Employing the MS-ARJI-GJR-GARCH-X model, in which the parameters for the jump process, the asymmetric GARCH effect and the impacts of oil price shocks are regime-dependent, this paper analyzes the impact of crude oil price shock on stock return dynamics. Empirical results reveal three...

In this comprehensive empirical study we critically evaluate the use of forecast averaging in the context of electricity prices. We apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our...

In this paper, we forecast excess stock returns of S&P 500 index from January 1997 to December 2012 using both well-known traditional macroeconomic indicators and oil market variables. Based on a dynamic model selection approach, we find that the forecasting accuracy can be improved after adding...

This paper examines risk transmission and migration among six US measures of credit and market risk during the full period 2004–2011 period and the 2009–2011 recovery subperiod, with a focus on four sectors related to the highly volatile oil price. There are more long-run equilibrium risk...

Oil prices increased dramatically during 2004–2006. Industry experts initially attributed these price increases to fundamental factors such as the rise in global demand, but also because of disruptions in the supply of oil. The price increases however were so substantial that additional...

Prior research has shown that energy sector stock prices are impacted by uncertainty. The coronavirus (COVID-19) pandemic has given rise to widespread health and economic-related uncertainty. In this study, we investigate the impact and the timing of the impact of COVID-19 related uncertainty on...

Stochastic process models of commodity prices are important inputs in energy investment evaluation and planning problems. In this paper, we focus on modeling and forecasting the long-term price level, since it is the dominant factor in many such applications. To provide a foundation for our...

This work discusses potential pitfalls of applying linear regression models for explaining the relationship between spot and futures prices in electricity markets, in particular, the bias coming from the simultaneity problem, the effect of correlated measurement errors and the impact of...

We propose using Realized GARCH-type models to estimate the daily price volatility in the EPEX power markets. The model specifications extract the volatility-related information from realized measures, which improves the in-sample fit of the data. More importantly, evidence on the out-of-sample...

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...