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Brent Crude

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


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TL;DR: In this paper, the authors investigated the spillover effect from the Brent oil futures market to the 11 stock markets of Central and Eastern European economies using wavelet-based quantile parameters.
Abstract: This paper investigates the idiosyncratic volatility spillover effect from the Brent oil futures market to the 11 stock markets of Central and Eastern European economies. As volatility proxies, we use regime‐switching conditional volatilities, obtained from two‐states MS‐GARCH model. In order to determine the level of this effect in different market conditions and in different time‐horizons, we combine wavelet methodology with the quantile regression approach. Our results indicate that the volatility spillover effect is not particularly strong across the countries and the wavelet scales, except in those conditions when stock market volatility is exceptionally high. Also, the wavelet‐based quantile parameters report that the volatility transmission effect gradually subsides with the flow of time, and it applies for the majority of the indices. Romanian BET index experiences the strongest volatility spillover effect from oil in conditions when Romanian stock market is under extreme stress. The reason for this finding probably lies in the facts that Romania is the largest oil and gas producer among all CEECs, and oil and gas markets tend to comove strongly. Based on findings of wavelet quantile parameters and wavelet correlations, we can conclude that hedgers and portfolio managers can build their portfolio strategies, combining Brent oil futures with the CEE indices.

1 citations

Journal Article
TL;DR: In this article, the authors analyzed the interrelationship between Brent and Daqing oil price,BJ and Qinhuangdao steam coal price by using VAR model and Impulse response function.
Abstract: The paper analyses the interrelationship between Brent and Daqing oil price,BJ and Qinhuangdao steam coal price by using VAR model and Impulse response function.The result shows that there are long term equilibrium relationships between international and domestic energy prices.Brent oil price is the granger reason for steam coal prices and Daqing oil price.While BJ steam coal price and Daqing oil price is the granger reason for domestic coal price,and Daqing oil price is the granger reason for BJ steam coal price.The impulse between energy prices is positive and increases initially and decreases afterwards.The contribution of domestic oil price to domestic coal price equals to total contribution of international oil and steam coal price,which is nearly 42%.And domestic oil price has 3times impulse effect on BJ steam coal price than Brent oil price.While Brent oil price makes about 37%contribution to Daqing oil price fluctuation.This means that China,s energy market in a weak position in the international energy market.We need to strengthen energy prices early warning mechanism and energy reserves mechanisms.

1 citations

Posted Content
TL;DR: This article applied threshold cointegration regression to test whether price increases in Chinese stock index cause Brent crude oil index and found that stock price increases can be used as predictors for oil price increases.
Abstract: This paper tests whether price increases in Chinese stock index cause Brent crude oil index. We apply threshold cointegration regression. Our findings in the usual regime suggest that oil price increases do not tend to affect Chinese stock market but oil prices better explain stock returns and stock price increases. However, our findings in the unusual regime suggest that stock price increases can be used as predictors for oil price increases, but oil price increases poorly explain stock price increases. Therefore, our findings could shed lights on threshold cointegratted dynamics of price increases between Brent crude oil and Chinese stock markets.

1 citations

Posted Content
TL;DR: In this article, the authors tried to measure oil price uncertainty based on the conditional standard deviations which are derived from univariate (G)ARCH models and the measure of uncertainty they choose is the within-year high-low range of the Conditional Standard Deviation.
Abstract: In this paper we try to measure oil price uncertainty. The measure of uncertainty is based on the conditional standard deviations which are derived from univariate (G)ARCH models. The measure of uncertainty we choose is the within-year high-low range of the conditional standard deviations. It is likely that the higher the uncertainty, the higher the high-low range within a year will be. We focus on volatility of the price of a barrel Brent crude, over the period 5 January, 1982 to 23 April, 2002 representing 5296 observations. The preferred model is a symmetric GARCH(1,3) model. Asymmetric leverage effects are not found. We also examine the volatility in monthly time series for the period January, 1970 to April, 2002. For this time span and frequency we prefer the GARCH(1,1) model.

1 citations

Journal Article
TL;DR: In this article, the forecasting accuracy of four stochastic processes and four univariate random walk models using daily data of OPEC Reference Basket series is compared. And the study finds that the random walk univariate model outperforms the other stochiastic processes.
Abstract: Most academic papers on oil price forecasting have frequently focused on the use of WTI and European Brent oil price series with little focus on other equally important international oil price benchmarks such as the OPEC Reference Basket (ORB). The ORB is a weighted average of 11-member countries crude streams weighted according to production and exports to the main markets. This paper compares the forecasting accuracy of four stochastic processes and four univariate random walk models using daily data of OPEC Reference Basket series. The study finds that the random walk univariate model outperforms the other stochastic processes. An element of uncertainty was introduced into the point estimates by deriving probability distribution that describes the possible price paths on a given day and their likelihood of occurrence. This will help decision makers, traders and analysts to have a better understanding of the possible daily prices that could occur. JEL Classification Numbers : E64; C22; Q30 Keywords : Oil Price Forecasting, Probability Distributions, and Forecast Evaluation Statistics, Brownian Motion with Mean Reversion process, GARCH Models

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202346
202266
202162
202064
201952
201845