Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. A new Bayesian semiparametric model for temporal multilayer networks with both intra- and inter-layer connectivity is proposed. A hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the number of COVID-19 cases in Europe. Two layers, defined by stock returns and volatilities are considered and within and between layers connectivity is investigated. The financial connectedness arising from the interactions between two layers is measured. The model is applied in order to compare the topology of the network before and after the spreading of the COVID-19 disease.

COVID-19 spreading in financial networks: A semiparametric matrix regression model

Billio M.;Casarin R.;Costola M.;Iacopini M.
2024-01-01

Abstract

Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. A new Bayesian semiparametric model for temporal multilayer networks with both intra- and inter-layer connectivity is proposed. A hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the number of COVID-19 cases in Europe. Two layers, defined by stock returns and volatilities are considered and within and between layers connectivity is investigated. The financial connectedness arising from the interactions between two layers is measured. The model is applied in order to compare the topology of the network before and after the spreading of the COVID-19 disease.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S2452306221001180-main.pdf

accesso aperto

Descrizione: ten1
Tipologia: Documento in Post-print
Licenza: Accesso gratuito (solo visione)
Dimensione 5.55 MB
Formato Adobe PDF
5.55 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3752110
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact