Credit Risk Factor Pricing in the Iranian Capital Market: A Geske Model-Based Approach

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Finance, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.

2 Associate Prof., Department of Business Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.

3 Associate Prof., Department of Accounting, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.

4 Prof., Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

Abstract

Objective
Excessive reliance on debt financing in a company’s capital structure increases its credit risk and, consequently, the likelihood of bankruptcy. Since shareholders are considered the residual claimants of the company, the financing method and capital structure composition can significantly influence expected returns and the pricing process of securities issued by the firm. Accordingly, this study aims to examine the role of the credit risk factor in asset pricing models and evaluate its explanatory power in explaining stock returns in the Iranian capital market.
 
 
Methods
In this study, to obtain a comprehensive measure of credit risk, the Geske model—an advanced extension of the Merton model—was employed. Accordingly, the probability of default for companies’ total debt was first estimated based on the Geske model using numerical algorithm techniques. After calculating the probability of default, the credit risk factor was defined based on the difference in returns between companies with high and low probabilities of default. Next, the hedging regression method was used to examine the role of the credit risk factor in explaining stock and bond returns. In the next step, the credit risk factor was added to the asset pricing factor models, and by running time series regressions on a large set of test assets, the explanatory power of the extended models with the credit risk factor was evaluated and tested in comparison with conventional asset pricing models. Finally, to examine the robustness and stability of the results and to more accurately assess the predictability of credit risk factor loadings in explaining cross-sectional excess returns, a two-stage Fama-Macbeth test was used. In the first stage of this test, time-varying factor loadings for the credit risk factor were calculated using time series regressions on asset pricing factor models. Then, in the second stage, cross-sectional regression was performed for excess returns relative to the factor loadings estimated in the first stage. Finally, the credit risk factor price was determined as the average of the estimated coefficients from the cross-sectional regression. To achieve this goal, data from companies listed on the Tehran Stock Exchange and the Iranian OTC market between 2004 and 2023, and a diverse set of test assets, including portfolios sorted based on various company characteristics, were used.
 
Results
The results of the spanning regression indicate that the credit risk factor contains unique and significant information that cannot be explained by other factors included in asset pricing models. Furthermore, the results of the time-series regression tests and model performance evaluation criteria demonstrate that incorporating the credit risk factor into multifactor asset pricing models enhances their explanatory power in explaining the returns of the test assets. Also, the results of the Fama-Macbeth test show that the time series average of the coefficients related to the credit risk factor is positive and significant, indicating a positive risk premium for this factor. These findings indicate that investors receive excess returns in exchange for accepting higher credit risk, and this factor is positively priced in the Iranian capital market.
 
Conclusion
The findings of this study show that adding credit risk factor to asset pricing models significantly increases the power of these models in explaining fluctuations in returns of financial assets and stocks and also increases their forecasting accuracy. Also, the results indicate that credit risk, as a systematic and unavoidable factor that is a function of the company's economic environment, is reflected in stock returns by taking a positive risk premium and increases the expected return of stocks.
 

Keywords

Main Subjects

References

 
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Financial Research Journal
Volume 28, Issue 2
2026
Pages 558-597

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