Oil Spot Prices Research Articles

Jump Processes in the Market for Crude Oil

In many commodity markets, the arrival of new information leads to unexpectedly rapid changes—or jumps—in commodity prices. Such arrivals suggest the assumption that log-return relatives are normally distributed may not hold. Combined with time-varying volatility, the possibility of jumps offers a potential explanation for fat tails in oil price returns. This article investigates the potential presence of jumps and time-varying volatility in the spot price of crude oil and in futures prices. The investigation is carried out over three data frequencies (Monthly, Weekly, Daily j, which allows for an investigation of temporal properties. Employing likelihood ratio tests to compare among four stochastic data-generating processes, we find that that allowing for both jumps and time-varying volatility improves the model’s ability to explain spot prices at each level of temporal aggregation; this combination also provides a statistically compelling improvement in model fit for futures prices at the Daily and Weekly level. At the monthly level, allowing for jumps does not provide a statistically significant increase in model fit after incorporating time-varying volatility into the model.

Determinants of oil futures prices and convenience yields

Commodity futures prices are usually modelled using affine term structure spot price models with latent factors extracted from the data. However, very little research to date has considered the question – What are the economic drivers behind the calibrated latent factors? This paper addresses this question in the context of a three-factor – short-, medium- and long-term – model for crude oil spot prices by studying the relations between these factors and appropriate economic variables. An affine combination of the short- and medium-term factors is identified as the (instantaneous) convenience yield. Estimating a structural vector auto-regression model we find that the short-term factor mainly relates to demand variables in the physical markets and to trading variables in the futures markets (such as the net short position of commercial hedgers), the medium-term factor relates to business cycles, demand and trading variables, and the long-term factor relates mainly to financial factors.

Measuring contagion between energy market and stock market during financial crisis: A copula approach

In this paper, we apply time-varying copulas to investigate whether a contagion effect existed between energy and stock markets during the recent financial crisis. Using the WTI oil spot price, the S&P500 index, the Shanghai stock market composite index and the Shenzhen stock market component index returns, evidence was found for a significantly increasing dependence between crude oil and stock markets after the failure of Lehman Brothers, thus supporting the existence of contagion in the sense of Forbes and Rigobon's (2002) definition. Moreover, increased tail dependence and symmetry characterize all the paired markets. This indicates that significant increases in tail dependence are an actual dimension of the contagion phenomenon and that crude oil and stock prices are linked to the same degree regardless of whether markets are booming or crashing during the sample period. Finally, the contagion effect is found to be much weaker for China than the US. The empirical results have potentially important implications for risk management.

Forecasting the West Texas Intermediate Crude Oil Spot Price Using Multi Model Inference

We measure the performance of Multi Model Inference (MMI) forecasts compared to predictions made from a single for crude oil prices. We forecast the West Texas Intermediate (WTI) crude oil spot prices using total OECD petroleum inventory levels, surplus production capacity, the CBOE Volatility Index (VIX) and an implementation of a Subset Autoregression with Exogenous Variables (SARX). Coecient and standard error estimates obtained from SARX determined by conditioning on a single best model ignore uncertainty and result in under-estimated standard errors and over-estimated coecients. We find that the MMI forecast outperforms a single forecast for both in and out of sample data sets over a variety of statistical performance measures, and further and that weighting models according to the BIC generally yields superior results both in and out of sample when compared to the AIC.

The Fundamental and Speculative Components of the Oil Spot Price: A Real Option Value Approach

This paper investigates the recent evolution of the oil price, with the objective to analyze the main drivers that during last fifteen years have led the unstable path and the volatility persistence in the international oil market. We assume that the oil price is composed by two components, deterministic and speculative. The fi rst one can be defi ned as the certain one, and it is referred to the fundamental component given by supply and demand interaction. Di fferently, the uncertain one is given by unclear changes in the price structure, and it is assumed to be linked to the speculative activity. Through a structural equation model (SEM) in a linear reduced form we find that the speculation in the oil market measured with the real option methodology can improve the traditional model explaining a consistent part of the oil fluctuations.

Commodity Futures Index Trading and Spot Oil Prices

Politicians, market participants, and economists have argued about whether the increased trading induced by the growth of index funds over the past decade is a cause of high commodity prices.

A hybrid optimized artificial intelligent model to forecast crude oil using genetic algorithm

Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this study, a novel hybrid optimum model based on artificial intelligent (AI) is proposed for world crude oil spot price forecasting. A three-layer feed forward neural network (FNN) model was used to model the oil price forecasting. Genetic algorithm (GA) is employed not only to improve the learning algorithm, but also to reduce the complexity in parameter space. GA optimizes simultaneously, the connection weights between layers and the thresholds. In addition, GA reduces the dimension of the feature space and eliminates irrelevant factors. For verification and testing, two main crude oil price series, West Texas intermediate (WTI) crude oil spot price, Brent crude oil spot price and Iran crude oil are used to test the effectiveness of the proposed optimized neural network. Results show that optimized model has advantages in comparison with conventional ANN in terms of accuracy, variability, model creation and model examination. Both simulated and actual data sets are used for comparison. Key word: Artificial neural network, crude oil, genetic algorithm, optimization.

