Head of Department - Department of Data Science and Analytics
His research focuses on Bayesian econometrics, energy economics, financial econometrics and macroeconometrics. He has published in several leading academic journals.
Francesco serves the academia in several roles: he is in the editorial board of the following journals: Annals of Applied Statistics; International Journal of Forecasting; Journal of Applied Econometrics; Studies in Nonlinear Dynamics and Econometrics. He is also member of the executive committee of the Society of Nonlinear Dynamics and Econometrics and of the steering commette of the Italian Econometric Association.
Avesani, Diego; Zanfei, Ariele, Di Marco, Nicola, Galletti, Andrea, Ravazzolo, Francesco, Righetti, Maurizio & Majone, Bruno (2022)
Short-term hydropower optimization driven by innovative time-adapting econometric model
Applied Energy, 310 Doi: 10.1016/j.apenergy.2021.118510
Durante, Fabrizio; Gianfreda, Angelica, Ravazzolo, Francesco & Rossini, Luca (2022)
A multivariate dependence analysis for electricity prices, demand and renewable energy sources
Information Sciences, 590, s. 74- 89. Doi: 10.1016/j.ins.2022.01.003
Billé, Anna Gloria; Gianfreda, Angelica, Del Grosso, Filippo & Ravazzolo, Francesco (2022)
Forecasting electricity prices with expert, linear, and nonlinear models
International Journal of Forecasting Doi: 10.1016/j.ijforecast.2022.01.003
Iacopini, Matteo; Ravazzolo, Francesco & Rossini, Luca (2022)
Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
Journal of business & economic statistics Doi: 10.1080/07350015.2022.2035229
Ferrari, Davide; Ravazzolo, Francesco & Vespignani, Joaquin (2021)
Forecasting energy commodity prices: A large global dataset sparse approach
Energy Economics, 98 Doi: 10.1016/j.eneco.2021.105268
This paper focuses on forecasting quarterly nominal global energy prices of commodities, such as oil, gas and coal,using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of poten-tially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for morethan 80% of the global GDP. To deal with the information on this large database, we apply dynamic factor modelsbased on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimatesparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selectedloadings across variables. When the model is extended to predict energy commodity prices up to four periodsahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter aheadfor all energy commodities and up to 4 quarters ahead for gas prices. Our model also provides superior forecaststhan machine learning techniques, such as elastic net, LASSO and random forest, applied to the same database.
Agudze, Komla M.; Billio, Monica, Casarin, Roberto & Ravazzolo, Francesco (2021)
Markov switching panel with endogenous synchronization effects
Journal of Econometrics, s. 1- 18. Doi: 10.1016/j.jeconom.2021.04.004
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
The North American journal of economics and finance, 55, s. 1- 12. Doi: 10.1016/j.najef.2020.101347
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
Canadian Journal of Economics Doi: 10.1111/caje.12442
Ravazzolo, Francesco; Casarin, Roberto, Corradin, Fausto & Sartore, Domenico (2020)
A scoring rule for factor and autoregressive models under misspecification
Gianfreda, Angelica; Ravazzolo, Francesco & Rossini, Luca (2020)
Comparing the forecasting performances of linear models for electricity prices with high RES penetration
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
Journal of the American Statistical Association Doi: 10.1080/01621459.2016.1273117
Casarin, Roberto; Foroni, Claudia, Marcellino, Massimiliano & Ravazzolo, Francesco (2018)
Uncertainty through the lenses of a mixed-frequency bayesian panel markov-switching model
Annals of Applied Statistics, 12(4), s. 2559- 2586. Doi: 10.1214/18-AOAS1168
Bianchi, Daniele; Guidolin, Massimo & Ravazzolo, Francesco (2018)
Dissecting the 2007-2009 real estate market bust: Systematic pricing correction or just a housing fad?
