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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: The results indicate that oil price variations affect the Saudi stock market, and the proposed RNN model produces the next-day trading signal with 55% accuracy.
Abstract: This study tests the Saudi stock market weak form using the weak form of an efficient market hypothesis and proposes a recurrent neural network (RNN) to produce a trading signal. To predict the next-day trading signal of several shares in the Saudi stock market, we designed the RNN with a long short-term memory architecture. The network input comprises several time series features that contribute to the classification process. The proposed RNN output is fed to a trading agent that buys or sells shares based on the share current value, current available balance, and the current number of shares owned. To evaluate the proposed neural network, we used the historical oil price data of Brent crude oil in combination with other stock features (e.g., previous day (opening and closing price of the evaluated share). The results indicate that oil price variations affect the Saudi stock market. Furthermore, with 55% accuracy, the proposed RNN model produces the next-day trading signal. For the same period, the proposed RNN trading method achieves an investment gain of 23%, whereas the buy-and-hold method obtained 1.2%.
2 citations
01 Jan 2016
TL;DR: In this article, the authors focus on capturing the best representation of the main drivers behind SGP movements as a sensible step towards a more complex modelling exercise to explain Spanish gas pricing mechanics, and seek to better understand longterm persistence properties of SGP to obtain a view of how and to what extent those are transmitted through links with other primary energy commodities.
Abstract: This study expands on previous research on Spanish gas prices by investigating on the nature of the existing relationships with its main determinants and with special attention to Brent oil price relationship. The study focus on capturing the best representation of the main drivers behind SGP movements as a sensible step towards a more complex modelling exercise to explain Spanish gas pricing mechanics. In addition the analysis does also seek to better understand long-term persistence properties of SGP to obtain a view of how and to what extent those are transmitted through links with other primary energy commodities. Results from our investigation show that when comparing the different lags of Brent oil prices fitting normalized gas prices, the proxy best representing crude oil price is close to a Brent price lagging six months with validity for the next three months. Results for generic unit root tests indicate that all the series analysed are stationary in first differences logarithm what would open scope for using cointegration methods to study SGP long-run dynamics in the future.
2 citations
Posted Content•
TL;DR: In this paper, the authors developed real-time forecasting models for the price of Brent crude oil, which has become increasingly accepted as the best measure of the global price of oil in recent years.
Abstract: Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions. First, we address a number of econometric and data issues specific to real-time forecasts of quarterly oil prices. Second, we develop real-time forecasting models not only for U.S. benchmarks such as West Texas Intermediate crude oil, but we also develop forecasting models for the price of Brent crude oil, which has become increasingly accepted as the best measure of the global price of oil in recent years. Third, we design for the first time methods for forecasting the real price of oil in foreign consumption units rather than U.S. consumption units, taking the point of view of forecasters outside the United States. In addition, we investigate the costs and benefits of allowing for time variation in vector autoregressive (VAR) model parameters and of constructing forecast combinations. We conclude that quarterly forecasts of the real price of oil from suitably designed VAR models estimated on monthly data generate the most accurate forecasts among a wide range of methods including forecasts based on oil futures prices, no-change forecasts and forecasts based on regression models estimated on quarterly data.
2 citations
TL;DR: In this paper, the Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Auto-GARCH model were applied to the same problem.
Abstract: The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to...
2 citations
TL;DR: In this paper , the authors provided an in-depth analysis of forecasting ability of different GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and finding the best GARCH model for VaR estimation for crude oil.
Abstract: This paper aims at providing an in-depth analysis of forecasting ability of different GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and finding the best GARCH model for VaR estimation for crude oil. Analysis of VaR forecasting performance of different GARCH models is done using Kupiecs POF test, Christoffersens test and Backtesting VaR Loss Function. Crude oil is one of the most important fuel sources and has contributed to over a third of the world’s energy consumption. Oil shocks have influence on macroeconomic activities through various ways. Sharp oil price changes delay business investment because they raise uncertainty thus reducing aggregate output for some time. Analysis of crude oil prices trends is instrumental in informing the economy’s policy and decision making. Continued development and improvement of models used in analyzing prices improve forecasting accuracy which in turns leads to better costs and revenue prediction by businesses. The study uses Brent Crude Oil prices data over a period of ten years from the year 2011 to 2020. The study finds that the IGARCH T-distribution model is the best model out of the five models for VaR estimation based on LR.uc Statistic (0.235) and LR.cc Statistic (0.317) which are the least among the values realized. ME and RMSE for the five models used for forecasting have negligible difference. However, the IGARCH model stands out with IGARCH T-distribution being the best out of the five models in this study with ME of 0.0000963591 and RMSE of 0.05304335. We therefore conclude that the IGARCH T-distribution model is the best model out of the five models used in this study for forecasting Brent crude oil price volatility as well as for VaR estimations.
2 citations