Does model complexity improve pricing accuracy? The case of CoCos

Abstract

In this study, we analyze whether model complexity improves accuracy of CoCo pricing models. We compare the out-of-sample pricing ability of four models using a broad dataset that contains all CoCos which were issued between January 1, 2013 and May 31, 2016 in euros. The regarded models include the standard model from De Spiegeleer and Schoutens (J Deriv 20:27–36, 2012), a modified version enriched by credit risk, an extended model that accounts for the effective lifetime of the CoCo, and a trading model, solely based on historic market prices but no pricing theory at all. For a normal market environment, the simple trading model provides a higher pricing accuracy than the theory-based models. Under distress, however, a theory-based model with a sufficiently high complexity is required.

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“…However, there is an ongoing debate concerning the relationship between model complexity and accuracy in literature. To analyze this relationship, Koziol and Weitz (2021) examined various pricing models and input data types (e.g., historical data, solvency data, and product data). They found that under normal circumstances, in their case, a normal market environment, increased model complexity does not necessarily improve its output accuracy and that input data can also play a central role (Koziol & Weitz, 2021).…”

“…To analyze this relationship, Koziol and Weitz (2021) examined various pricing models and input data types (e.g., historical data, solvency data, and product data). They found that under normal circumstances, in their case, a normal market environment, increased model complexity does not necessarily improve its output accuracy and that input data can also play a central role (Koziol & Weitz, 2021). In an earlier evaluation of the complexity and the accuracy of different forecasting models, Ahlburg (1995) concluded that "it is too early to say whether simple models are more accurate than complex models or whether causal models are more accurate than noncausal models" (p. 287); this debate continues today.…”