In this paper, we forecast energy market volatility using both univariate and multivariate GARCH-class models. First, we forecast volatilities of individual assets and find that multivariate models display better performance than univariate models. Second, we forecast crack spread volatility and...

We investigate volatility models and their forecasting abilities for three types of petroleum futures contracts traded on the New York Mercantile Exchange (West Texas Intermediate crude oil, heating oil #2, and unleaded gasoline) and suggest some stylized facts about the volatility of these...

An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations,...

We study the empirical performance of the classical minimum-variance hedging strategy, comparing several econometric models for estimating hedge ratios of crude oil, gasoline and heating oil crack spreads. Given the great variability and large jumps in both spot and futures prices, considerable...

Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead...

This paper deals with the analysis of two observed features in historical oil price data; in particular, persistence and cyclicity. Using monthly data from September 1859 to October 2013, we observe that the series presents two peaks in the spectrum, one occurring at the long run or zero...

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains...

This study examines the degree of time persistence in U.S. disaggregated renewable energy consumption (hydropower, geothermal, solar, wind, wood, waste, and biofuels) using innovative fractional integration and autoregressive models with monthly data for the period 1994:2 to 2011:10. The results...