Collateralized Commodity Obligations:
Modeling and Risk Assessment

Abstract

In this article the authors address the risk characteristics and rating of Collateralized Commodity Obligations (CCO), which are recently devised structured products similar to the Collateralized Debt Obligation (CDO). Commodities as an asset class have been in the spotlight of investors’ attention for the past decade. CCOs, which are fixed income instruments, provide fixed income investors an exposure to commodity markets. The underlying assets of a CCO are Commodity Trigger Swaps (CTS); these are similar to Credit Default Swaps, but instead of a default, a “trigger” occurs when a commodity price reaches a certain pre-set level. Rating agencies have used their CDO evaluators to rate CCOs, however particular characteristics of commodity prices and an abundance of historical price data for commodities render such an approach questionable. Recently, S&P has withdrawn its ratings for CCOs, which may be linked to some concerns regarding their rating approach. The authors examine the historical performance of CCOs and propose two novel approaches to their rating. The first is a flexible multivariate parametric model for commodity prices: a mean-reversion model with correlated trends. The second approach is close in spirit to the historical simulation method for risk management and is based on the block bootstrap technique. The authors apply both approaches to an example of a CCO and compare the results to the ratings provided by the rating agencies. They find that simulated ratings are sensitive to the model assumptions; the default probabilities resulting from the agencies’ ratings underestimate both historically observed and bootstrap-simulated default probabilitie; and the non-parametric approach most closely matches the historically observed probabilities of default. The results demonstrate the benefit of a data-driven, non-parametric modeling approach to rating CCOs.

TOPICS: Commodities, CLOs, CDOs, and other structured credit, information providers/credit ratings, statistical methods