Countries that joined the European Union in 2004 have to decide when to adopt the Euro. This decision depends on the evaluation of the relative costs and benefits associated with giving up the exchange rate instrument. Recent empirical work on several new EU members has questioned the role of the exchange rate as a shock absorber, thus downplaying the potential costs in terms of macroeconomic stabilization. In this paper, we address the issue from a different perspective, emphasizing the role of pass-through from exchange rate to domestic inflation in new EU members. The focus is on four countries (Czech Republic, Hungary, Poland and Slovenia – NM-4) that have adopted some form of floating or managed exchange rate regimes. The paper reports empirical results indicating high pass-through coefficients and links them to the degree of policy accommodation. High exchange rate pass-through in NM-4 indicates that stabilization of nominal exchange rates would lower inflationary pressures and help fulfill criteria to enter the EMU.

In this paper we propose a novel credit risk modelling approach where number of days past due is modeled instead of a binary indicator of default. In line with regulatory requirements, the number of days overdue on loan repayments are transformed to a binary variable by applying 90-days past due threshold, and use it as the dependent variable in default probability models. However, potentially useful information is lost with this transformation. Lower levels of days past due are expected to be good predictors of future incidence of default. We show that a dynamic Tobit model, where number of days overdue is used as a censored continuous dependent variable, significantly outperforms models based on binary indicators of default. It correctly identifies more than 70% of defaulters and issues less than 1% of false alarms. Its superiority is confirmed also by more accurate rating classification, higher rating stability over the business cycle and more timely identification of defaulted borrowers. The implications for banks are clear. By modelling number of days past due they can significantly improve risk identification and reduce procyclicality of IRB capital requirements. Moreover, we show how modelling of days past due can be used also for stage allocation for the purposes of IFRS9 reporting.