Generalized Dynamic Graphical Models for the impact of the COVID-19 pandemic on financial markets.
|Vincenzo Candila||Componenti strutturati del gruppo di ricerca|
|Paolo De Angelis||Componenti strutturati del gruppo di ricerca|
|Beatrice Foroni||Dottorando/Assegnista/Specializzando componente non strutturato del gruppo di ricerca|
|Giorgio Alleva||Componenti strutturati del gruppo di ricerca|
|Marco Geraci||Componenti strutturati del gruppo di ricerca|
|Alberto Giovanni Arcagni||Componenti strutturati del gruppo di ricerca|
The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and the rapid spread of the COVID-19 disease that began at the dawn of 2020, have had a dramatic impact on financial markets around the globe. The COVID-19 crisis has created an unprecedented level of risk, causing investors to suffer significant losses in a very short period of time. The outbreak of the pandemic, including the associated public health measures for containment and suppression, have led to an economic and financial downturn. The goal of the proposed research is to study the multifaceted economic and social consequences of the COVID-19 on financial markets interdependence and on the most important economic indicators. To achieve an in-depth understanding of the market reactions to this shock, we develop a graphical modeling framework that allows us to investigate how and to what extent the interconnections among several stock markets, have been impacted, modified and reshaped, because of the COVID-19 pandemic outbreak. In particular, to handle both continuous and discrete variables we will use Mixed Graphical Models (MGMs) which are able to learn the graph structure between heterogeneous variables in the network. To capture how interactions between the variables in the network change over time during the pandemic period, we introduce a dynamic MGM by considering state-dependent parameters evolving over time according to a latent semi-Markov process. Moreover, since the high level of integration of international financial markets highlights the need to accurately assess contagion and systemic risk under different market conditions, we also propose a dynamic quantile graphical model to identify the tail conditional dependence structure in multivariate data across different quantiles of the variables in the network. We will apply the proposed models to a large financial and economics datasets from several countries and develop tools to be includeded in freely availabe R packages.