WebNov 5, 2024 · Paolo Pagnottoni is Assistant Professor at the Department of Economic and Management of the University of Pavia, Italy. His main research interests cover the application of econometric and network theory approaches to the study of financial stability, systemic risk and the emergence of interdependencies in economic and financial systems. WebJun 22, 2024 · Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy.
Bayesian Variable Selection for Matrix Autoregressive Models
WebDowntown Winter Garden, Florida. The live stream camera looks onto scenic and historic Plant Street from the Winter Garden Heritage Museum.The downtown Histo... WebApr 24, 2024 · Other related researches studied the interconnectedness and spillover in the cryptocurrency market (such as Corbet et al., 2024b; Giudici and Pagnottoni, 2024a,b). Another important area regards the study of Bitcoin derivatives—i.e., options and futures written on Bitcoin, with studies conducted by Corbet et al. ( 2024a ), Baur and Dimpfl ... circumferential vernier wrap tape
Paolo Pagnottoni on LinkedIn: The motifs of risk …
WebMay 6, 2024 · Paolo Pagnottoni University of Pavia Maria Elena De Giuli University of Pavia Abstract and Figures In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin... WebOct 1, 2024 · Paolo Pagnottoni. University of Pavia - Department of Economics and Management. Date Written: September 30, 2024. Abstract. The COVID-19 pandemic has exerted severe impacts on the socioeconomic fabric of worldwide countries, spreading financial and economic instability, affecting sentiment of market participants and … WebAug 18, 2024 · Paolo Pagnottoni. University of Pavia - Department of Economics and Management. Galin L. Jones. University of Minnesota - Minneapolis. Date Written: August 11, 2024. Abstract. We propose a Bayesian method for variable selection in high-dimensional matrix autoregressive models which reflects and exploits the original matrix … diamond invalid host