What explains risk premiums in crude oil futures?*

AbstractThis paper studies the existence of risk premiums in crude oil futures prices with simple regression and Bayesian vector autoregressive models. It also studies the importance of three main risk premiums models in explaining and forecasting the risk premiums in practice. While the existence of the premiums and the validity of the models can be established at certain time points, it turns out that the choice of sample period has a considerable effect on the results. Hence, the risk premiums are highly time‐varying. The study also establishes a model, based on speculative positions in the futures markets, which has some predictive power for future oil spot prices.

A metric and topological analysis of determinism in the crude oil spot market

We test whether the spot price of crude oil is determined by stochastic rules or exhibits deterministic endogenous fluctuations. In our analysis, we employ both metric (correlation dimension and Lyapunov exponents) and topological (recurrence plots) diagnostic tools for chaotic dynamics. We find that the underlying system for crude oil spot prices (i) is of high dimensionality (no stabilization of the correlation dimension), (ii) does not exhibit sensitive dependence on initial conditions, and (iii) is not characterized by the recurrence property. Thus, the empirical evidence suggests that stochastic rather than deterministic rules are present in the system dynamics of the crude oil spot market. Recurrent plot analysis indicates that volatility clustering is an adequate, but not complete, explanation of the morphology of oil spot prices.

U.S. industry-level returns and oil prices

This paper empirically investigates and provides further support for the oil price effect documented in Driesprong et al. (2008) in the U.S. industry-level returns. We find that oil price predictability is concentrated in a relatively small number of industry-level returns, the relevant measure for a study of the oil effect is percentage change in oil spot prices, and changes in oil futures prices have virtually no prediction power for industry-level returns. With percentage changes in oil spot prices as the predictor, approximately one fifth of industry returns are oil-predictable. We detect a two trading weeks delay in reaction to oil price changes which is consistent with the Hong and Stein (1996) underreaction hypothesis. These results are robust to various alternative specifications, and are shown to be unrelated to time-varying risk premia. Moreover, we demonstrate that trading strategies based on the oil effect generate superior gains in comparison with buy-and-hold strategy in the presence of reasonable trading costs.

U.S. Industry-Level Returns and Oil Prices

This paper empirically investigates and provides further support for the oil price effect documented in Driesprong et al. (2008) in the U.S. industry-level returns. We find that oil price predictability is concentrated in a relatively small number of industry-level returns, the relevant measure for a study of the oil effect is percentage change in oil spot prices, and changes in oil futures prices have virtually no prediction power for industry-level returns. With percentage changes in oil spot prices as the predictor, approximately one fifth of industry returns are oil-predictable. We detect a two trading weeks delay in reaction to oil price changes which is consistent with the Hong and Stein (1996) underreaction hypothesis. These results are robust to various alternative specifications, and are shown to be unrelated to time-varying risk premia. Moreover, we demonstrate that trading strategies based on the oil effect generate superior gains in comparison with buy-and-hold strategy in the presence of reasonable trading costs.

A empirical study of changes in international crude oil spot price relationship between Taiwan stock prices

Previous studies have shown correlation between changes in international oil prices on stock returns and the stock market has some of. However, these studies often do not obey the country is located in North America, Britain, Australia and other areas of the developed economies and the accumulation of the main level of the stock market conducted. In this study, international relations, oil prices and stock indices, the Taiwan stock market did have some of the current impact of. Results of the study provide evidence of the nature, scale and scope of the relationship between the global oil prices, stock returns and stock index in Taiwan. Therefore, it has been found that disclosure of signifi cant diff erences between oil shocks across time and companies. The purpose of the survey is to provide a complete analysis of play, the relationship between oil prices and the Taiwan Stock Index, which aims to fi nd out whether such a relationship exists, whether a good correlation with the conclusions of Taiwan's economy. The questionnaire aimed to answer the question to study in order to conduct a survey to assess the results of the study.

Forecasting the Price of Oil

Abstract We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real and nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures prices in forecasting the spot price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future oil demand and oil supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?

Do Financial Investors Destabilize the Oil Price?

We assess whether and to what extent …nancial activity in the oil futures markets has contributed to destabilize oil prices in recent years. We de…ne a destabilizing …nancial shock as a shift in oil prices that is not related to current and expected fundamentals, and thereby distorts e¢ cient pricing in the oil market. Using a structural VAR model identi…ed with sign restrictions, we disentangle this non-fundamental …nancial shock from fundamental shocks to oil supply and demand to determine their relative importance. We …nd that shocks to oil demand and supply remain the main drivers of oil price swings. Financial investors in the futures market can however destabilize oil spot prices, although only in the short run. Moreover, …nancial activity appears to have exacerbated gyrations in the oil market over the past decade, particularly in 2007-2009.

