In this paper we build an agent-based model based on a threefold financial accelerator: (i) leverage accelerator - negative shocks on firms' output make banks less willing to loan funds, and firms less willing to make investments, hence a credit reduction follows further reducing the output;...

In the present paper we analyse the role of dividends distributed by firms and banks, highlighting the effects of their increase on financial instability and macroeconomic dynamics. During the last decades, the financialisation of nonfinancial corporations has been characterised by a shift from...

Starting from the agent-based decentralized matching macroeconomic model proposed in Riccetti et al. (2012), we explore the effects of banking regulation on macroeconomic dynamics. In particular, we study the overall credit exposure and the lending concentration towards a single counterparty,...

In this paper we analyze the network structure that endogenously emerges in the credit market of the agent-based model of Riccetti et al. (2011), where two kinds of financial accelerator are at work: the 'leverage accelerator' and the 'network-based accelerator'. We focus on the properties of...

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature and can be seen as a first-order perturbation around the...

Validation has been an important issue in using the ABM approach. It has been pointed out that deriving the necessary conditions for reproducing specific macro behavior is difficult due to the functional complexity of the models. However, based on the authors' experience in ABM, we believe it is...

This paper develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. We use an artificial neural network (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm...

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature and can be seen as a first-order perturbation around the...

In this paper, we suggest a first-passage-time model which can explain default probability and default correlation dynamics under stochastic market environment. We add a Markov regime-switching market condition to a first-passage-time model of Zhou (2001). Using this model, we try to explain...

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an...