196,981 research outputs found
Measuring sovereign bond fragmentation in the Eurozone
No full textFragmentation in the sovereign bond market of the Eurozone involves divergences in borrowing costs and undermines the stability of the monetary union. In this paper, we propose an
indicator of fragmentation between government bonds of the core and peripheral European
countries. Using a regime-switching cointegration model, we identify the absence of fragmentation as periods where the bond yields of the two groups share a common stochastic
trend in the long-run. The results show that the indicator of fragmentation is responsive to
systemic stress events and is negatively related to the ECB’s monetary policy actions
A Matrix-Variate t Model for Networks
No full textNetworks 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
No full textNetwork 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.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 COVID19 disease
Bayesian Dynamic Tensor Regression
No full textHigh- 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
Bayesian semiparametric inference for TVP-SVAR models with asymmetry and fat tails
No full textTime-varying parameter (TVP) structural vector autoregressive models with stochastic volatility (SVAR-SV) usually assume Gaussian innovations and a smooth or discrete path for the coefficients. To account for possible skewness and fat tails, this work introduces a semiparametric mixture of multivariate restricted skew-t innovation distributions, also permitting the inference of clusters of asymmetry across data series. Moreover, a dynamic shrinkage prior is designed for the coefficients of the contemporaneous and lagged variables to model the path of the parameters flexibly. Inference in high-dimensional settings is performed via a Markov chain Monte Carlo algorithm that leverages the stochastic representation of the skew-t distribution for obtaining a conditional linear Gaussian state-space model. Then, the algorithm alternates between the centred and non-centred parametrizations to improve the mixing and samples from the joint smoothed distribution without loops. The proposed semiparametric approach is combined with a sparsification method to extract time-varying Granger-causal networks in different applications regarding the COVID-19 pandemic across Europe and financial contagion transmission in Europe and the world
Sotto il Segno di Roma
No full textRoma e la conquista dell'Italia - Le forme della "romanizzazione" - Il popolamento di età romana nel territorio montano a Nord e a Sud della Salaria - Il territorio della Sabina interna (estremità settentrionale della Regio IV: Abruzzo, Lazio, Umbria) - Il territorio piceno e medio-adriatico (area centro-meridionale della Regio V: Marche, Abruzzo) - La viabilità - Riferimenti bibliografic
Google search volumes and the financial markets during the COVID-19 outbreak
No full textDuring the outbreak of the COVID-19, concerns related to the severity of the pandemic have played a prominent role in investment decisions. In this paper, we analyze the relationship between public attention and the financial markets using search engine data from Google Trends. Our findings show that search query volumes in Italy, Germany, France, Great Britain, Spain, and the United States are connected with stock markets. The Italian Google Trends index is found to be the main driver of all the considered markets. Furthermore, the country-specific market impacts of COVID-19-related concerns closely follow the Italian lockdown process
Proper scoring rules for evaluating density forecasts with asymmetric loss functions
No full textThis paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the
ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period
BLGA at high ionic strength: vanishing attractive interactions and 'frustrated' aggregation
No full textExtreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment
No full textIn this article, we provide a comment on the work of Dai et al. (2023), who introduced the Time-Varying Parameters Quantile Vector Auto Regressive model (TVP-QVAR) to analyze the spillovers between high carbon emission stocks, green bonds, and crude oil. We argue that some peculiar results provided in the study cited above are due to a mismatch between the methodology presented by the authors and the code used to conduct the empirical analysis. We empirically support our claims by applying an approximate methodology to the data shared by Dai et al. (2023)
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