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Ansattprofil

Francesco Ravazzolo

Instituttleder - Institutt for datavitenskap og analyse

Biografi

Francesco Ravazzolo is Head of Department of Data Science and Analytics at BI Norwegian Business School. He is also Full Professor of Econometrics at Faculty of Economics and Management at Free University of Bozen-Bolzano and visiting Professor at Center for Applied Macro and commodity Prices at BI Norwegian Business School.
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.

Publikasjoner

Asimakopoulos, Stylianos; Lorusso, Marco & Ravazzolo, Francesco (2023)

A Bayesian DSGE approach to modelling cryptocurrency

Review of economic dynamics Doi: 10.1016/j.red.2023.09.006

We develop and estimate a DSGE model to evaluate the economic repercussions of cryptocurrency. In our model, cryptocurrency offers an alternative currency option to government currency, with endogenous supply and demand. We uncover a substitution effect between the real balances of government currency and cryptocurrency in response to technology, preferences and monetary policy shocks. We find that an increase in cryptocurrency productivity induces a rise in the relative price of government currency with respect to cryptocurrency. Since cryptocurrency and government currency are highly substitutable, the demand for the former increases whereas it drops for the latter. Our historical decomposition analysis shows that fluctuations in the cryptocurrency price are mainly driven by shocks in cryptocurrency demand, whereas changes in the real balances for government currency are mainly attributed to government currency and cryptocurrency demand shocks.

Galdi, Giulio; Casarin, Roberto, Ferrari, Davide, Fezzi, Carlo & Ravazzolo, Francesco (2023)

Nowcasting industrial production using linear and non-linear models of electricity demand

Energy Economics, 126 Doi: 10.1016/j.eneco.2023.107006 - Fulltekst i vitenarkiv

This article proposes different modelling approaches which exploit electricity market data to nowcast industrial production. Our models include linear, mixed-data sampling (MIDAS), Markov-Switching (MS) and MS-MIDAS regressions. Comparisons against autoregressive approaches and other commonly used macroeconomic predictors show that electricity market data combined with an MS model significantly improve nowcasting performance, especially during turbulent economic states, such as those generated by the recent COVID-19 pandemic. The most promising results are provided by an MS model which identifies two volatility regimes. These results confirm that electricity market data provide timely and easy-to-access information for nowcasting macroeconomic variables, especially when it is most valuable, i.e. during times of crisis and uncertainty.

Fronzetti Colladon, Andrea; Grippa, Francesca, Guardabascio, Barbara, Costante, Gabriele & Ravazzolo, Francesco (2023)

Forecasting consumer confidence through semantic network analysis of online news

Scientific Reports, 13(1) Doi: 10.1038/s41598-023-38400-6 - Fulltekst i vitenarkiv

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers’ judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

Bashiri Behmiri, Niaz; Fezzi, Carlo & Ravazzolo, Francesco (2023)

Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks

Energy, 278 Doi: 10.1016/j.energy.2023.127831 - Fulltekst i vitenarkiv

One of the most controversial issues in the mid-term load forecasting literature is the treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a few weeks ahead, researchers have, so far, implemented three approaches: a) excluding weather from load forecasting models altogether, b) assuming future weather to be perfectly known and c) including weather forecasts in their load forecasting models. This article provides the first systematic comparison of how the different treatments of weather affect load forecasting performance. We incorporate air temperature into short- and mid-term load forecasting models, comparing time-series methods and feed-forward neural networks. Our results indicate that models including future temperature always significantly outperform models excluding temperature, at all-time horizons. However, when future temperature is replaced with its prediction, these results become weaker.

