Showing papers on "Brent Crude published in 2018"

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Abstract: Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

Modelling the Relationship between Crude Oil and Agricultural Commodity Prices

Abstract: textThe food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets, which is a novel contribution. For the period January 2000 - July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000 - July 2006; (ii) August 2006 - April 2013; and (iii) May 2013 - July 2018. The Structural Vector Autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities

The predictive content of CBOE crude oil volatility index

Abstract: Volatility forecasting is an important issue in the area of econophysics. The information content of implied volatility for financial return volatility has been well documented in the literature but very few studies focus on oil volatility. In this paper, we show that the CBOE crude oil volatility index (OVX) has predictive ability for spot volatility of WTI and Brent oil returns, from both in-sample and out-of-sample perspectives. Including OVX-based implied volatility in GARCH-type volatility models can improve forecasting accuracy most of time. The predictability from OVX to spot volatility is also found for longer forecasting horizons of 5 days and 20 days. The simple GARCH(1,1) and fractionally integrated GARCH with OVX performs significantly better than the other OVX models and all 6 univariate GARCH-type models without OVX. Robustness test results suggest that OVX provides different information from as short-term interest rate.

Analysis of the impact of crude oil price fluctuations on China’s stock market in different periods—Based on time series network model

Abstract: This paper studies the influence of Brent oil price fluctuations on the stock prices of China’s two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co-movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse-graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network-oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co-movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series.

Asian Spot Prices for LNG and Other Energy Commodities

Abstract: We investigate the relationship between the Japan-Korea Marker (JKM) price of LNG, which has become more important as spot trading of LNG has increased, and spot prices of Brent oil, fuel oil and thermal coal in Asia. We find that the JKM price appears to reflect inter-fuel competition in Asia. In this respect, it could be better than oil or other spot natural gas prices as a reference price for indexing long-term LNG contracts in Asia. The JKM may also be suitable for underpinning the development of an LNG pricing hub in Asia with associated derivatives markets.

Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis

Abstract: This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries’ and emerging market countries’ stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries’ stock returns to MSCI emerging countries’ stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries’ stock returns to the MSCI emerging countries’ stock returns is detected. In addition, G7 countries’ stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries’ stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets’ stock returns—especially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets.

Brent prices and oil stock behaviors: evidence from Nigerian listed oil stocks

Abstract: Given the shale oil glut that culminated in the most recent and continuing oil price drop from June 2014 and the global financial crisis of 2008 that triggered a cyclical downturn in oil prices and stock market activity, this study investigates the impact of Brent oil price shocks on oil related stocks in Nigeria. This study uses a vector autoregressive (VAR) model with the impulse response function and the forecast variance decomposition error. The empirical evidence reveals that oil price shocks have a negative impact on Nigerian oil and gas company stocks. In theory, this situation should apply to oil importing countries and is therefore uncharacteristic of an oil exporting country like Nigeria. The findings suggest that oil companies operating in Nigeria should diversify their investments to protect their business from single-sector market forces, and can also embrace the advantages of outsourcing some of their operations to specialist providers to increase flexibility and reduce operating costs. Finally, for vertically integrated oil and gas companies, oil price hedging and energy risk management will be beneficial because it will mean that these companies will take a position in the crude oil futures market. This will allow for better cash flow management and flexibility. This study extends the existing literature in two distinct ways. First, it provides, to the best of our knowledge, the first examination of the impact of oil price shocks on stock market activities with a focus on the market returns of oil and gas companies listed in the Nigerian Stock Exchange. Second, this study uses daily data because high frequency data contain more information than lower frequency data does, and lower frequency data average out too much important information.

The physical market and the WTI/Brent price spread

Abstract: West Texas Intermediate (WTI) and Brent Crude are primary benchmarks in oil pricing. Although produced in different locations, WTI and Brent are of similar quality and are used for similar purposes. Under the oil market globalisation assumption (Weiner, 1991), prices of crude oils with the same quality should move closely together at all times. However, empirical evidence shows that notable variations exist in the WTI/Brent spread, particularly after 2010, creating risks as well as potential arbitrage opportunities for oil market participants. The paper analyses the dynamics of WTI/Brent price spread for the period between January 1994 and December 2016. A test for structural breaks in the WTI/Brent price spread indicates a change from a stationary to a non‐stationary time series in December 2010, which is also confirmed by the unit root and cointegration tests. The impact of physical market fundamentals on the dynamics of WTI/Brent price spread is then analysed using the Structural Vector Autoregression Model for each sub‐sample period separated by the structural break. Impulse response functions show that the WTI/Brent spread is mainly driven by US production shocks.

Sustainability and Competitiveness in Thai Rubber Industries

Abstract: This article assesses the economic, legal and environmental conditions that Thai rubber farmers face and evaluates actions they can take to increase incomes. Statistical analyses determine relationships between prices of oil, natural and synthetic rubber. Pearson correlation tests found a strong positive relationship (r = 0.887) between the price of Brent crude and Thai ribbed smoked sheets, and a moderate positive relationship between price changes in Brent and synthetic rubber (r = 0.648). Regression analysis showed Brent oil price is a good predictor of natural rubber price. Moderate to strong positive relationships were also found between natural rubber price and the gross domestic products of Japan, China and the United States. Criminal antitrust behaviour in the rubber industries appeared to interfere with normal pricing in rubber markets. No significant bivariate correlation was found between rainfall in Thailand and natural rubber price, production or export, although fl ooding and other environmental issues clearly affected rubber farms. A survey of options showed that Thai rubber farmers can best improve their livelihoods through the collective purchase and use of new technologies and by integrating into downstream supply chain industries. At the very least, farmers are urged to abandon monocrop methods and supplement their incomes with fruit, fi sh, livestock or pigs.

