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

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

eWOM and growth strategies for the tourism industry in maritime museum networks. The case of the ARCA Adriatica tourist product

Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented

A Matrix-Variate t Model for Networks

Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statistical treatment of the data is needed before network analysis can be performed. We show that node centrality measures can be heavily affected by random errors and propose a flexible model based on the matrix-variate t distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions

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

Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with 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 European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease

eWom and sentiment analysis to support decision processes within maritime heritage museum networks. The case of Arca Adriatica

Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented

Metformin: a modulator of bevacizumab activity in cancer? A case report.

Recurrent type I endometrial cancer ((EC)) has poor prognosis and demands novel therapeutic approaches. Bevacizumab, a VEGF-A neutralizing monoclonal antibody, has shown clinical activity in this setting. To our knowledge, however, although some diabetic cancer patients treated with bevacizumab may also take metformin, whether metformin modulates response to anti-VEGF therapy has not yet been investigated. Here, we report the case of a patient with advanced (EC) treated, among other drugs, with bevacizumab in combination with metformin. The patient affected by relapsed (EC) G3 type 1, presented in march 2010 with liver, lungs and mediastinic metastases. After six cycles of paclitaxel and cisplatin she underwent partial response. Later on, she had disease progression notwithstanding administration of multiple lines of chemotherapy. In march 2013, due to brain metastases with coma, she began steroid therapy with development of secondary diabetes. At this time, administration of Bevacizumab plus Metformin improved her performance status. CT scans performed in this time window showed reduced radiologic density of the lung and mediastinic lesions and of liver disease, suggestive of increased tumor necrosis. Strong F-18-FDG uptake by PET imaging along with high levels of monocarboxylate transporter 4 and lack of liver kinase B1 expression in liver metastasis, highlighted metabolic features previously associated with response to anti-VEGF therapy and phenformin in preclinical models. However, clinical benefit was transitory and was followed by rapid and fatal disease progression. These findingsalbeit limited to a single casesuggest that tumors lacking LKB1 expression and/or endowed with an highly glycolytic phenotype might develop large necrotic areas following combined treatment with metformin plus bevacizumab. As metformin is widely used among diabetes patients as well as in ongoing clinical trials in cancer patients, these results deserve further clinical investigation

Bayesian Dynamic Tensor Regression

High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parametrization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time

Opinion Dynamics and Disagreements on Financial Networks

We propose a new measure of disagreement based on connectedness, which generalizes the disagreement index introduced in Billio et al. (2018). Building on the lifting approach in Hendrickx (2014), we extend Billio et al. (2018) to signed networks, which allows us to consider more general consensus dynamics and disagreement with antagonistic behaviour. Synthetic and real-world financial networks of serial correlation are considered for illustrating the new measure and for studying opinion dynamics and convergence to consensus on prices for financial assets

Markov Switching Panel with Endogenous Synchronization Effects

This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may nd application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest.This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may find application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest