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

Jin-Chuan Duan () and Andras Fulop ()
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Jin-Chuan Duan: School of Management, University of Toronto
Andras Fulop: School of Management, University of Toronto

No 517, CERS-IE WORKING PAPERS from Institute of Economics, Centre for Economic and Regional Studies

Abstract: The transformed-data maximum likelihood estimation (MLE) method for struc- tural 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. A simulation study is then conducted to ascertain the performance of the estimation method.

Keywords: Particle filtering; maximum likelihood; option pricing; credit risk; simulation (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2005
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Citations: View citations in EconPapers (5)

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