Distance-to-Default Credit Risk Estimation via Pre-computed Models
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Solution Overview
Problem
The existing methods for calculating a company's distance-to-default using the Merton model are computationally intensive and time-consuming, especially when assessing changes in credit risk for a large portfolio of companies, and current approximations are limited to small changes in underlying factors.
Innovation Solution
A credit evaluation system that creates a training data set from distance-to-default values for a set of companies, builds predictive models based on this data, and forecasts estimated changes in distance-to-default values for other companies using these models, enabling quick and accurate estimates without the need for iterative calculations of asset value and volatility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Merton model is used to calculate distance-to-default via iterative solution of nonlinear equations, then measurement precision is improved, but productivity deteriorates due to computational intensity and time consumption
Solution Approach 1:
The patent pre-calculates and stores the relationship between distance-to-default and its drivers (equity value, asset volatility, debt maturity) by solving the Merton model equations in advance for a range of parameter values. These pre-computed results are then used to quickly estimate distance-to-default changes without performing iterative calculations during actual credit risk assessment, thus resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent creates a simplified copy or approximation of the complex Merton model by establishing empirical relationships or lookup tables based on pre-computed results. This copy allows for rapid estimation of distance-to-default by matching current market conditions against the pre-stored relationships, avoiding the need to solve the full nonlinear system of equations while maintaining reasonable accuracy.
2Productivity
If Chen's numerical approximation method is used to estimate distance-to-default changes, then productivity is improved, but measurement precision deteriorates for large changes in underlying factors
Solution Approach 1:
The patent dynamically selects the appropriate estimation method based on the magnitude of changes in underlying factors. For small changes, it uses the efficient numerical approximation method. For large changes, it switches to methods based on pre-computed relationships that maintain accuracy. This dynamic adaptation resolves the contradiction between speed and accuracy by optimizing the approach according to actual conditions.
Solution Approach 2:
The patent changes the approach parameters based on the scale of input changes. When equity value changes, asset volatility changes, or debt maturity changes exceed certain thresholds, the system transitions from using simple numerical approximations to using pre-computed relationship methods, thereby maintaining measurement precision across different magnitudes of market movements while preserving computational efficiency where applicable.
Data Source
AI summary
A method, computer system, and computer program product are provided for assessing a credit risk of a set of companies. A computer system creates a training data set from distance-to-default values for a first set of companies. The computer system builds a set of predictive models based on the training data set, linking the observed distance-to-default to market capitalization and total liabilities. The computer system forecasts estimated new distance-to-default values for a second set of companies, based on their current distance-to-default (obtained from the Merton approach), and a future change in market capitalization and/or change in total liabilities, according to the set of predictive models.