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Quasi-Maximum Likelihood for Estimating Structural Models.

Malek Ben-Abdellatif1, Hatem Ben-Ameur2, Rim Chérif3

  • 1Department of Finance, School of Business, ESLSCA University, Giza 12511, Egypt.

Studies in Nonlinear Dynamics and Econometrics
|August 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an extended structural model for estimating unobservable firm asset values. The novel approach uses dynamic programming and quasi-maximum likelihood estimation for accurate parameter estimation and asset value extraction.

Keywords:
credit-spread puzzleestimationjump-diffusion processesquasi-maximum likelihoodstructural model

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Area of Science:

  • Quantitative Finance
  • Financial Econometrics
  • Corporate Finance

Background:

  • Estimating structural models is challenging due to unobservable firm asset values.
  • Existing models often lack the flexibility to incorporate complex financial structures.

Purpose of the Study:

  • To develop an extended structural model for estimating firm asset values.
  • To accommodate diverse financial features like multiple asset classes and debt structures.
  • To provide a flexible and effective estimation method.

Main Methods:

  • Derivation of the likelihood function using observed firm equity values.
  • Application of dynamic programming for model solution and asset value extraction.
  • Approximation and optimization of the likelihood function for quasi-maximum likelihood (QML) estimation.

Main Results:

  • Successfully extracted time series of firm asset values (pseudo-observations).
  • Achieved quasi-maximum likelihood (QML) estimates for unknown model parameters.
  • Demonstrated the flexibility and effectiveness of the QML approach.

Conclusions:

  • The proposed extended structural model and QML estimation provide a robust framework.
  • The model offers insights into the credit-spread puzzle, suggesting remedies through jumps and bankruptcy costs.