Billé, Anna Gloria; Tomelleri, Alessio & Ravazzolo, Francesco (2023)

Forecasting regional GDPs: a comparison with spatial dynamic panel data models

Spatial Economic Analysis Doi: 10.1080/17421772.2023.2199034

The monitoring of the regional (provincial) economic situation is of particular importance due to the high level of heterogeneity and interdependences among different territories. Although econometric models allow for spatial and serial correlation of various kinds, the limited availability of territorial data restricts the set of relevant predictors at a more disaggregated level, especially for gross domestic product (GDP). Combining data from different sources at NUTS-3 level, this paper evaluates the predictive performance of a spatial dynamic panel data model with individual fixed effects and some relevant exogenous regressors, by using data on total gross value added (GVA) for 103 Italian provinces over the period 2000–2016. A comparison with nested panel sub-specifications as well as pure temporal autoregressive specifications has also been included. The main finding is that the spatial dynamic specification increases forecast accuracy more than its competitors throughout the out-of-sample, recognising an important role played by both space and time. However, when temporal cointegration is detected, the random-walk specification is still to be preferred in some cases even in the presence of short panels.

Miroshnychenko, Ivan; Vocalelli, Giorgio, De Massis, Alfredo, Grassi, Stefano & Ravazzolo, Francesco (2023)

The COVID-19 pandemic and family business performance

Small Business Economics Doi: 10.1007/s11187-023-00766-2

This study examines the impact of the COVID-19 pandemic on corporate financial performance using a unique, cross-country, and longitudinal sample of 3350 listed firms worldwide. We find that the financial performance of family firms has been significantly higher than that of nonfamily firms during the COVID-19 pandemic, accounting for pre-pandemic business conditions. This effect is pertinent to firms with strong family involvement in management or in both management and ownership. We also identify the role of firm-, industry-, and country-level contingencies for family business financial performance during the COVID-19 pandemic. This study offers a novel understanding of the financial resilience across different types of family business and sets an agenda for future research on the drivers of resilience of family firms to adverse events. It also provides important and novel evidence for policymakers, particularly for firms with different ownership and management structures.

Casarin, Roberto; Grassi, Stefano, Ravazzolo, Francesco & van Dijk, Herman K. (2023)

A flexible predictive density combination for large financial data sets in regular and crisis periods

Journal of Econometrics Doi: 10.1016/j.jeconom.2022.11.004 - Fulltekst i vitenarkiv

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel dynamic clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual US stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on dynamic cluster composition, weight patterns and model set incompleteness gives valuable signals for improved modelling. This enables higher predictive accuracy and better assessment of uncertainty and risk for investment fund management.

Foroni, Claudia; Ravazzolo, Francesco & Rossini, Luca (2023)

Are low frequency macroeconomic variables important for high frequency electricity prices?

Economic Modelling, 120 Doi: 10.1016/j.econmod.2022.106160

Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lags or renewable energy sources. However, the recent turmoil of energy prices and the Russian–Ukrainian war increased attention in evaluating the relevance of industrial production and the Purchasing Managers’ Index output survey in forecasting the daily electricity prices. We develop a Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in frequency between the daily prices and the monthly macro variables in Germany and Italy. We find that the inclusion of macroeconomic low frequency variables is more important for short than medium term horizons by means of point and density measures. In particular, accuracy increases by combining hard and soft information, while using only surveys gives less accurate forecasts than using only industrial production data.

Fronzetti Colladon, Andrea; Grassi, Stefano, Ravazzolo, Francesco & Violante, Francesco (2022)

Forecasting financial markets with semantic network analysis in the COVID-19 crisis

Journal of Forecasting Doi: 10.1002/for.2936 - Fulltekst i vitenarkiv

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-related keywords appearing in the text. The index assesses the importance of the economic-related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

Gianfreda, Angelica; Ravazzolo, Francesco & Rossini, Luca (2022)

Large Time-Varying Volatility Models for Hourly Electricity Prices*

Oxford Bulletin of Economics and Statistics Doi: 10.1111/obes.12532 - Fulltekst i vitenarkiv

We study the importance of time-varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well-known time series models in a large dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that using regressors as fuel prices, forecasted demand and forecasted renewable energy is essential to properly capture the volatility of these prices. Moreover, we show that the time-varying volatility models outperform the constant volatility models in both the in-sample model-fit and the out-of-sample forecasting performance.

