Methods for modeling credit risk metrics of publicly traded companies in a shot noise market

摘要

Computer-implemented methods are proposed for modeling credit risk metrics of publicly traded companies. The company is characterized with an asset value modeled by superposition of two Geometric Shot Noise random processes. The credit risk metrics include Probability of Default, Distance to Default, Credit Spread, Expected Credit Loss, and Recovery Rate. The proposed analytical structural default model is designed based on options pricing equation obtained by using superposition of two Geometric Shot Noise processes with market memory. The methods define three modes for calculating the credit risk metrics: a double jump mode, a jump-diffusion mode, a diffusion mode. Each mode is characterized with market memory due to autocorrelation of stock price returns empirically observed in the markets. Market memory has two limitation cases: long memory and short memory. For each of the above modes, the long memory case initiates a coherent sub-mode, while the short memory case initiates a memoryless sub-mode.