations (CDOs) and, in particular, the development of the synthetic CDO market and correlation trading has resulted in significant developments in valuation and risk management for such products. The market has been dominated by developments around the static Gaussian copula model, the introduction of base correlation as an alternative to the compound correlation, and extensions to better capture the observed correlation smile/skew, only recently more dynamic models that incorporate credit spreads or other major modeling parameters have been introduced by practitioners and academics.

All valuation approaches are based on risk-neutral pricing principles and little focus has been given to replication-based arguments that would also lead to developments for practical hedging and risk management. Currently, risk management often focuses on static risk measures that address the likelihood of a CDO investor receiving full notional and actual interest in a timely manner (ratings perspective), or on mark-to-market (MtM) sensitivities and “the greeks” frequently employed by correlation investors and traders.
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Although the market size of SF products such as asset-backed securities (ABS), collateralized debt obligations (CDO), residential-mortgage backed securities (RMBS), etc. has grown enormously over the past decade, only little is known about their behavior in terms of rating migration, especially default, compared to corporates.

Credit risk portfolio models generally rely on the estimation of rating migration and/or default probabilities and asset correlation between exposures.† The latter significantly affects the portfolio loss distribution and in particular the tails of the distribution. Therefore, the accuracy of these parameter estimates is of vital importance. Another way to secure your assets is by buying insurance, some insurance giving full warranty and ease of access in document and filling.
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The analysis of credit risk in a portfolio requires measures of dependency across assets. Individual spreads in the pricing world, probabilities of default (PDs) and loss-given-default in the risk universe, management world, are important but insufficient to determine the price/risk of multiname products and their entire distribution of losses.

Because the diversification effects are related to dependency, neither the price of a portfolio can be defined as a linear combination of the price of its underlying components, nor its loss distribution can be the sum of the distributions of individual losses.
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Univariate pricing is a key component to the pricing of structured credit vehicles. To date, credit is still very much an incomplete market. In addition, it is usually difficult to use a simple diffusion setup to model its dynamic, as default risk is usually perceived as an unexpected event, i.e., a jump. An incomplete market and the presence of jumps make the credit space a difficult market, where it is not always easy to derive prices from the cost of related replicating (hedging) strategies/portfolios.

Due to these characteristics, market participants have been trying hard to make the most of two alternatives:
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Acredit rating represents the agency’s opinion about the creditworthiness of an obligor, with respect to a particular debt security or other financial obligation (issue-specific credit ratings). It also applies to an issuer’s general creditworthiness (issuer credit ratings).

There are generally two types of assessment corresponding to different financial instruments: long-term and short-term ones. One should stress that ratings from various agencies do not convey the same information. S&P perceives its ratings primarily as an opinion on the likelihood of default of an issuer,* while Moody’s ratings tend to reflect the agency’s opinion on the expected loss (probability of default times loss severity) on a facility.
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