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 - Fulltekst i vitenarkiv

The ongoing transformation of the electricity market has reshaped the hydropower production paradigm for storage reservoir systems, with a shift from strategies oriented towards maximizing regional energy production to strategies aimed at the revenue maximization of individual systems. Indeed, hydropower producers bid their energy production scheduling 1 day in advance, attempting to align the operational plan with hours where the expected electricity prices are higher. As a result, the accuracy of 1-day ahead prices forecasts has started to play a key role in the short-term optimization of storage reservoir systems. This paper aims to contribute to the topic by presenting a comparative assessment of revenues provided by short-term optimizations driven by two econometric models. Both models are autoregressive time-adapting hourly forecasting models, which exploit the information provided by past values of electricity prices, with one model, referred to as Autoarimax, additionally considering exogenous variables related to electricity demand and production. The benefit of using the innovative Autoarimax model is exemplified in two selected hydropower systems with different storage capacities. The enhanced accuracy of electricity prices forecasting is not constant across the year due to the large uncertainties characterizing the electricity market. Our results also show that the adoption of Autoarimax leads to larger revenues with respect to the use of a standard model, increases that depend strongly on the hydropower system characteristics. Our results may be beneficial for hydropower companies to enhance the expected revenues from storage hydropower systems, especially those characterized by large storage capacity.

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

This paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model while studying Germany, the widest studied market in Europe. The inter-dependencies are investigated in-depth and monitored over time, with particular emphasis on the tail behavior. To this end, suitable tail dependence measures are introduced to take into account a multivariate extreme scenario appropriately identified through the Kendall’s distribution function. The empirical evidence demonstrates a strong association between electricity prices, renewable energy sources, and demand within a day and over the studied years. Hence, this analysis provides guidance for further and different incentives for promoting green energy generation while considering the time-varying dependencies of the involved variables.

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

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.

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 - Fulltekst i vitenarkiv

This article 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 (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.

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 - Fulltekst i vitenarkiv

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

Advances in Decision Sciences, 24(2), s. 1- 38. Doi: 10.47654/v24y2020i2p66-103 - Fulltekst i vitenarkiv

Gianfreda, Angelica; Ravazzolo, Francesco & Rossini, Luca (2020)

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

International Journal of Forecasting, 36, s. 974- 986. Doi: 10.1016/j.ijforecast.2019.11.002 - Fulltekst i vitenarkiv

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

Journal of Empirical Finance, 53, s. 181- 196. Doi: 10.1016/j.jempfin.2019.07.001 - Fulltekst i vitenarkiv

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

Economic Journal, 129(617), s. 311- 337. Doi: 10.1111/ecoj.12520 - Fulltekst i vitenarkiv

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

International Journal of Forecasting, 35(2), s. 485- 501. Doi: 10.1016/j.ijforecast.2018.09.005 - Fulltekst i vitenarkiv

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

Clark, Todd E. & Ravazzolo, Francesco (2015)

Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility

Journal of applied econometrics, 30(4), s. 551- 575. Doi: 10.1002/jae.2379

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

Bernardi, Mauro; Grassi, Stefano & Ravazzolo, Francesco (1)

Bayesian Econometrics

Journal of Risk and Financial Management [Kronikk]

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.

Akademisk grad
År Akademisk institusjon Grad
2007 Tinbergen Institute, EUR Ph.D.
Arbeidserfaring
År Arbeidsgiver Tittel
2021 - Present BI Norwegian Business School Head of Department
2021 - Present Free University of Bozen-Bolzano Professor
2020 - Present AIAQUA Founder
2018 - 2021 Free University of Bozen-Bolzano Professor
2014 - 2021 BI Norwegian Business School Professor II
2016 - 2018 Free University of Bozen-Bolzano Associate Professor
2014 - 2015 Norges Bank Principal Researcher
2012 - 2014 BI Norwegian Business School Researcher
2007 - 2014 Norges Bank Senior Researcher