Abstract
Bharath and Shumway (2008) provide evidence that shows that it is the functional form of Merton’s (1974) distance to default (DD) model that makes it useful and important for predicting defaults. In this paper, we investigate whether the default predictability of the Merton DD model would be affected by taking investors’ ambiguity aversion into consideration. The Cox proportional hazard model is used to compare the forecasting power of Bharath and Shumway’s naive model, which retains the functional form of the Merton DD model and computes the default probability in a naive way, with our new model, which treats investors’ ambiguity aversion as additional information. We provide evidence to show that our new model performs better than Bharath and Shumway’s naive model. In addition, our empirical results show that the statistical significance of Bharath and Shumway’s naive default probability is retained in the credit default swap (CDS) spread regressions, though the sign of the coefficient is changed. However, both the sign and the statistical significance of our model are retained in the CDS spread regressions.
Highlights
Default can happen if the value of the issuer’s total assets is less than the value of the debt obligations should the issuer be unable to make the required payments
It is worth noting that π naive and π Confidence Index (CCI) are useful for measuring default probabilities, as both are significant in Models 1 and 2, and the coefficients of the two models are negative
Hilscher and Szilagyi [2] and Bharath and Shumway [3] provided evidence that the default predictability of the Merton distance to default (DD) model can be attributed to its specific functional form
Summary
Default can happen if the value of the issuer’s total assets is less than the value of the debt obligations should the issuer be unable to make the required payments. The structural model and reduced-form model are the main measures of default probabilities. Model, which is inspired by Merton’s [1] bond pricing model, a default-triggering event is explicitly defined as a firm’s failure to pay debt obligations by means of modeling the equity value of the firm as a call option on the firm’s value, with the firm’s face value of the debt as a strike price. The Merton model is widely used to measure the distance to default by Moody’s KMV (Kealhofer, McQuown and Vasicek). This was used previously to provide quantitative credit analysis tools to creditors and investors until the acquisition in 2002 by Moody’s Analytics and is part of Moody’s
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