This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Credit risk is the main risk in the banking sector and is as such one of key issues for financial stability. We estimate various PD models and use them in the application to credit rating classification. Models include firm specific characteristics and macroeconomic or time effects. By linking...

This paper proposes a credit scoring model for the empirical assessment of default risk drivers of shipping bank loans. A unique dataset, consisting of the credit portfolio of a ship-lending bank is used to estimate a logit model with two-way clustered adjusted standard errors, ensuring robust...

Building on the work of Sorge and Virolainen (2006), we revisit the data on aggregate Finnish bank loan losses from the corporate sector, which covers the ‘Big Five' crisis in Finland in the early 1990s. Several extensions to the empirical model are considered. These extensions are then used...

We present a stochastic simulation model for estimating forward-looking corporate probability of default and loss given default. We formulate the model in a discrete time frame, apply capital-budgeting techniques to define the relationships that identify the default condition, and solve the...

We use a vector error correction model to study the long-term relationship between aggregate expected default frequency and the macroeconomic development, i.e. CPI, industry production and short-term interest rate. The model is used to forecast the median expected default frequency of the...