Oil Price Model Research Articles

Can the green bond market enter a new era under the fluctuation of oil price?

This paper investigates how oil price (OP) influences the prospects of green bonds by utilising the quantile-onquantile (QQ) method and researching the interactions between OP and green bond index (GBI) from 2011:M1 to 2021:M11. We find that impacts from OP on the GBI are positive in the short run. The positive effects indicate that high OP can promote the development of the green bond market, indicating that green bonds can be considered an asset to avoid OP shocks. However, in the medium and long term, there is a negative impact due to the oversupply of the oil market and the increase in green energy industry profits. These results are identical to the supply and demand-based correlation model of green bonds and oil price, which underlines a specific effect of OP on GBI. The GBI effect on OP is consistently positive across all quantiles. It indicates that green bonds cannot be considered efficient measures to alleviate the oil crisis due to the instability of the Middle East COVID-19 and the small scale of green bonds. The issuers of green bonds can make decisions based on OP. Understanding the relationship between OP and GBI is also beneficial for investors.

Hybrid Feature Reduction Using PCC-Stacked Autoencoders for Gold/Oil Prices Forecasting under COVID-19 Pandemic

The financial markets have been influenced by the emerging spread of Coronavirus disease, COVID-19. The oil, and gold as well have experienced a downward trend due to the increased rate in the number of confirmed COVID-19 cases. Lately, the published COVID data comprised new variables that may influence the accuracy of the oil/gold prices forecasting models including the Stringency index, Reproduction rate, Positive Rate, and Vaccinations. In this study, Deep Autoencoders are introduced and combined with the well-known approach: Pearson Correlation Coefficient, PCC, analysis in selecting the key features that affect the accuracy of the forecasting models of gold and oil prices with respect to COVID-19 pandemic. We have utilized a hybrid approach of PCC along with a 2-Stage Stacked Autoencoder, SA, to extract the latent features which are then submitted to Neural Network, NN, regression model. The NN regressor has been trained using the Bayesian Regularization-backpropagation algorithm which provides a good generalization for small noisy datasets. The hybrid approach has yielded the minimum MSE values of 8.97 × 10−3 and 5.356 × 10−2 on the oil/gold validation set, respectively. Compared to the existing approaches, the proposed approach has outperformed the ARIMA, ML based regression models in forecasting the oil/gold prices. In addition, the introduced framework has yielded lower Mean Absolute Error, MAE, than the Recurrent Neural Network, RNN, and the Principal Component Analysis, PCA, for dimension reduction. The retrieved results showed that the hybrid method produced more robust features by considering the relationship between the input features.

Islamic Stock indices and COVID-19 pandemic

Motivated by the COVID-19 pandemic, we construct a single factor predictive model for stock returns that incorporates uncertainty index for pandemics and epidemics (UPE). Specifically, we examine whether Islamic stocks are either vulnerable or have better hedge potential when compared to the performance of their conventional counterparts. In general, we find that the Islamic stocks can be used to hedge whereas the conventional stocks are seen to be vulnerable to uncertainty due to pandemics across different time periods. In particular, during COVID-19 pandemic, although the hedging effectiveness of Islamic stock seems to decline, it is still better compared to the worse performance of the conventional stocks. The outcome remains the same even after controlling our model for oil price, geopolitical risk and economic policy uncertainty. We further evaluate the predictive power of the UPE both for the in-sample and out-of-sample periods by comparing its forecast performance with that of a benchmark model. Our results suggest that the consideration of the UPE information in the valuation of stocks is crucial for investment decisions.

A Sentiment-Enhanced Hybrid Model for Crude Oil Price Forecasting

A Sentiment-Enhanced Hybrid Model for Crude Oil Price Forecasting

Early field planning using optimisation and considering uncertainties

During early phases of oil field development, field planners must decide upon the optimal number of wells and optimal field plateau rate, usually by performing sensitivity studies. These design choices are then “frozen” in subsequent development stages. However, they often end up being suboptimal when the field is built and produced and the uncertainty is reduced. In this work, we employ non-linear numerical optimisation, latin hypercube sampling and the Schwartz & Smith oil price model to compute probability distributions of the optimal number of wells, plateau rate and project value. We also employ an analytical model to compute production profiles and project value and consider uncertainties in in-place oil volume, well productivity and oil price. Then, we study how do these distributions change from early field planning until when the field is abandoned, when uncertainties are reduced to a minimum. The variation in time of the in-place oil volume uncertainty is modelled with a random walk. The well productivity is a step function altered randomly after production startup. The actual oil price trajectory is picked randomly from possible trajectories computed with the Schwartz and Smith model. The results show that the distributions of the optimal number of wells, plateau rate and project value depend greatly on the uncertainties in the input data. Field designs based on the average of the distributions during the early phase are profitable, but suboptimal. A potential upside of such designs is that they entail less capital investment and therefore less financial risk when compared against the optimal field design. • Robust approach to consider uncertainties when computing optimal field design. • The optimal design strongly depends on the uncertainties in input parameters. • Uncertainties in input parameters are considered to vary during the field’s life. • The value of perfect information is modest for the cases studied in this paper.

