Blar i Scientific articles på forfatter "Ravazzolo, Francesco"

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

      Foroni, Claudia; Ravazzolo, Francesco; Rossini, Luca (Peer reviewed; Journal article, 2023)
      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, ...

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

      Gianfreda, Angelica; Ravazzolo, Francesco; Rossini, Luca (Journal article; Peer reviewed, 2020)
      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 ...

    • Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach 

      Caporin, Massimiliano; Gupta, Rangan; Ravazzolo, Francesco (Peer reviewed; Journal article, 2021)
      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) ...

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

      Casarin, Roberto; Grassi, Stefano; Ravazzolo, Francesco; van Dijk, Herman K. (Peer reviewed; Journal article, 2023)
      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 ...

    • Forecasting consumer confidence through semantic network analysis of online news 

      Fronzetti Colladon, Andrea; Grippa, Francesca; Guardabascio, Barbara; Costante, Gabriele; Ravazzolo, Francesco (Peer reviewed; Journal article, 2023)
      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 ...

    • Forecasting cryptocurrencies under model and parameter instability 

      Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco (Journal article; Peer reviewed, 2019)
      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, ...

    • Forecasting electricity prices with expert, linear, and nonlinear models 

      Billé, Anna Gloria; Gianfreda, Angelica; Del Grosso, Filippo; Ravazzolo, Francesco (Peer reviewed; Journal article, 2022)
      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 ...

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

      Fronzetti Colladon, Andrea; Grassi, Stefano; Ravazzolo, Francesco; Violante, Francesco (Peer reviewed; Journal article, 2022)
      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 ...

    • Identification of financial factors in economic fluctuations 

      Furlanetto, Francesco; Ravazzolo, Francesco; Sarferaz, Samad (Journal article; Peer reviewed, 2017)
      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 ...

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

      Bashiri Behmiri, Niaz; Fezzi, Carlo; Ravazzolo, Francesco (Peer reviewed; Journal article, 2023)
      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 ...

    • Large Time-Varying Volatility Models for Hourly Electricity Prices* 

      Gianfreda, Angelica; Ravazzolo, Francesco; Rossini, Luca (Peer reviewed; Journal article, 2022)
      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 ...

    • Markov switching panel with endogenous synchronization effects 

      Agudze, Komla M.; Billio, Monica; Casarin, Roberto; Ravazzolo, Francesco (Journal article; Peer reviewed, 2021)
      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 ...

    • A multivariate dependence analysis for electricity prices, demand and renewable energy sources 

      Durante, Fabrizio; Gianfreda, Angelica; Ravazzolo, Francesco; Rossini, Luca (Peer reviewed; Journal article, 2022)
      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 ...

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

      Galdi, Giulio; Casarin, Roberto; Ferrari, Davide; Fezzi, Carlo; Ravazzolo, Francesco (Peer reviewed; Journal article, 2023)
      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 ...

    • Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions 

      Iacopini, Matteo; Ravazzolo, Francesco; Rossini, Luca (Peer reviewed; Journal article, 2022)
      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 ...

    • A scoring rule for factor and autoregressive models under misspecification 

      Ravazzolo, Francesco; Casarin, Roberto; Corradin, Fausto; Sartore, Domenico (Journal article; Peer reviewed, 2020)
      Factor models (FM) are now widely used for forecasting with large set of time series. Another class of models, which can be easily estimated and used in a large dimensional setting, is multivariate autoregressive models ...

    • Short-term hydropower optimization driven by innovative time-adapting econometric model 

      Avesani, Diego; Zanfei, Ariele; Di Marco, Nicola; Galletti, Andrea; Ravazzolo, Francesco; Righetti, Maurizio; Majone, Bruno (Peer reviewed; Journal article, 2022)
      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 ...

    • The bank-sovereign nexus: Evidence from a non-bailout episode 

      Caporin, Massimiliano; Natvik, Gisle James; Ravazzolo, Francesco; Santucci de Magistris, Paolo (Journal article; Peer reviewed, 2019)
      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 ...

    • Time-varying combinations of predictive densities using nonlinear filtering 

      Billio, Monica; Casarin, Roberto; Ravazzolo, Francesco; Dijk, Herman K. van (Journal article; Peer reviewed, 2013)
      We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several speci cations of multivariate time-varying ...