Estimating the structural credit risk model when equity prices are contaminated by trading noises

Duan, Jin-Chuan and Andras Fulop

Journal of Econometrics, 2009, vol. 150, issue 2, pages 288-296

Abstract: The transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan [Duan, J.-C., 1994. Maximum likelihood estimation using price data of the derivative contract. Mathematical Finance 4, 155-167] 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 [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449-470]. 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.

Keywords: Particle; filtering; Maximum; likelihood; Option; pricing; Credit; risk; Microstructure (search for similar items in EconPapers)
Date: 2009

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6VC0 ... 687a8727cef0a9b3970f
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:eee:econom:v:150:y:2009:i:2:p:288-296

Access Statistics for this article

Journal of Econometrics is edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao, P. M. Robinson and A. Zellner

More articles in Journal of Econometrics from Elsevier
Series data maintained by Heidi Boesdal ().