In this article we examine the properties of estimates of realized volatility at various intra-daily sampling frequencies for Brent Crude oil futures traded at the IntercontinentalExchange (ICE). The estimates of realized volatility are subsequently modeled and forecasted to predict day-ahead...

In this paper we propose a simple one-factor quantile regression model based on realized volatility to forecast Value-at-Risk (VaR). The model only uses daily realized volatility as input and thus simplifies estimation substantially compared with most other methodologies currently used to...

For fixed maturity, under the no-arbitrage assumption, futures prices should follow a martingale with respect to the trading time, at least under the pricing measure. Therefore, a prominent display of trading time seasonality under the physical measure raises warning signs and can only occur by...

This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of...