Oil price volatility and corporate cash holding

Since it is believed that oil price movements highly influence economic growth in oil-rich countries, this study examines the influence of oil price volatility on corporate cash holdings and adjustment speed and tests whether firms in the petrochemical industry are affected differently from firms in other industries. This study addresses these research questions using static and dynamic system GMM models on a sample of non-financial firms listed in Gulf Cooperation Council (GCC) countries between 2005 and 2019. The results of this study assert that oil price volatility has a significant impact on cash-holding decisions. Specifically, the study displays a positive and significant relationship between oil price volatility and firms' cash holdings. Again, the study discovers that firms adjust towards their target cash-holding level faster during high oil price volatility than during periods of low oil price volatility. Consistent with the trade-off theory, the study unveils that precautionary motive is a crucial factor motivating firms to retain large amounts of cash. The results also showed that firms in the petrochemical industry have a higher cash-holding ratio than firms in other industries. These results support the view that oil price volatility exposes oil-related industries to a higher uncertainty than other industries. Moreover, the findings are robust to an alternative measure of oil price volatility and model specifications.

Is Facebook Prophet Superior than Hybrid Arima Model to Forecast Crude Oil Price?

Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains a difficult topic because of its dependency on a variety of factors, including the economic cycle, international relations, and geopolitics. Forecasting the price of oil is a difficult but gratifying task. Motivated by this issue, we present a robust model for accurate crude oil price forecasting using ARIMA and Prophet models based on machine learning technique to produce a reliable weekly and monthly crude oil price predictions. We apply the Savitzky–Golay smoothing filter to get a better denoising performance for our forecast models. For model evaluation, we apply cross validation with sliding windows on both models and compares the performances using RMSE and MAPE. The results show that the ARIMA- based machine learning approach performs better as compared to the Prophet model for both one-week and one-month forecast ahead intervals.

A Dynamic Level Technical Indicator Model for Oil Price Forecasting

Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities’ prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit.

Comparative Modelling of Price Volatility in Nigerian Crude Oil Markets Using Symmetric and Asymmetric GARCH Models

The study aimed at developing an appropriate GARCH model for modelling in Nigerian Crude Oil Prices Markets using symmetric and Asymmetric GARCH models while the specific objectives of the study include to: build an appropriate Symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedacity (GARCH) model for Nigerian Crude Oil Prices, compare the advantage of using Symmetric and Asymmetric GARCH. The data for the study was extracted from the Central Bank of Nigeria online statistical database starting from January, 1982 to December, 2018. The software used in estimating the parameters of the model is Econometric view (Eview) software version ten (10). Two classes of models were used in the study; they are symmetric and Asymmetric GARCH models. The results of the estimated models revealed that Asymmetric GARCH model (EGARCH (1,1) in student’s-t error assumption gave a better fit than the first order Symmetric GARCH models. Also, Using EGARCH (1,1) models with their corresponding error distribution in estimating crude oil price was found that the larger the size of the estimated news components of the model, the higher the negative news associated with high impact of volatility. This means that conditional volatility estimated using EGARCH model has strong asymmetric characteristic which is prone to news sensitivity. Based on the above findings, recommendations were made in the study.

Predictive Analysis Model for Oil price Forecasting using a Hybrid Model

Petroleum plays a vital role in the economy of our country. It is one of the important sources of the energy and act as vital raw material for a number of industries. Every economic sector in the world is dependent on crude oil, any increase or decrease in the price of crude oil has a ripple effect on the global economy. Hence accurately predicting the price petroleum become the hot issues in many countries around the world. To grasp the trend of petroleum price and reduce the negative impact of petroleum price changes. In this paper a new improved hybrid model is proposed by combining Particle Swarm Optimization (PSO) and Back Propagation (BP)-Artificial Neural Network(ANN) techniques to predict the oil price.

Forecasting the Price of Fuel Oil: A STL-(ELM+ARIMA) Combination Approach

Focusing on fuel oil price forecasting, we propose a “decomposition-prediction-integration” route and STL-(ELM+ARIMA) combination forecasting model. This model decomposes the fuel oil price time series by STL, and effectively combines the advantages of high frequency seasonal cycle and short-term fluctuation time series forecasting in ELM non-parametric model with the advantages of low frequency trend forecasting in ARIMA parametric model. Finally, this paper conducts an empirical study on the spot price of Singapore’s Platts fuel oil 180CST to verify the effectiveness of the proposed forecasting method. The results show that the forecasting accuracy of 180 CST fuel oil price model based on STL-( ELM (1) + ARIMA (2) +ELM (3)) is highest.

