This article introduces a very flexible framework for causal and predictive market views and stress-testing. The framework elegantly combines Bayesian networks (BNs) and Entropy Pooling (EP). In the new framework, BNs are used to generate a finite set of joint causal views / stress-tests for the...

MONASH models are descended from Johansen's 1960 model of Norway. The first MONASH model was ORANI, used in Australia's tariff debate of the 1970s. Johansen's influence combined with institutional arrangements in their development gave MONASH models distinctive characteristics, facilitating a...

This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for...

This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic parameterized expectations algorithm (PEA)....

In this paper we apply the multivariate construction for Lévy processes introduced by Ballotta and Bonfiglioli (2014) to propose an integrated model for the joint dynamics of FX exchange rates and asset prices. We show that the proposed construction is consistent in terms of symmetries with...

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 use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic...

This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic parameterized expectations algorithm (PEA)....

This paper presents how scenario analysis techniques can be used for building financial models that are able to capture the dynamics of the underlying asset prices both in benign periods and in times of stress. The paper presents case studies for building pricing models for equity and FX...