Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …

We compare the performance of a wide set of regression techniques and machine-learning
algorithms for predicting recovery rates on non-performing loans, using a private database …

We extend the linear quantile regression with a neural network structure to enable more
flexibility in every quantile of the bank loan loss given default distribution. This allows us to …

With the advent of the new Basel Capital Accord, banking organizations are invited to
estimate credit risk capital requirements using an internal ratings based approach. In order …

We develop a model of the joint capital structure decisions of banks and their borrowers.
Bank leverage of 85% or higher emerges because bank seniority both dramatically reduces …

We compare six modeling methods for Loss Given Default (LGD). We find that non-
parametric methods (regression tree and neural network) perform better than parametric …

Mortgage default rates rose dramatically during the recent housing market downturn,
increasing from less than 1% to historically high levels. Understanding the rise in mortgage …

Loss given default modelling has become crucially important for banks due to the
requirement that they comply with the Basel Accords and to their internal computations of …

We study how the term structure of interest rates relates to mortgage choice at both
household and aggregate levels. A simple utility framework of mortgage choice points to the …

The mortgage meltdown: what went wrong and how do we fix it? Owning a home can
bestow a sense of security and independence. But today, in a cruel twist, many Americans …