Abstract In this paper, we propose a Maximization–Maximization ( MM ) algorithm for the assessment of hidden parameters in structural credit risk models. Step M1 updates the value, volatility, and expected return on the firm’s assets by maximizing the log-likelihood function for the time series of equity prices; Step M2 updates the default barrier by maximizing the equity holders’ participation in the firm’s asset value. The main contribution of the method lies in the M2 step, which allows for ‘endogenizing’ the default barrier in light of actual data on equity prices. Using a large international sample of companies, we demonstrate that theoretical credit spreads based on the MM algorithm offer the lowest CDS pricing errors when compared to other, traditional default barrier specifications: smooth-pasting condition value, maximum likelihood estimate, KMV’s default point, and nominal debt.