A calibration method for structural models of credit risk with reporting bias

We propose a novel calibration methodology based on the maximum likelihood estimator to recover the parameters of a structural model of credit risk which accounts for potential reporting bias. Such bias is introduced by the managers and it is unobserved by outsider investors which can only estimate it. The calibration is performed using a combination of balance sheet, financial indicators and market prices of equities. We apply the calibration algorithm to Tyco, a real case of reporting bias in the United States history. We show that the calibrated model is able to predict the market stock price with a high degree of accuracy.