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

Objective: Excessive use of debt in the capital structure of companies increases the credit risk of companies and, as a result, the probability of their bankruptcy. Given that shareholders are considered the remaining owners of the company, it can be said that how the company is financed or the composition of the capital structure of companies is effective on their expected returns and the pricing process of securities issued by the company. Accordingly, this study aims to investigate the role of the credit risk factor in asset pricing models and evaluate the explanatory power of this factor in explaining stock returns in the Iranian capital market.

Methodology: In this study, in order to achieve a comprehensive criterion for measuring credit risk, we used the Geske model, which is an advanced and developed version of the Merton model. Accordingly, we first calculated the probability of default of the total debt of companies 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 probability 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, in order 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, and 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.

Findings: The results of the spanning regression show that the credit risk factor contains unique and meaningful information that cannot be explained by other factors in asset pricing factor modelsIn addition, based on the results of time series regression tests and the performance evaluation criteria of the models, adding a credit risk factor to multi-factor asset pricing models improves the explanatory power of these models 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.