Markov Regime Switching-Garch Modeling On World Oil Prices

This study aims to explain and test the performance of the best forecasting model for world oil prices. The world oil price is included in a time-series data type that has high volatility and different variants at each point in time. Precise and precise time-series modeling of this type of data is required to properly explain structural changes and explain any shift in volatility. The method that is applied and produces the best model in describing world oil prices is the Markov Regime Switching-GARCH. Modeling results can be used as alternative data for investor’s consideration for determining their investment decisions.

МИРОВОЕ ПОТРЕБЛЕНИЕ ПЕРВИЧНЫХ ЭНЕРГОНОСИТЕЛЕЙ: ОСНОВНЫЕ ТЕНДЕНЦИИ ВО ВТОРОМ ДЕСЯТИЛЕТИИ XXI В.

Актуальность исследования определяется необходимостью повышения энергоэффективности и выполнением требований устойчивого развития. Цель работы: анализ тенденций по основным характеристикам на рынке энергоносителей: цене и объему потребления. Потребление выбрано по основным видам первичных энергоносителей: нефть, природный газ, ядерное топливо, возобновляемые источники энергии, гидроэлектроэнергия, каменный уголь. Анализ и прогноз цен проводятся только для нефти марки «Брент». Также необходимо изучить влияние требований устойчивого развития на потребление энергоносителей по макрорегионам: Северная Америка, Южная Америка и Карибы, Азиатско-Тихоокеанский регион, страны СНГ, Средний Восток, Европейский регион, Африка. Методы: прогнозная экстраполяция, основанная на анализе трендов, определяющих на уровне макрорегионов готовность к отказу от устаревших и развитию современных видов первичных энергоносителей; метод аналогии при построении циклической модели цен на нефть. Результаты. Выделены и охарактеризованы четыре цикла динамики цен на нефть в первые два десятилетия XXI в. Спрогнозированы два варианта ценовых уровней на 2020−2021 гг. Дана характеристика основным тенденциям в потреблении первичных энергоносителей отдельно по каждому их виду и каждому региону мира.

Understanding Oil Cycle Dynamics to Design the Future Economy.

In this work, we make the ansatz that economic production is reduced to the energy made available to the economy. In (Illig and Schindler, BioPhys Econ Resour Qual 2(1):1, 2017) the price of oil was expressed as a function of the size of the economy, the cost share of oil, and the quantity of oil extracted. We clarify assumptions needed to use this explicit price equation to study prices. Using the current extraction rate, the previous year’s extraction rate, and interest rates of the Federal Reserve we use linear regression to give a model for oil prices from 1966 to 2018. The model verifies that deductions made from the explicit price equation are consistent with empirical data over the given time period. Our analysis indicates that the contraction phase of world oil extraction began in 2020 and that it will be characterized by relatively low oil prices. We present some challenges and opportunities for building a future economy if our assumptions prove valid.

Modelling exchange rate pass-through: A model of oil prices and asymmetric exchange rate fluctuations

Orientation: The literature on exchange rate pass-through appears to have shifted from the question of whether the pass-through is complete or incomplete to whether or not it is sufficient to assume that the pass-through is exogenous despite the vulnerability of exchange rates to shocks because of other external prices. Research purpose: The primary objective of this study is to examine the role of oil prices in the dynamics of exchange rate pass-through to domestic inflation in net oil-exporting and oil-importing countries. Motivation for the study: Motivated by the increasing evidence of significant responses of exchange rates to changes in oil prices, this study hypothesises that changes in oil prices matter for the degree and direction of exchange rate pass-through in the context of oil-importing and oil-exporting dichotomy. This study attempts to re-define the areas of ambiguities on the exchange rate, and other accompanying factors linked to it for clarity. Research approach/design and method: Using a macro panel data set, we explore the newly formulated non-linear panel autoregressive distributed lag model to account for asymmetries in our assessment of the role of oil prices in the degree and direction of pass-through of the exchange rate. Thus, in addition to reflecting the pass-through in a nonlinear form, this study also accounts for heterogeneity as well as non-stationarity. Besides, we also evaluate the role of prices in the pass-through of exchange rates symmetrically using the symmetric version of the Panel ARDL model. Main findings: Given the data under consideration, our empirical findings give credence to the school of thought challenging the widely held assertion that the declining pass-through of the exchange rate is mainly caused by the phenomenon of the developed market. Practical/managerial implications: We also find that accounting for asymmetries in the pass-through matters for the extent to which changes in oil prices accelerate the degree of pass-through. Contribution/value-add: This study finds evidence of an insignificant role of oil prices in the pass-through of the exchange rate fluctuations to inflation. Once the pass-through is captured asymmetrically, it becomes evident that changes in oil prices matter in the pass-through.

