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. 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

Buildings’ Energy Efficiency and the Probability of Mortgage Default: The Dutch Case

We investigate the relationship between building energy efficiency and the probability of mortgage default. To this end, we construct a novel panel data set by combining Dutch loan-level mortgage information with provisional building energy ratings provided by the Netherlands Enterprise Agency. Using the logit regression and the extended Cox model, we find that building energy efficiency is associated with a lower probability of mortgage default. There are three possible channels that might drive the results: (i) personal borrower characteristics captured by the choice of an energy-efficient building, (ii) improvements in building performance that could help to free-up the borrower’s disposable income, and (iii) improvements in dwelling value that lower the loan-to-value ratio. We address all three channels. In particular, we find that the default rate is lower for borrowers with less disposable income. The results hold for a battery of robustness checks. This suggests that the energy efficiency ratings complement borrowers’ credit information and that a lender using information from both sources can make superior lending decisions than a lender using only traditional credit information. These aspects are not only crucial for shaping future energy policy, but also have implications for the risk management of 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

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

Market Linkages, Variance Spillover and Correlation Stability: Empirical Evidences of Financial Contagion

Working paper 0602 GRETA, Venezi

Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

We propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models are individually misspecified. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and surveys of stock market prices. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis; structural changes like the Great Moderation are empirically identified by our model combination and the predicted probabilities of recession accurately compare with the NBER business cycle dating. Model weights have substantial uncertainty attached and neglecting this may seriously affect results. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the left tail of the professional forecasts during the start of the financial crisis around 2008