Journal of Financial Econometrics, 16(1), s. 34- 62. Doi: 10.1093/jjfinec/nbx023
Foroni, Claudia; Ravazzolo, Francesco & Sadaba, Barbara (2018)
Assessing the predictive ability of sovereign default risk on exchange rate returns
Journal of International Money and Finance, 81, s. 242- 264. Doi: 10.1016/j.jimonfin.2017.12.001
Bianchi, Daniele; Guidolin, Massimo & Ravazzolo, Francesco (2017)
Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section
Journal of business & economic statistics, 35(1), s. 110- 129. Doi: 10.1080/07350015.2015.1061436
Krüger, F; Clark, Todd E & Ravazzolo, Francesco (2017)
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts
Journal of business & economic statistics, 35(3), s. 470- 485. Doi: 10.1080/07350015.2015.1087856
Lerch, Sebastian; Thorarinsdottir, Thordis Linda, Ravazzolo, Francesco & Gneiting, Tilmann (2017)
Forecaster's dilemma: Extreme events and forecast evaluation
Statistical Science, 32(1), s. 106- 127. Doi: 10.1214/16-STS588
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
International Journal of Forecasting, 33(1), s. 153- 173. Doi: 10.1016/j.ijforecast.2016.02.004
Pettenuzzo, Davide & Ravazzolo, Francesco (2016)
Optimal Portfolio Choice Under Decision-Based Model Combinations
Journal of applied econometrics, 31(7), s. 1312- 1332. Doi: 10.1002/jae.2502
Lombardi, Marco J & Ravazzolo, Francesco (2016)
On the correlation between commodity and equity returns: Implications for portfolio allocation
Journal of Commodity Markets, 2(1), s. 45- 57. Doi: 10.1016/j.jcomm.2016.07.005
Billio, Monica; Casarin, Roberto, Ravazzolo, Francesco & van Dijk, Herman K. (2016)
Interconnections Between Eurozone and us Booms and Busts Using a Bayesian Panel Markov-Switching VAR Model
Journal of applied econometrics, 31(7), s. 1352- 1370. Doi: 10.1002/jae.2501
Aastveit, Knut Are; Jore, Anne Sofie & Ravazzolo, Francesco (2016)
Identification and real-time forecasting of Norwegian business cycles
International Journal of Forecasting, 32(2), s. 283- 292. Doi: 10.1016/j.ijforecast.2015.06.006
Casarin, Roberto; Grassi, Stefano, Ravazzolo, Francesco & van Dijk, Herman K. (2015)
Parallel sequential monte carlo for efficient density combination: The DeCo MATLAB toolbox
Journal of Statistical Software, 68 Doi: 10.18637/jss.v068.i03
Monticini, Andrea & Ravazzolo, Francesco (2014)
Forecasting the intraday market price of money
Journal of Empirical Finance, 29, s. 304- 315. Doi: 10.1016/j.jempfin.2014.08.006
Ravazzolo, Francesco & Vahey, Shaun P (2014)
Forecast densities for economic aggregates from disaggregate ensembles
Studies in Nonlinear Dynamics & Econometrics, 18(4), s. 367- 381. Doi: 10.1515/snde-2012-0088
Martinsen, Kjetil; Ravazzolo, Francesco & Wulfsberg, Fredrik (2014)
Forecasting macroeconomic variables using disaggregate survey data
International Journal of Forecasting, 30(1), s. 65- 77. Doi: 10.1016/j.ijforecast.2013.02.003
Billio, Monica; Casarin, Roberto, Ravazzolo, Francesco & van Dijk, Herman K. (2013)
Time-varying combinations of predictive densities using nonlinear filtering
Journal of Econometrics, 177(2), s. 213- 232. Doi: 10.1016/j.jeconom.2013.04.009
Ravazzolo, Francesco & Rothman, Philip (2013)
Oil and U.S. GDP: A Real-Time Out-of-Sample Examination
Journal of Money, Credit and Banking, 45(2-3), s. 449- 463. Doi: 10.1111/jmcb.12009
Ravazzolo, Francesco & Lombardi, Marco J (2012)
Oil price density forecasts: exploring the linkages with stock markets
[Report]. Handelshøyskolen BI.
Ravazzolo, Francesco; Rigobon, Roberto, Caporin, Massimiliano & Pelizzon, Loriana (2012)
Measuring Sovereign Contagion in Europe
[Report]. Handelshøyskolen BI.
Ravazzolo, Francesco & Rothman, Philip (2011)
Oil and US GDP: A Real-Time Out-of Sample Examination
[Report]. Handelshøyskolen BI.
|2007||Tinbergen Institute, EUR||Ph.D.|
|2012 - Present||BI Norwegian Business School||Researcher|
|2007 - Present||Norges Bank||Senior Researcher|