Foreign currency borrowing of households in new EU member states

The post-Lehman phase of the financial crisis has exposed a number of weaknesses in the banking sectors of the European Union’s New Member States (NMSs). One of these is the prevalence of lending in foreign currency. While banks themselves in these countries have not taken on sizeable currency risk directly, they passed it on to households and the corporate sector. With large depreciations taking place or looming in the region, the currency risk at households and corporates without a natural hedge is now being transformed into credit risk for the banking sector. This is creating a serious problem in maintaining financial stability and cripples monetary policy in countries where it operates primarily through the exchange rate channel. The patterns of foreign currency lending to households in NMSs vary widely both across countries and time periods. For example, FX lending to households is virtually non-existent in the Czech Republic while in some Baltic countries its share is close to 100 per cent of total household lending. The main goal of the paper is (1) to present the stylised facts of pre-crisis FX lending in NMSs systematically and (2) to try to explain these differing patterns in an econometric model. In order to do so, a panel database of household FX borrowing is compiled, covering 10 NMSs in the period 1999-2008. Our estimation results suggest that the degree of household FX borrowing depends on the interest rate differential, the institutional features of mortgage financing and the monetary regime. Household FX borrowing tends to be less prevalent if the interest rate differential is small, fixed interest rate mortgage financing is available and the monetary authority’s “fear of floating” is low.foreign currency lending, new member states, credit risk, monetary policy

tramME: Mixed-Effects Transformation Models Using Template Model Builder

Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME, which builds on existing implementations of transformation models (mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures

Identification of credit supply shocks in a Bayesian SVAR model of the Hungary economy

Using Hungarian macroeconomic and financial data, we estimate a Bayesian structural VAR model suitable for macroprudential simulations. We identify standard macroeconomic and credit supply shocks by sign and zero restrictions. In contrast to the previous literature, different types of credit shocks are distinguished in our paper: a risk assessment and a policy shock. Our main findings are the following. First, we demonstrate that both credit supply and macroeconomic shocks explain the variance of endogenous variables at roughly similar order of magnitude. Second, it is shown that credit supply shocks do not have a dominant role in the decline of the Hungarian economy over the crisis period that started in 2008, although their contribution was non-negligible. Third, the importance of unidentified shocks increased in the crisis period

Foreign currency borrowing of housholds in new EU member states

The post-Lehman phase of the financial crisis has exposed a number of weaknesses in the banking sectors of the European Union's New Member States (NMSs). One of these is the prevalence of lending in foreign currency. While banks themselves in these countries have not taken on sizeable currency risk directly, they passed it on to households and the corporate sector. With large depreciations taking place or looming in the region, the currency risk at households and corporates without a natural hedge is now being transformed into credit risk for the banking sector. This is creating a serious problem in maintaining financial stability and cripples monetary policy in countries where it operates primarily through the exchange rate channel. The patterns of foreign currency lending to households in NMSs vary widely both across countries and time periods. For example, FX lending to households is virtually non-existent in the Czech is close to 100 per cent of total household lending. The main goal of the paper is (1) to present the stylised facts of pre-crisis FX lending in NMSs systematically and (2) to try to explain these differing patterns in an econometric model. In order to do so, a panel database of household FX borrowing is compiled, covering 10 NMSs in the period 1999-2008. Our estimation results suggest that the degree of household FX borrowing depends on the interest rate differential, the institutional features of mortgage financing and the monetary regime. Household FX borrowing tends to be less prevalent if the interest rate differential is small, fixed interest rate mortgage financing is available and the monetary authority's 'fear of floating' is low

Smooth Transformation Models for Survival Analysis: A Tutorial Using R

Over the last five decades, we have seen strong methodological advances in survival analysis, mainly in two separate strands: One strand is based on a parametric approach that assumes some response distribution. More prominent, however, is the strand of flexible methods which rely mainly on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying corresponding available software resources is becoming increasingly difficult. This task becomes particularly challenging in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportionality, or dependent censoring. In this tutorial, we explore the potential of using smooth transformation models for survival analysis in the R system for statistical computing. These models provide a unified maximum likelihood framework that covers a range of survival models, including well-established ones such as the Weibull model and a fully parameterised version of the famous Cox proportional hazards model, as well as extensions to more complex scenarios. We explore smooth transformation models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalised medicine within this framework. By fitting these models to survival data from a two-arm randomised controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated within the smooth transformation model framework in R. This is achieved by the implementation provided by the "tram" package and few related packages

Electrochemical migration of Sn and Ag in NaCl environment

The impact of chloride ion concentration on electrochemical migration (ECM) of tin and silver was studied by using an in-situ optical and electrical inspection system. It was found, that in both cases, dendrites grow not only in an electrolyte solution at low chloride concentration but also in an electrolyte at medium and high or even saturated chloride concentrations as well. According to the results, the migration susceptibility has decreased at low and medium concentration levels in both cases. However, the ECM susceptibility of Ag has increased, while the migration susceptibility of Sn was decreased at the saturated concentrations

Effect of Bismuth and Silver on the Corrosion Behavior of Lead-free Solders in 3.5 wt% NaCl Solution

Electroanalytical tests were carried out to investigate the corrosion behavior of various lead-free solder alloys in 3.5 wt% NaCl bulk solution under room temperature. Scanning electron microscope (SEM) method was applied to investigate the corroded surface structure and corrosion depth using cross-sectional samples. Furthermore, energy dispersive spectroscopy (EDS) method was also applied to identify the chemical elemental composition of the corrosion products of the solders. The results showed that the bismuth and silver bearing solders have lower corrosion resistance compared to other lead-free solders and to the widely used SAC305 as well. The different corrosion resistance was explained by the different silver and bismuth content and volume of the corrosion products, which can lead back to the differences of the original alloying components. This study is highlighted that the bismuth and silver content may pose a relative high corrosion risk related to lead-free solder alloys used in electronics

Individual participant data meta-analysis with mixed-effects transformation models

One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying R package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach