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Published 2009 | Published
Book Section - Chapter Open

A Calibration Method for Structural Models of Credit Risk with Reporting Bias

Abstract

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.

Additional Information

© 2009 IEEE. This work was supported by a SISL fellowship granted by the Information and Sciences Laboratory at Caltech.

Attached Files

Published - Capponi2009p80262009_Ieee_Symposium_On_Computational_Intelligence_For_Financial_Engineering.pdf

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Capponi2009p80262009_Ieee_Symposium_On_Computational_Intelligence_For_Financial_Engineering.pdf

Additional details

Created:
August 20, 2023
Modified:
October 20, 2023