Hunting the Living Dead A “Peso Problem” in Corporate Liabilities Data
Matteo Manera, Umberto Cherubini
Credit risk,Corporate debt,Peso problem,Maximum likelihood,Transformed data
Economy and Society
Recent literature has pointed out that information asymmetries may be the reason for the poor performance of structural credit risk models to fit corporate bond data. It is well known in fact that these models lead to a strong understatement of the credit spread terms structure, particularly on the short maturity end. Possible explanations stem from strategic debt service behavior and, as discovered more recently, the problem of accounting transparency. This raises the possibility that some of these flaws could be reconducted to a sort of "peso problem", i.e. that the market may ask for a premium in order to allow for a small probability that accounting data may actually be biased (Baglioni and Cherubini, 2005). In this paper we propose a modified version of the Duan (1994,2000) MLE approach to structural models estimation in order to allow for this "peso problem" effect. The model is estimated for the Parmalat case, one of the most famous cases of accounting opacity, using both equity and CDS data.