Brent Crude

About: Brent Crude is a research topic. Over the lifetime, 548 publications have been published within this topic receiving 9879 citations.

The relationship between European Brent crude oil price development and US macroeconomy

Abstract: Abnormal volatility has a damaging effect on the macroeconomy and is seen as a measure of risk in asset and commodity markets. This investigation had the aim to analyze the supposed transatlantic volatility inducing effect of the most prominent scheduled macroeconomic news announcements from the United States (US) on Brent Blend crude oil price intraday volatility over a period of seven years from 2012 to 2018. The objective was to generate a ranking list of scheduled US macroeconomic news that forecast high intraday volatility episodes at precise points in time. A total of 38 US news was analyzed using a data mining workflow. Data modeling was conducted using a simple ordinary least squares regression model and performed with programming language Python. A one hour window of rolling standard deviation based on one minute high-frequency closing prices were applied. As a result, 20 scheduled US macroeconomic news was successfully identified to significantly impact Brent crude oil price volatility. The model strongly supports the forecast of high price fluctuations and provides an opportunity for market players to adjust their risk management strategies right in time.

Multiscale Hedging with Crude Oil Futures Based on EMD Method

Abstract: Studying the impact of the different components in data on hedging can provide valuable guidance to investors. However, the previous multiscale hedging studies do not examine the issue from the data itself. In this study, we use the empirical mode decomposition (EMD) method to reconstruct the crude oil futures and spot returns into three different scales: short-term, medium-term, and long-term. Then, we discuss the crude oil hedging performance under the dynamic minimum-CVaR framework at different scales. Based on the daily prices of Brent crude oil futures contract from August 18, 2005, to September 16, 2019, the empirical results show that the extracted scales comprise different information of original returns, short-term information occupies the most important position, and hedging is mainly driven by short-term information. Besides, hedging relying on long-term information has the best hedging performance. Removing some information related to short-term noise from the original returns is helpful for investors.

Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?

Abstract: We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons.

Short-Term Fuzzy Forecasting of Brent Oil Prices

Abstract: Oil prices movements is very important macroeconomic factor for decision making. The accuracy of results fordifferent types of oil brands depends on models and algorithms. This paper evaluates the effectiveness of usingfuzzy sets to forecast daily Brent oil prices. It also contains possible modifications of the proposed method and incomparison with basic methods. The results suggest that Brent oil prices series have short memory because usinginformation about last 2-days prices shows better forecast accuracy. Forecasting based on fixed universe ofdiscourse shows better efficiency and it also proves that oil prices series has short memory. Adding theprobability of switching between linguistic terms in defuzzification function could be used to improve accuracyof predictions. Also the approach can take into consideration expert’s opinion about direction of future variation.The effective expert’s work can reduce errors of forecast from 1.5% till 0.76%. But this modification can be usedif experts correctly guess the direction of the change in trend in eight out of ten cases and more. The reasonableobtained results can be used by analysts dealing with the prediction of oil prices.