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.