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

Jin-Chuan Duan () and Andras Fulop ()
Additional contact information
Jin-Chuan Duan: Rotman School of Management, University of Toronto, Postal: 105, rue Saint George, TORONTO, Ontario, M5S 3E6 ,, http://www.rotman.utoronto.ca/
Andras Fulop: ESSEC Business School, Postal: Avenue Bernard Hirsch - B.P. 50105, 95021 CERGY-PONTOISE Cedex , FRANCE, ,, http://www.essec.edu

No DR 06015, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School

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.

Keywords: Credit Risk; Maximum Likelihood; Microstructure; Option Pricing; Particle Filtering (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2006-10
New Economics Papers: this item is included in nep-ecm, nep-mst and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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