Model averaging and ensemble methods for risk corporate estimation

SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions

Marika Vezzoli University of Brescia

Silvia Figini University of Pavia

Sovereigns Banks and other Financial

Intermediaries (BFIs)

Corporations

Sovereigns Banks and other Financial

Intermediaries (BFIs)

Corporations

!   In this study we investigate ensemble learning and classical model averaging in order to identify which procedure performed better in terms of predictive accuracy

!  We compare ensemble learning approaches, like Random Forest (Breiman, 2001) with Bayesian Model Averaging (BMA) (e.g. Steel, 2011)

!  Moreover, we compare single models with respect to their aggregated version. More precisely:

!  Classification Trees vs Random Forest !  Logistic Regression vs Bayesian Model Averaging

!   In order to make a coherent comparison among the models we have fixed a set of performance indicators able to assess the models at hand

!   Empirical evidences are given on a real credit risk data sample

   

Figini and Giudici (2013)

   

We have compared !   BMA with Random Forests

and also !   Logistic Regression vs Bayesian Model Averaging !   Classification Trees vs Random Forests

Both in the parametric and non parametric frameworks we underline that ensemble models perform better in terms of the key performance indicators employed

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