Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2008.12.003
Title: Estimating the structural credit risk model when equity prices are contaminated by trading noises
Authors: Duan, J.-C. 
Fulop, A.
Keywords: Credit risk
Maximum likelihood
Microstructure
Option pricing
Particle filtering
Issue Date: 2009
Source: Duan, J.-C., Fulop, A. (2009). Estimating the structural credit risk model when equity prices are contaminated by trading noises. Journal of Econometrics 150 (2) : 288-296. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jeconom.2008.12.003
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. © 2009.
Source Title: Journal of Econometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/44423
ISSN: 03044076
DOI: 10.1016/j.jeconom.2008.12.003
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