Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Xiangdong Liu (), Jiahui Wu and Xianglong Li
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Xiangdong Liu: School of Economics, Jinan University, Guangzhou 510632, China
Jiahui Wu: School of Economics, Jinan University, Guangzhou 510632, China
Xianglong Li: School of Economics, Jinan University, Guangzhou 510632, China

Mathematics, 2023, vol. 11, issue 14, 1-19

Abstract: This paper investigates the financial default model with stochastic intensity by incomplete data. On the strength of the process-designated point process, the likelihood function of the model in the parameter estimation can be decomposed into the factor likelihood term and event likelihood term. The event likelihood term can be successfully estimated by the filtered likelihood method, and the factor likelihood term can be calculated in a standardized manner. The empirical study reveals that, under the filtered likelihood method, the first model outperforms the other in terms of parameter estimation efficiency, convergence speed, and estimation accuracy, and has a better prediction effect on the default data in China’s financial market, which can also be extended to other countries, which is of great significance in the default risk control of financial institutions.

Keywords: incomplete observable data; financial default markets; marked point process; self-exciting process; filtered likelihood method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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