Hybrid Approach for Quantifying Company Assets Using Structural Credit Risk Models

Abstract

Global investors have been allocating resources for developing quantitative credit risk models for forecasting credit risk and estimating the cost associated with defaults in order to arrive at the credit derivatives which may handle the risks. In this chapter, the authors propose a hybrid Merton model for measuring credit risk. They estimate market volatility using an iterative annualized historical volatility approach and corporate asset value using the Merton model. For corporate assets, actual default probability and risk neutral probability are correlated. Monte Carlo simulation predictions of the real-time asset price of S&P global-listed Tesla Inc. support the approach. The derived book asset value is 0.44% and the simulated asset value is 0.43%. Model convergence is shown by the minimal difference between the past three iterations. The hybrid strategy to select risk neutral stock value captures volatility variance. Comparative analysis with real-time data confirms the approach's correctness.