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.
Caporin, Massimiliano; Gupta, Rangan & Ravazzolo, Francesco (2021)
Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach
We study contagion between Real Estate Investment Trusts (REITs) and the equity market in the U.S. over four sub-samples covering January, 2003 to December, 2017, by using Bayesian nonparametric quantile-on-quantile (QQ) regressions with heteroskedasticity. We find that the spillovers from the REITs on to the equity market has varied over time and quantiles defining the states of these two markets across the four sub-samples, thus providing evidence of shift-contagion. Further, contagion from REITs upon the stock market went up during the global financial crisis particularly, and also over the period corresponding to the European sovereign debt crisis, relative to the pre-crisis period. Our main findings are robust to alternative model specifications of the benchmark Bayesian QQ model, especially when we control for omitted variable bias using the heteroskedastic error structure. Our results have important implications for various agents in the economy namely, academics, investors and policymakers.
Ravazzolo, Francesco & Vespignani, Joaquin (2020)
World steel production: A new monthly indicator of global real economic activity
We compare alternative univariate versus multivariate models and frequentist versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts is inspected in four main European markets (Germany, Denmark, Italy, and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian vector autoregressive specifications with exogenous variables dominate other multivariate and univariate specifications in terms of both point forecasting and density forecasting.
Caporin, Massimiliano; Natvik, Gisle James, Ravazzolo, Francesco & Santucci de Magistris, Paolo (2019)
The bank-sovereign nexus: Evidence from a non-bailout episode
We explore the interplay between sovereign and bank credit risk in a setting where Danish authorities first let two Danish banks default and then left the country’s largest bank, Danske Bank, to recapitalize privately. We find that the correlation between bank and sovereign credit default swap (CDS) rates changed with these events. Following the non-bailout events, the sensitivity to external shocks, proxied by CDS rates on the European banking sector, declined both for Danske Bank and for Danish sovereign debt. After Danske Bank’s recapitalization, its exposure to the European banking sector reappeared while that did not happen for Danish sovereign debt. The decoupling between CDS rates on sovereign and private bank debt indicates that the vicious feedback loop between bank and sovereign risk weakened after the non-bailout policies were introduced.
Furlanetto, Francesco; Ravazzolo, Francesco & Sarferaz, Samad (2019)
Identification of financial factors in economic fluctuations
We estimate demand, supply, monetary, investment and financial shocks in a VAR identified with a minimum set of sign restrictions on US data. We find that financial shocks are major drivers of fluctuations in output, stock prices and investment but have a limited effect on inflation. In a second step, we disentangle shocks originating in the housing sector, shocks originating in credit markets and uncertainty shocks. In the extended set‐up, financial shocks are even more important and a leading role is played by housing shocks that have large and persistent effects on output.
Catania, Leopoldo; Grassi, Stefano & Ravazzolo, Francesco (2019)
Forecasting cryptocurrencies under model and parameter instability
This paper studies the predictability of cryptocurrency time series. We compare several alternative univariate and multivariate models for point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto-predictors and rely on dynamic model averaging to combine a large set of univariate dynamic linear models and several multivariate vector autoregressive models with different forms of time variation. We find statistically significant improvements in point forecasting when using combinations of univariate models, and in density forecasting when relying on the selection of multivariate models. Both schemes deliver sizable directional predictability.
Bassetti, Federico; Casarin, Roberto & Ravazzolo, Francesco (2018)
Bayesian Nonparametric Calibration and Combination of Predictive Distributions
In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional forecast evaluation methods to subsets of extreme observations has unexpected and undesired effects, and is bound to discredit skillful forecasts when the signal-to-noise ratio in the data generating process is low. Conditioning on outcomes is incompatible with the theoretical assumptions of established forecast evaluation methods, thereby confronting forecasters with what we refer to as the forecaster’s dilemma. For probabilistic forecasts, proper weighted scoring rules have been proposed as decision-theoretically justifiable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a real data study on probabilistic forecasts of U.S. inflation and gross domestic product (GDP) growth, we illustrate and discuss the forecaster’s dilemma along with potential remedies.
Bjørnland, Hilde C; Ravazzolo, Francesco & Thorsrud, Leif Anders (2017)
Forecasting GDP with global components: This time is different