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

Abstract: This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

The Impact of Oil Price Shocks on Sector Indices: Evidence from Borsa IStanbul

Abstract: We analyze the dynamic relationship between daily Brent oil prices and selected sector index returns of Borsa Istanbul. To perform an elaborate analysis, because oil price fluctuations affect sectors differently, the sectoral index returns are classified as oil-user, oil-related, oil-substitute, and financial. Employing Johansen and Juselius (1990) cointegrating technique, the long-run relationship is examined between the oil price changes and sectoral stock returns. After the investigation of the causal relationship between these two variables, Impulse Response Functions and Variance Decomposition Analysis are used to evaluate how shocks to variables rebound through a system. Given that significant changes have occurred across capital markets throughout the period, it would appear to be worthwhile to investigate whether changes in interactions among oil prices and sectoral stock returns have occurred as a result. The findings indicate that; there is cointegration between returns of half of the sectoral indices analyzed and oil prices Granger causes sectoral index returns.

Ensemble Incremental Random Vector Functional Link Network for Short-term Crude Oil Price Forecasting

Abstract: In this paper, an ensemble incremental learning model composed of Empirical Mode Decomposition (EMD), Random Vector Functional Link network (RVFL) and Incremental RVFL is presented in this work. First of all, EMD is employed to decompose the historical crude oil price time series. Then each sub-signal is modeled by an RVFL model to generate the corresponding forecast IMF value. Finally, the prediction results of all IMFs are combined to formulate an aggregated output for crude oil price. By introducing incremental learning, along with EMD based ensemble methods into RVFL network, the forecasting performance can be significantly improved with respect to both efficiency and accuracy. The crude oil price datasets from West Texas Intermediate (WTI) and Brent oil are used to test the effectiveness of the proposed EMD-Incremental-RVFL method. Simulation results demonstrated attractiveness of the proposed method compared with seven benchmark methods including long short-term memory (LSTM) network, especially based on fast computation speed.

Modeling brent oil price with markov chain process of the fuzzy states

Abstract: Purpose - The rapid change of crude oil price in the international market has attacted several investors into examining price fluctuations. The estimation regarding to the exact monthly price of the brent oil has always been a diffucult task in the business sector. Methodology - In this study , the directions of the monthly Brent oil prices from January 2003 to January 2017are analyzed using the Markov Chains of Fuzzy States technique. In the first instance, the data are classified into twenty-one fuzzy states, and then calculated the probability transition matrix of the fuzzy states for the given period. Findings- The directions of the monthly Brent oil prices are analyzed with transition matrix. Next the steady condition of the Brent oil return is obtained. These results give valuable information to decision makers regarding the investment opportunities of Brent oil for the short and long term marketing strategies. Conclusion- In crucial months, when a monthly return increases or decreases significantly, the proceeding month’s expected return also increase or decreases significantly. The proposed model can be used to estimate short term returns (one day) and also employing several fuzzy sets may give more investment opportunities.

Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

Abstract: Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

Bidirectional spillover effect between Russian stock index and the selected commodities

Abstract: This paper investigates shock and volatility spillover effect between Russian index RTS and six futures commodities (Brent oil, natural gas, gasoline, gold, platinum and palladium), observing joint time-frequency domain via wavelet decomposed series. Due to the fact that our time-span of almost 16 years is permeated with tranquil and crisis periods, we divided full-sample into three subsamples – before, during and after World financial crisis (WFC) via modified ICSS algorithm. We find that spillover effects happen mostly from the commodity markets toward RTS index in all three subsamples. However, during relatively calm periods (first and third sub-periods), spillover effects are very moderate and they occur in relatively few wavelet scales, which points that duration of these effects is limited in peaceful times. On the other hand, duration of spillover effects and its intensity increased during WFC. Also, wavelet coherence indicates that there are areas of stronger co-movements in period of WFC at higher wavelet scales for pairs such as RTS-Brent, -gasoline and -platinum. Commodities that have the strongest transmission effect on RTS index are Brent oil, gasoline and palladium, while gold has strong volatility transmission only during WFC.

Are Oil Industry Mergers Becoming Less Profitable

Abstract: Are oil industry mergers becoming less profitable? This study evaluates oil industry consolidations that occur during the sixteen-year time frame between 1998 and 2013 to find out. This quantitative study focuses on the stock price total return performance of acquirer companies over a four year horizon for each merger transaction. The portfolios created from these transactions provide for an analysis of the economics of the mergers after full integration of target companies. Four benchmarks are incorporated to provide various economic adjustment factors. There are seven cases presented that show that oil industry mergers are becoming less profitable. Implications are that companies may chase mergers as an easy way to increase returns, but this may not occur. As ever larger companies chase the remaining players and bid up their selling prices, increased returns may not always be the outcome.

Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities

Abstract: This study examines the out-of-sample value-at-risk forecasting performance of the GARCH, FIGARCH, HYGARCH and FIAPARCH models for West Texas intermediate crude oil, Europe Brent crude oil, heating oil#2, propane and New York Harbour Conventional Gasoline regular under the standard normal, Student's t and skewed Student's t distribution assumptions. Additionally, the expected shortfall is calculated in all cases. The results clearly show that the HYGARCH model under the normal distribution is the most accurate for short trading positions, whereas the FIGARCH model under the Student's t distribution is preferred for long trading positions. This further implies that it is important to consider downside and upside risk separately to obtain more accurate results.