Does modeling framework matter? A comparative study of structural and reduced-form models

Abstract

This study provides a rigorous empirical comparison of structural and reduced-form credit risk frameworks. As major difference we focus on the discriminative modeling of default time. In contrast to previous literature, we calibrate both approaches to bond and equity prices. By using same input data, applying comparable estimation techniques, and assessing the out-of-sample prediction quality on same time series of CDS prices we are able to judge whether empirically the model structure itself makes an important difference. Interestingly, the models' prediction power is quite close on average. Still, the reduced-form approach outperforms the structural for investment-grade names and longer maturities. JEL Classification: G13Keywords: Credit risk, structural models, reduced-form models, default intensity, stationary leverage, credit default swaps. Non-technical SummaryIn the financial industry, applying the Black/Scholes option pricing framework for pricing purposes has been widely accepted as the benchmark model for equity and FX derivatives.However, no single agreed pricing model has emerged that could serve as a benchmark for instruments that are exposed to credit risk. The literature differentiates between structural models that are based on modeling of the evolution of the balance sheet of the issuer, and reduced-form models that specify credit risk exogenously by a default intensity process.Until now, there has been no common agreement in academia and in the financial industry on which model framework better captures credit risk. In previous studies, even when testing the same model, the use of different datasets has contributed to quite different results. This study overcomes this issue by applying the same dataset to structural and reduced-form approaches. Leverage has been used as a key credit risk factor that could be explanatory in both frameworks. By using the same input data, applying comparable estimation techniques and assessing the out-of-sample prediction quality on the same time series of CDS prices, we are able to judge whether empirically the model structure itself makes an important difference. The models' predictive power is quite close on average, indicating that for pricing purposes the modeling type does not matter compared to the input data used. Still, the reduced-form approach outperforms the structural approach for investment-grade names and longer maturities. In contrast, the structural approach performs better for shorter maturities and sub-investment grade names. The study concludes that both frameworks provide CDS price prediction results equally well if a basis of comparison can be provided. These results have implications on choosing appropriate risk measurement techniques in financial markets.

A hybrid information approach to predict corporate credit risk

“…K E Y W O R D S bond spread, corporate credit risk, model combination, reduced-form model, structural model J E L C L A S S I F I C A T I O N C22, G13 J Futures Markets. Similar results for individual models are also reported by Gündüz and Homburg (2014). The formulations of the two structural models are consistent with the manager's the perspective that the firm's condition is observable and that default is an accessible stopping time.…”

“…Similar results for individual models are also reported by Gündüz and Homburg (2014). Our data set consists of 279,826 monthly corporate bond yield spreads to the swap rate of noncallable bonds issued by industrial firms over the period 1992-2016.…”

“…We also find that while the reduced-form models contribute more in the pooled strategies for speculative-grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names. In addition, previous empirical studies (e.g., Gündüz & Homburg, 2014) suggest that the reduced-form approach outperforms the structural models for investment-grade (IG) names and longer maturities, and the structural approach performs better for shorter maturities and sub-IG names. 2018;38:1062-1078. wileyonlinelibrary.com/journal/fut 1062 | © 2018 Wiley Periodicals, Inc.…”

“…The model combination has been widely used in econometric forecasting since the pioneer work by Bates and Granger (1969). In addition, previous empirical studies (e.g., Gündüz & Homburg, 2014) suggest that the reduced-form approach outperforms the structural models for investment-grade (IG) names and longer maturities, and the structural approach performs better for shorter maturities and sub-IG names. Some excellent reviews include Clemen (.…”

Pricing Vulnerable Options with Jump Clustering

“…Owing to its tractability, the reduced‐form approach to modeling credit risk is popular. Though there exist differences between the two approaches, Gündüz and Uhrig‐Homburg () have recently shown that their predictive powers are quite close, on average. For more details about the two approaches, please refer to Bielecki and Rutkowski (), and the references therein.…”