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,...

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 provides a comprehensive nonlinear analysis of asymmetric adjustment of the dynamic relationship between energy intensity and urbanization using the time series data of 1978–2010 in China at both the national and the macro regional levels. Two sets of unit root tests are applied...

In this paper, the long memory properties of disaggregated fossils, coal and electricity retail consumption in the U.S. over the 1989–2009 period are examined. The presence of long memory is related to autocorrelation persistence of each series. Our results show that there is heterogeneity in...

This paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be summarized as follows. First, we provide evidence...

This paper examines the effect of structural breaks on the spot–futures oil prices relationship. We explore the impact of structural breaks on four critical issues, including cointegrating relationships, market efficiency under the expectation hypothesis and the no arbitrage rule, causalities,...