Structural Credit Risk Model with Stochastic Volatility: A Particle-filter Approach

Di Bu and Yin Liao ()

No 98, NCER Working Paper Series from National Centre for Econometric Research

Abstract: This paper extends Merton's structural credit risk model to account for the fact that the firm's asset volatility follows a stochastic process. With the presence of stochastic volatility, the transformed-data maximum likelihood estimation (MLE) method of Duan (1994, 2000) can no longer be applied to estimate the model. We devise a particle filtering algorithm to solve this problem. This algorithm is based on the general non-linear and non-Gaussian filtering with sequential parameter learning, and a simulation study is conducted to ascertain its finite sample performance. Meanwhile, we implement this model on the real data of companies in Dow Jones industrial average and find that incorporating stochastic volatility into the structural model can largely improve the model performance.

Keywords: Credit risk, Merton model, Stochastic volatility, Particle Filtter; Default probability, CDS (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2013-10-28
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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