Francesco Ravazzolo

• Prof. dr.
• Professor (Associate) at Free University of Bozen-Bolzano

Bioenergy is seen as a renewable energy source expected to deliver a major contribution to decarbonization of power and heat by major institutions, although its affordability in specific contexts has not been assessed in detail by the available literature. In this study, we develop a techno-economic model of a small-scale decentralized biomass gasi...

This paper proposes a non-parametric test for Granger causality in quantiles to detect causal-ity from a high-frequency driver to a low-frequency target. In an economic application, we examine Granger causality between inflation, as a low-frequency macroeconomic variable, and a selection of commodity futures, including gold, oil, and corn, as high-...

In the Italian electricity market, we analyze the Aggregate Zonal Imbalance, which is the algebraic sum, changed in sign, of the amount of energy procured by the Italian national Transmission and System Operator in the Dispatching Services Market at a given time in the northern Italian electricity macro-zone. Specifically, we determine possible rel...

This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up t...

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. W...

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’ j...

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 predicto...

This study examines the impact of the Covid-19 pandemic on corporate financial performance using a unique, crosscountry and longitudinal sample of 3,350 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 b...

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 nonli...

For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to i...

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 ex...

This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of US macro and financial indicators, where the homoskedasticity assumption is relaxed to allow for time-varying volatility. In particular, we exploit the mixture representation of the multivariate asymmetric Laplace likelihood and the Cholesky-typ...

Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. However, neither theory nor estimation methods are currently available when the data present heavy tails or anomalous observations, which is often the case in applications. In...

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 inclu...

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 all...

This paper aims to analyze unemployment-generating supply shocks. It proposes a structural vector autoregressive model estimated via a newly assembled identification scheme that relies on a minimum set of sign restrictions dictated by economic theory and recent market developments. We show that unemployment-generating supply shocks coexist with sta...

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 schedu...

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 d...

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...

This paper provides an iterative model selection for forecasting day-ahead hourly electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources (RES), fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models...

This article studies the impact of online news on social and economic consumer perceptions through the application of semantic network analysis. Using almost 1.3 million online articles on Italian media covering a period of four years, we assessed the incremental predictive power of economic-related keywords on the Consumer Confidence Index. We tra...

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 interact...

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 potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the...

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...

The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics.

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. W...

We analyse the importance of low frequency hard and soft macroeconomic information, respectively the industrial production index and the manufacturing Purchasing Managers' Index surveys, for forecasting high-frequency daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, intr...

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 huge dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exp...

This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends 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 ACPS is of general use in any situation where the decision maker h...

This paper proposes world steel production as an indicator of global real economic activity. World steel production data is published with only a one‐month delay, thereby providing timely information for world real GDP forecasters. We find that world steel production and Lutz Kilian's (2009) index of global real economic activity generate large gai...

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,...

This chapter reviews different methods to construct density forecasts and to aggregate forecasts from many sources. Density evaluation tools to measure the accuracy of density forecasts are reviewed and calibration methods for improving the accuracy of forecasts are presented. The manuscript provides some numerical simulation tools to approximate p...

This paper develops a Dynamic Stochastic General Equilibrium (DSGE) model to evaluate the economic repercussions of cryptocurrency. We assume that cryptocurrency offers an alternative currency option to government currency for households and we have an endogenous supply and demand for cryptocurrency. We estimate our model with Bayesian techniques u...

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 no...

This paper investigates how investor sentiment affects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power on stock market returns. Concerning the Eur...

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 uni...

We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forec...

This paper compares alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, with and without renewable energy sources (RES). The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark...

We use a flexible Bayesian model averaging method to estimate a factor pricing model characterized by structural uncertainty and instability in macro-financial factor loadings and idiosyncratic risks. We propose such a framework to investigate key differences in the pricing mechanism that applies to residential versus nonresidential real estate inv...

We propose a Bayesian panel model for mixed frequency data, where parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. To estimate the model, we develop a Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution, and we assess its performance...

Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being “efficient”, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies’ vo...

Increased sovereign credit risk is often associated with sharp currency movements. Therefore, expectations of the probability of a sovereign default event can convey important information regarding future movements of exchange rates. In this paper, we investigate the possible pass-through of risk in the sovereign debt markets to currency markets by...

This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use of two econometric approaches based on quantile regressions (standard quantile regression and Bayesian quantile regression with heteroskedasticity)...

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...

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 r...

This article proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. publicly traded assets. The model assumes that risk exposures and idiosyncratic volatility follow a break-point latent process, allowing for changes at any point...

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility-based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecast...

The proposed panel Markov-switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time-varying transition matrices of country-specific Markov chains, allowing for interconnections. An efficient multi-move sampling algorithm draws time-varying Markov-switching chains. Using industrial production growth a...

We propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. First, Monte Carlo simulations show that predictive densities for the various MIDAS models derived from the block wild bootstrap approach are more accurate in terms of coverage rates than predictive densit...

In recent years several commentators hinted at an increase of the correlation between equity and commodity returns, blaming for that surging investment in commodity-related products. This paper investigates such claims by looking at various measures of correlation, and assesses what are the implications of this for asset allocation. We develop a ti...

We define and forecast classical business cycle turning points for the Norwegian economy. When defining reference business cycles, we compare a univariate and a multivariate Bry–Boschan approach with univariate Markov-switching models and Markov-switching factor models. On the basis of a receiver operating characteristic curve methodology and a com...

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using...

Challenging statements have appeared in recent years in the literature on advances in computational procedures.[...]

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and o...

We carry out a pseudo out-of-sample density forecasting study for US GDP with an autoregressive benchmark and alternatives to the benchmark that include both oil prices and stochastic volatility. The alternatives to the benchmark produce superior density forecasts. This comparative density performance appears to be driven more by stochastic volatil...

Recent evidence highlights that commodity price changes exhibit a short-lived, yet robust contemporaneous effect on commodity currencies, which is mainly detectable in daily-frequency data. We use MIDAS models in a Bayesian setting to include mixed-frequency dynamics while accounting for time-variation in predictive ability. Using the random walk M...

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and v...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite b...

A long strand of literature has shown that the world has become more global. Yet, the recent Great Global Recession turned out to be hard to predict, with forecasters across the world committing large forecast errors. We examine whether knowledge of in-sample co-movement across countries could have been used in a more systematic way to improve fore...

This paper analyzes the sovereign risk contagion using credit default swaps (CDS) and bond premiums for the major eurozone countries. By emphasizing several econometric approaches (nonlinear regression, quantile regression and Bayesian quantile regression with heteroskedasticity) we show that propagation of shocks in Europe's CDS has been remarkabl...

We define and forecast classical business cycle turning points for the Norwegian economy. When defining reference business cycles, we compare a univariate and a multivariate Bry–Boschan approach with univariate Markov-switching models and Markov-switching factor models. On the basis of a receiver operating characteristic curve methodology and a com...

A long strand of literature has shown that the world has become more global. Yet, the recent Great Global Recession turned out to be hard to predict, with forecasters across the world committing large forecast errors. We examine whether knowledge of in-sample co-movement across countries could have been used in a more systematic way to improve fore...

In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian’s index of global real economic activity and the index of OECD World industrial production. We develop an econometric...