The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the...

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks - Brent in Europe and WTI in America are compared. OVX index is...

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...

Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing...

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...

The present work attempts to evaluate the advantages inherent to the use of exogenous variables highly correlated to the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving average types incorporating seasonal treatment and exogenous...

This research uses annual time series data on CO2 emissions in India from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Our diagnostic tests indicate that India CO2 emission data is I (2). The study presents the ARIMA (2, 2, 0) model. The diagnostic tests...