Modeling Credit Risk with Partial Information

Abstract

This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a reduced form model by constructing an economy where the market sees the manager's information set plus noise. The noise makes default a surprise to the market. In contrast, we obtain a reduced form model by constructing an economy where the market sees a reduction of the manager's information set. The reduced information makes default a surprise to the market. We provide an explicit formula for the default intensity based on an Azéma martingale, and we use excursion theory of Brownian motions to price risky debt.

A variation of the Azéma martingale and drawdown options

“…One of the first papers to introduce the use of Azéma martingales in finance is Dritschel and Protter (). Further, the Azéma martingale is used to construct solutions to the Skorokhod embedding problem for the length and height of excursions (see Obłój & Yor, ), and to model the default probability of a firm with only information on whether the firm's cash balances are negative or not (see Cetin, Jarrow, & Protter, ). Filtered Azéma martingales, which are obtained by projecting the Brownian motion onto the signs of another observation process, were studied in Cetin ().…”

Credit Risk Modeling With Misreporting and Incomplete Information

“…Several works have appeared after this seminal paper, all focusing on how the available information set impacts the term structure of credit spreads. Cetin et al [7] and Guo et al [18] propose an approach in which the market is assumed to only partially observe, and possibly with a lag, relevant information concerning the state of the firm. Brody et al [4], and Brody et al [5] derive bond pricing formulas in an economic model where information about the actual cash flows of the debt obligation are obscured to market participants by a gaussian noise process which vanishes when the time of each required cash flow is reached.…”

A hybrid information approach to predict corporate credit risk

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This study proposes a hybrid information approach to predict corporate credit risk. The imperfect knowledge of the market is because of the fact that accounting reports and/or management press releases either purposefully or inadvertently add extraneous information that obscures the knowledge of the firm's asset value (Cetin, Jarrow, Protter, & Yildirim, 2004), leading to an inaccessible default time.Given the distinct information foundation of these models, we propose combining the aforementioned four credit risk model representatives to construct a hybrid information-based forecast for corporate credit spread. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time.

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