The energy sector is one of the most important in the world, so that time series fluctuations in leading energy sources have been analysed widely. Much of the empirical analysis in the literature has concentrated on using daily, weekly or monthly data, with little research based on intra-day data. The intention of the paper is to analyse the relationships among the S&P 500 Index and futures prices, returns and volatility of three leading energy commodities, namely crude oil, natural gas and ethanol, using intra-day data. The detailed analysis of intra-day temporal aggregation in examining returns relationships and volatility spillovers across the equity and energy futures markets, and the effects of day-night returns, volume, realized volatility, asymmetry, and spillovers across the four financial markets, leads to interesting and useful empirical results for decision making and hedging strategies. The empirical results relating to alternative models of mean and variance feedback and asymmetry for intra-daily returns, asymmetry and volatility spillovers, and dynamic conditional correlations and covariances, show that the relationships between the stock market and alternative energy financial derivatives, specifically futures prices and returns, can and do vary according to the trading range, whether daily or day-night effects are considered, and the temporal aggregation and time frequencies that are used.

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Keywords Asymmetry, Crude oil futures, Day-night realized volatility, Day-night returns, Day-night volume, Ethanol futures, Intra-day prices and returns, Natural gas futures, S&P 500 index, Spillovers, Trading range
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22), Time-Series Models; Dynamic Quantile Regressions (jel C32), Financial Econometrics (jel C58), Asset Pricing (jel G12), International Financial Markets (jel G15)
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Journal International Review of Economics and Finance
Caporin, M, Chang, C-L, & McAleer, M.J. (2018). Are the S&P 500 index and crude oil, natural gas and ethanol futures related for intra-day data?. International Review of Economics and Finance. doi:10.1016/j.iref.2018.08.003