FORECASTING THE CRUDE OIL SPOT PRICE BY WAVELET NEURAL NETWORKS USING OECD PETROLEUM INVENTORY LEVELS

In this study, a novel forecasting model based on the Wavelet Neural Network (WNN) is proposed to predict the monthly crude oil spot price. In the proposed model, the OECD industrial petroleum inventory level is used as an independent variable, and the Wavelet Neural Network (WNN) is used to explore the nonlinear relationship between inventories and the price. For verification purposes, the West Texas Intermediate (WTI) crude oil spot price is used for the tested target. Experimental results reveal that the WNN can model the nonlinear relationship between inventories and the price very well. Furthermore, the in-sample and out-of-sample prediction performance also demonstrates that the WNN-based forecasting model can produce more accurate prediction results than other nonlinear and linear models, even when the lengths of the forecast horizon are relatively short or long.

Consumption and Hedging in Oil‐Importing Developing Countries

AbstractWe study the consumption and hedging strategy of an oil‐importing developing country that faces multiple crude oil shocks. In our model, developing countries have two particular characteristics: their economies are mainly driven by natural resources and their technologies are less efficient in energy usage. The natural resource exports can be correlated with the crude oil shocks. The country can hedge against the crude oil uncertainty by taking long/short positions in existing crude oil futures contracts. We find that both inefficiencies in energy usage and shocks to the crude oil price lower the productivity of capital. This generates a negative income effect and a positive substitution effect, because today’s consumption is relatively cheaper than tomorrow’s consumption. Optimal consumption of the country depends on the magnitudes of these effects and on its risk‐aversion degree. Shocks to other crude oil factors, such as the convenience yield, are also studied. We find that the persistence of the shocks magnifies the income and substitution effects on consumption, thus also affecting the hedging strategy of the country. The demand for futures contracts is decomposed in a myopic demand, a pure hedging term and productive hedging demands. These hedging demands arise to hedge against changes in the productivity of capital due to changes in crude oil spot prices. We calibrate the model for Chile and study to what extent the country’s copper exports can be used to hedge the crude oil risk.

What Explains Risk Premia in Crude Oil Futures?

This paper studies the existence of risk premiums in crude oil futures prices with simple regression and Bayesian vector autoregressive models. It also studies the importance of three main risk premiums models in explaining and forecasting the risk premiums in practice. While the existence of the premiums and the validity of the models can be established at certain time points, it turns out that the choice of sample period has a considerable effect on the results. Hence, the risk premiums are highly time‐varying. The study also establishes a model, based on speculative positions in the futures markets, which has some predictive power for future oil spot prices.

Growth And Oil Futures Prices In Developing Countries

This paper examines cointegration and causality between oil prices and economic growth for the oil importing developing countries of Turkey, India, Pakistan, The Philippines and Korea. The study finds the absence of cointegrating relationship between oil prices and economic activity but the existence of unidirectional short-run causality running from oil prices to economic growths for The Philippines and Pakistan. Unidirectional causality is also found to exist from six and nine month futures prices to economic growth for India and Turkey in a bivariate vector autoregression framework. The study fails to establish causal relationship between oil prices and economic growth for Korea, while for India and Turkey, non-causality has been established between oil spot price and economic growth. Hence, our results may suggest that oil futures markets will have more of a role to play in the economy as these markets mature and or as oil prices continue to increase.

Oil Speculation: The Impact On Prices, Inflation, Interest Rates And The Economy

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">The vector autoregression process is used to estimate a reformulated model of the equation of exchange developed from the quantity theory of money.<span style="mso-spacerun: yes;">  </span>In describing the effect of oil speculation, represented by spot oil price, on general price level and the overall economy, new variables are incorporated to take into account current global economic conditions and realities.<span style="mso-spacerun: yes;">  </span>A variable, which was introduced in Ajuzie et al, 2008 paper, import of goods and services, is included and impulse-response function is used to forecast future economy and determine policy scenarios.<span style="mso-spacerun: yes;">  </span>Results show that an increase in spot oil price last period has a highly statistically significant positive effect on CPI inflation.<span style="mso-spacerun: yes;">  </span>This effect is strong enough to eliminate the negative significant impact of import of goods and services on CPI inflation found in the paper of May 2008.<span style="mso-spacerun: yes;">  </span>Based on this finding, it is highly recommended that oil speculation should be discouraged and trade relations be bases purely on the principles of comparative advantage.<span style="mso-spacerun: yes;">  </span><span style="mso-spacerun: yes;">          </span></span></span></p>

A dynamic hedging approach for refineries in multiproduct oil markets

A multiproduct portfolio hedge ratio strategy for oil futures is investigated using a multivariate GARCH model based on dynamic conditional correlation and an error correction model (DCC-ECM-MVGARCH). By considering the characteristics of refiner profits from crack spread and the mutual relations among crude oil, gasoline and heating oil spot and future prices, we estimate the time-varying optimal hedge ratios for the oil-cracking margin. In addition, a naïve strategy, a traditional OLS model and dynamic B-GARCH model are selected to compare with our model for hedge effectiveness. Comparison of hedge effectiveness for in-sample and out-of-sample data reveals that the dynamic DCC-ECM-MVGARCH model is more sensitive to market fluctuations, provides a more accurate description of changes in volatility and has more advantages than other models. Therefore, the empirical results prove that application of the DCC-ECM-MVGARCH model for hedging of oil market portfolio can play an important role in avoiding the double risk of crude oil and oil product markets for refineries.