Crude oil price dynamics with crash risk under fundamental shocks

Our paper presents a crude oil price model in which the price is confined in a wide moving band. A price crash occurs when the price breaches the lower boundary where a smooth-pasting condition is imposed. Using an asymmetric mean-reverting fundamental (supply/demand) shock, the solution derived from the oil price equation for the model shows the oil price follows a mean-reverting square-root process, which is quasi-bounded at the boundary. The oil price dynamics generates left-skewed price distributions consistent with empirical observations. A weakened mean-reverting force for the price increases the probability leakage for the price across the boundary and the risk of a price crash. The empirical results show the oil price dynamics can be calibrated according to the model, where the mean reversion of the price dynamics is positively co-integrated with the oil production reaction to negative demand shocks, and with the risk reversals of the commodity currencies, the Canadian dollar and the Australian dollar in currency option markets. The results are consistent with an increased price crash risk with negative demand shocks and negative risk reversals. The forecasting performance of the oil price model is better than the futures-spread models and random walk models during the crash periods. While the price of oil was above the lower boundary for most of the time, the conditions for breaching the boundary were met in 2008 and 2014 when the price fell sharply.

Crude Oil Price Dynamics with Crash Risk Under Fundamental Shocks

Our paper presents a crude oil price model in which the price is confined in a wide moving band. A price crash occurs when the price breaches the lower boundary where a smooth-pasting condition is imposed. Using an asymmetric mean-reverting fundamental (supply/demand) shock, the solution derived from the oil price equation for the model shows the oil price follows a mean-reverting square-root process, which is quasi-bounded at the boundary. The oil price dynamics generates left-skewed price distributions consistent with empirical observations. A weakened mean-reverting force for the price increases the probability leakage for the price across the boundary and the risk of a price crash. The empirical results show the oil price dynamics can be calibrated according to the model, where the mean reversion of the price dynamics is positively co-integrated with the oil production reaction to negative demand shocks, and with the risk reversals of the commodity currencies, the Canadian dollar and the Australian dollar in currency option markets. The results are consistent with an increased price crash risk with negative demand shocks and negative risk reversals. The forecasting performance of the oil price model is better than the futures-spread models and random walk models during the crash periods. While the price of oil was above the lower boundary for most of the time, the conditions for breaching the boundary were met in 2008 and 2014 when the price fell sharply.

A novel hybrid model for forecasting crude oil price based on time series decomposition

Oil price forecasting has received a prodigious attention by scholars and policymakers due to its significant effect on various economic sectors and markets. Incentivized by this issue, the author proposes a novel hybrid model for crude oil price forecasting whose focus is on improving the accuracy of prediction taking into consideration the characteristics existing in the oil price time series. In so doing, the author constitutes a hybrid model consisting of complete ensemble empirical mode decomposition, support vector machine, particle swarm optimization, and Markov-switching generalized autoregressive conditional heteroskedasticity to capture the nonlinearity and volatility of the time series more effectively. Mean absolute error, root mean square error, and mean absolute percentage error tests are used to measure forecasting errors. Results robustness and forecasting quality of the proposed hybrid model compared with counterparts are also investigated by Diebold-Mariano test. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.

Optimal oil stockpiling, peak oil, and general equilibrium: case study of South Asia (oil importers) and Middle East (oil supplier)

Optimal stockpiling is the best possible strategy to overcome the problem of peak oil periods of oil producer economies. We measured the properties of strategic petroleum oil reserve and general equilibrium and its peak oil effects. Measured the optimized scales of SPR through using oil price model, global oil market, and depletion effects of oil production classification. The peak oil period occurs from the interection between the geological era, proficiency in a practical skill, economy of consumers, and geopolitics, and the quality of deciding of demand and supply in which we have done a general dynamic balance model. Results reveal that peak oil time periods may lead towards diverse oil prices time profiles, economic development, and commodity flows. Interestingly, the macroeconomic effects of peak oil and the trajectories in objective function of two options maximize the households' welfare and oil revenues and its effect on growth trajectories of oil-consuming countries. If an oil supply disruption happens, the rate of oil acquisition will be considerably decreased, though it may not be a good strategy to interrupt the activities of oil reserve with the aim of minimizing the overall costs.

A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.