ABSTRACT
We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioural heterogeneous agents' model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event.
ABSTRACT
The global energy crisis that began in fall 2021 and the subsequent spike in energy prices constitute a significant challenge for the world economy that risks undermining the post-COVID-19 recovery. In this paper, we develop and calibrate a new Multi-Agent model for Transition Risks (MATRIX) to analyze the role of energy in the functioning of a complex adaptive system and the economic and distributional effects of energy shocks. The economic system is populated by heterogeneous agents, i.e., households, firms and banks, which take optimal decision rules and interact in decentralized markets characterized by limited information. After calibrating the model on US quarterly macroeconomic data, we assess the economic and distributional impacts of different types of energy shocks, namely: (i) an exogenous increase in the price of fossil fuels (e.g., oil or gas);(ii) a decrease in energy firms' productivity;(iii) a reduction in the available quantity of fossil fuels. We find that the energy shocks entail similar effects at the aggregate level in terms of higher inflation and lower real GDP. Nevertheless, the distribution of gains and losses across sectors and agents varies significantly depending on the type of shock. Our findings suggest that policymakers should carefully consider the nature of energy shocks and the resulting distributional effects to design effective measures in response to energy crises.
ABSTRACT
The aim of this paper is to estimate the potential impacts of different COVID-19 scenarios on the Italian energy sector through 2030, with a specific focus on transport and industry. The analysis takes a multi-disciplinary approach to properly consider the complex interactions of sectors across Italy. This approach includes the assessment of economic conditions using macroeconomic and input-output models, modelling the evolution of the energy system using an energy and transport model, and forecasting the reaction of travel demand and modal choice using econometric models and expert interviews. Results show that the effect of COVID-19 pandemic may lead to mid-term effects on energy consumption. The medium scenario, which assumes a stop of the emergency by the end of 2021, shows that energy-related emissions remain 10% lower than the baseline in the industry sector and 6% lower in the transport sector by 2030, when compared with a pre-COVID trend. Policy recommendations to support a green recovery are discussed in light of the results.