Papers by Angelica Gianfreda
Oxford Bulletin of Economics and Statistics, Nov 30, 2022
We study the importance of time‐varying volatility in modelling hourly electricity prices when fu... more 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.
As the European Commission emphasizes, the world\u2014and Europe in particular\u2014needs to step... more As the European Commission emphasizes, the world\u2014and Europe in particular\u2014needs to step up its investment in energy efficiency and renewable technologies, and the development of clean energy business models, embracing new opportunities and consumer empowerment brought about by digitization. However, the transition to a low-carbon and sustainable economy, e.g., by shifting generation to renewable energy-sources (RES), introducing demand-response (DR) programs, and enabling technologies, is a difficult and costly process. Due to the intermittent and unpredictable nature of wind and solar power, a massive introduction of RES can affect prices paid to procure balancing resources and, consequently, the costs charged to end users. On the demand side, the transition involves not only technologies but also policies, user practices, information sharing, and a behavioral change among electricity consumers. This Special Issue, therefore, seeks to contribute to the literature through cutting-edge and multi-disciplinary research that addresses the (socio-)economics of sustainable and renewable energy-systems. We invite papers on innovative scientific developments, sound case studies, as well as reviews
RePEc: Research Papers in Economics, Jul 2, 2020
We study the importance of time-varying volatility in modelling hourly electricity prices when fu... more 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 exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that by using regressors as fuels prices, forecasted demand and forecasted renewable energy is essential in order 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.
arXiv (Cornell University), Jan 3, 2018
This paper compares alternative univariate versus multivariate models, frequentist versus Bayesia... more This paper compares alternative univariate versus multivariate models, 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 are 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 VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting.
By using time series of hourly spot prices and volumes of four European electricity markets, we s... more By using time series of hourly spot prices and volumes of four European electricity markets, we show that the total traded volume has negligible impact in determining the volatility of the electricity prices. This result is robust to the dierent econometric techniques adopted, namely a GARCH specication and a linear regression on realized volatilities. Our main explanation for the absence of a positive relation between volume and volatility is the lack of trading in the market based on superior information. We also discuss other theoretical explanations based on models borrowed from nancial economics.
Oxford Bulletin of Economics and Statistics
We study the importance of time‐varying volatility in modelling hourly electricity prices when fu... more 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.
We study the importance of time-varying volatility in modelling hourly electricity prices when fu... more 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 exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that by using regressors as fuels prices, forecasted demand and forecasted renewable energy is essential in order 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.
Quantitative Finance, Sep 29, 2022
Social Science Research Network, Jun 1, 2012
RePEc: Research Papers in Economics, 2011
In the last few years we have observed deregulation in electricity markets and an increasing inte... more In the last few years we have observed deregulation in electricity markets and an increasing interest of price dynamics has been developed especially to consider all stylized facts shown by spot prices. Only few papers have considered the Italian Electricity Spot market since it has been deregulated recently. Therefore, this contribution is an investigation with emphasis on price dynamics accounting for technologies, market concentration and congestions. We aim to understand how technologies, concentration and congestions affect the zonal prices since these ones combine to bring about the single national price (prezzo unico d'acquisto, PUN). Hence, understanding its features is important for drawing policy indications referred to production planning and selection of generation sources, pricing and risk-hedging problems, monitoring of market power positions and finally to motivate investment strategies in new power plants and grid interconnections. Implementing Reg-ARFIMA-GARCH models, we assess the forecasting performance of selected models showing that they perform better when these factors are considered.
International series in management science/operations research, Sep 30, 2017
It has been shown that model risk has an important effect on any risk measurement procedures, hen... more It has been shown that model risk has an important effect on any risk measurement procedures, hence its proper quantification is becoming crucial especially in energy markets, where market participants face several kinds of risks (such as volumetric, liquidity, and operational risk). Therefore, relaxing the assumption of normality and using a wide range of alternative distributions, we quantify the model risk in the German wholesale electricity market (the European Energy Exchange, EEX) by studying day–ahead electricity prices from 2001 to 2013 using the well-established setting of GARCH–type models. Taking advantage of this long price history, we investigate the “time evolution” of the measured model risk across years by adopting a rolling window procedure. Our results confirm that the increasing complexity of energy markets has affected the stochastic nature of electricity prices which have become progressively less normal through years, hence resulting in an increased model risk.
Mathematical and Statistical Methods for Actuarial Sciences and Finance
Executive Summary This paper compares for the the first time to our best knowledge the forecastin... more Executive Summary This paper compares for the the first time to our best knowledge the forecasting performances of linear univariate and multivariate models for hourly day-ahead electricity prices. Our set of models includes AR and VAR models with only dummy variables for seasonality which are used as baseline for the corresponding formulations enlarged by including also forecasted demand and renewable energy generation, analysed from both the frequentist and the Bayesian perspective. The accuracy of point and density forecasts are inspected in four main European markets characterized by different levels of renewable energy power generation. The analysis of these performances covers all 24 hours from 2015 to 2016. The first important finding is that both AR and VAR specifications with demand and renewable energy dominate models without RES. Secondly, the Bayesian approach leads to improvements in the univariate but also (and especially) in the multivariate models. Thirdly, and for t...
Information Sciences, 2022
This paper examines the dependence between electricity prices, demand, and renewable energy sourc... more 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 timevarying dependencies of the involved variables.
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Papers by Angelica Gianfreda