Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/108074 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
IEHAS Discussion Papers No. MT-DP - 2005/17
Verlag: 
Hungarian Academy of Sciences, Institute of Economics, Budapest
Zusammenfassung: 
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.
Schlagwörter: 
Particle filtering
maximum likelihood
option pricing
credit risk
simulation
JEL: 
C22
Dokumentart: 
Working Paper

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