Mateusz Pipień

Macroprudential policy gained significance after the 2007 crisis, increasing the importance of financial cycle analyses. In this study, the authors investigate the empirical features of the creditcycle in Poland, broken down by individual banks. The results point out a decrease in the amplitude of cyclical fluctuations. Furthermore the empirical findings indicate a differentiation of credit cycles in individual banks, suggesting that a microprudential policy towards one bank (or a group of banks) could bring similar effects to macroprudential policy. The results indicate the need to supplement the macroprudential policy analysis with disaggregated data for individual banks.

For a selected group of Latin America countries we estimated the parameters of convergence equations on the basis of annual data. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE) that departures from standard approach utilizing panel regressions. We show empirical evidence in favor of the variability of parameters describing the convergence effect and productivity growth rates across analyzed club of countries. We also test several restrictions leading to less parameterized models imposing constancy of parameters of interest across countries.

Assessment of welfare effects of macroprudential policy seems the most important application of the Dynamic Stochastic General Equilibrium (DSGE) framework of macro-modelling. In particular, the DSGE-3D model, with three layers of default (3D), was developed and used by the European Systemic Risk Board and European Central Bank as a reference tool to formally model the financial cycle as well as to analyze effects of macroprudential policies. Despite the extreme importance of incorporating financial constraints in Real Business Cycle (RBC) models, the resulting DSGE-3D construct still embraces the representative agent idea, making serious analyses of diversity of economic entities impossible. In this paper, we present an alternative to DSGE modelling that seriously departs from the assumption of the representativeness of agents. Within an Agent Based Modelling (ABM) framework, we build an environment suitable for performing counterfactual simulations of the impact of macroprudential policy on the economy, financial system and society. We contribute to the existing literature by presenting an ABM model with broad insight into heterogeneity of agents. We show the stabilizing effects of macroprudential policies in the case of economic or financial distress.

We demonstrate that analysis of long series of daily returns should take into account potential long-term variation not only in volatility, but also in parameters that describe asymmetry or tail behaviour. However, it is necessary to use a conditional distribution that is flexible enough, allowing for separate modelling of tail asymmetry and skewness, which requires going beyond the skew- t form. Empirical analysis of 60 years of S&P500 daily returns suggests evidence for tail asymmetry (but not for skewness). Moreover, tail thickness and tail asymmetry is not time-invariant. Tail asymmetry became much stronger at the beginning of the Great Moderation period and weakened after 2005, indicating important differences between the 1987 and the 2008 crashes. This is confirmed by our analysis of out-of-sample density forecasting performance (using LPS and CRPS measures) within two recursive expanding-window experiments covering the events. We also demonstrate consequences of accounting for long-term changes in shape features for risk assessment.

For two groups of post-communist countries (CEE and CIS) we estimated the parameters of convergence equations on the basis of annual data. We depart from standard econometric theory, which involves panel regression techniques. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE). We show empirical evidence in favour of the variability of parameters describing the convergence effect and productivity growth rates across countries. Our approach seems a convincing alternative to the panel regression approach where random effects can be estimated, imposing an assumption about the constancy of structural parameters within the group of countries under analysis. We discuss the role of the global financial crisis in the heterogeneity of convergence processes and productivity at the country level. The aforementioned SURE model was estimated based on two datasets, one containing observations prior to the crisis and the second containing the whole sample.

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In this paper we aim to find out whether bank specialization and bank capitalization affect the relationship between loans growth and capital ratio, both in expansions and in contractions. We hypothesize that the impact of bank capital on lending is relatively strong in cooperative banks and savings banks. We also expect that this effect is nonlinear, and is stronger in “low” capital banks than in “high” capital banks. In order to test our hypotheses, we apply the two-step GMM robust estimator for data spanning the years 1996–2011 on individual banks available in the Bankscope database. Our analysis shows that lending of poorly capitalized banks is more affected by capital ratio than lending of well-capitalized banks. Loans growth of cooperative and savings banks is more capital constrained that lending of commercial banks. Capital matters for the lending activity in contractions only in the case of savings and “low” capital banks.