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Estimating the Structural Credit Risk Model When Equity Prices Are Contaminated by Trading Noises

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Abstract
The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan (1994) is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. We devise a particle filtering algorithm that is practical for conducting the MLE estimation of the structural credit risk model of Merton (1974). We implement the method on the Dow Jones 30 firms and on 100 randomly selected firms, and find that ignoring trading noises can lead to significantly over-estimating the firm’s asset volatility. The estimated magnitude of trading noise is in line with the direction that a firm’s liquidity will predict based on three common liquidity proxies. A simulation study is then conducted to ascertain the performance of the estimation method.

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  • Duan, Jin-Chuan & Fulop, Andras, 2006. "Estimating the Structural Credit Risk Model When Equity Prices Are Contaminated by Trading Noises," ESSEC Working Papers DR 06015, ESSEC Research Center, ESSEC Business School.
    • Handle: RePEc:ebg:essewp:dr-06015
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    Cited by:

    1. Alina Sima (Grigore) & Alin Sima, 2011. "Distance to Default Estimates for Romanian Listed Companies," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 3(2), pages 091-106, December.

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    More about this item

    Keywords

    Credit Risk; Maximum Likelihood; Microstructure; Option Pricing; Particle Filtering;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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