Based on a large micro data set of loan accounts consisting of restructured and non-restructured loans we investigate the ability of loan-specific and macroeconomic covariates to predict the probability of default (PD). We seek to investigate whether differences in the PD between the two...

In this paper we introduce a discrete time pricing model for a European call option when the log-return of the underlying stock (asset) is subject to discontinuous market regime type of shifts in its mean or volatility whose risk can be priced in the market. The paper shows how to estimate this...

This paper presents a new stochastic volatility model which allows for persistent shifts in volatility of stock market returns, referred to as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are...

This paper employes a parametric model of structural breaks in the mean of stock returns which allows them to be endogenously driven by large positive or negative stock market return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime shifts and/or...

This papers provides clear cut evidence that recessionary and financial distressed conditions, as well as banning foreclosure laws, often introduced by governments to mitigate the effects of the economic and/or financial distressed conditions on mortgage loans, have adverse effects on the loan...

This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD...