Analysis of oil price fluctuations (original) (raw)
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Analysis of Nonstationary Stochastic Processes With Application to the Fluctuations In the Oil Price
Phys Rev E (Rapid …, 2007
We describe a method for analyzing a nonstationary stochastic process x(t), and utilize it to study the fluctuations in the oil price. Evidence is presented that the fluctuations in the returns y(t), defined as, y(t) = ln{x(t+1)/x(t)}, where x(t) is the datum at time t, constitute a Markov process, characterized by a Markov time scale t M . We compute the coefficients of the Kramers-Moyal expansion for the probability distribution function P (y, t|y 0 , t 0 ), and show that P (y, t|, y 0 , t 0 ) satisfies a Fokker-Planck equation, which is equivalent to a Langevin equation for y(t). The Langevin equation provides quantitative predictions for the oil price over Markov time scale t M . Also studied is the average frequency of positive-slope crossings, ν + α = P (y i > α, y i−1 < α), for the returns y(t), where T (α) = 1/ν + α is the average waiting time for observing y(t) = α again. The method described is applicable to a wide variety of nonstationary stochastic processes which, unlike many of the previous methods, does not require the data to have any scaling feature. PACS numbers(s): 05.10. Gg, 05.45.Tp
Geometric Brownian Motion and structural breaks in oil prices: A quantitative analysis
Energy Economics, 2006
The purpose of this paper is to present a quantitative analyses of oil price's path. We try to argue that, despite its parsimony and simplicity, Geometric Brownian Motion can perform well as a proxy for the movement of oil prices and for a state variable to evaluate oil deposits. We base our argument on evidences of very low speed of mean reverting (or long half-life), since unit root tests only can reject its null hypothesis in a sample longer than 100 years. On the other hand, we reject the null hypothesis of unit root with two endogenous breaks, showing that the usual rejection can be attributed to omitted structural breaks. We conclude that the average half-life of oil price (between 4 and 8 years depending on the model chosen) is long enough to allow a good approximation as a Geometric Brownian Motion.
Energy Economics, 2010
For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation process with GARCH and no mean reversion.
Modelling and Forecasting of Crude Oil Price Volatility Comparative Analysis of Volatility Models
Journal of Financial Risk Management, 2022
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.
International Journal of Energy Economics and Policy, 2013
In this study, it has been attempted to select the best continuous-time stochastic model, in order to describe and forecast the oil price of Russia, by information and statistics about oil price that has been available for oil price in the past. For this purpose, method of The Maximum Likelihood Estimation is implemented for estimation of the parameters of continuous-time stochastic processes. The result of unit root test with a structural break, reveals that time series of the crude oil price is a stationary series. The simulation of continuous-time stochastic processes and the mean square error between the simulated prices and the market ones shows that the Geometric Brownian Motion is the best model for the Russian crude oil price.
Simulation and hedging oil price with geometric Brownian Motion and single-step binomial price model
2017
This paper [1] uses the Geometric Brownian Motion (GBM) to model the behaviour of crude oil price in a Monte Carlo simulation framework. The performance of the GBM method is compared with the naive strategy using different forecast evaluation techniques. The results from the forecasting accuracy statistics suggest that the GBM outperforms the naive model and can act as a proxy for modelling movement of oil prices. We also test the empirical viability of using a call option contract to hedge oil price declines. The results from the simulations reveal that the single-step binomial price model can be effective in hedging oil price volatility. The findings from this paper will be of interest to the government of Nigeria that views the price of oil as one of the key variables in the national budget. JEL Classification Numbers : E64; C22; Q30 Keywords : Oil price volatility; Geometric Brownian Motion; Monte Carlo Simulation; Single-Step Binomial Price Model [1] Acknowledgement: We wish to...
A Novel Approach for Modeling Global Oil Market Volatility
Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment losses, and inefficient investments. In sum, the growth potential for energy-producing countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in energy markets, for the benefit of consumers and producers alike. Therefore, modeling and forecasting crude oil price volatility is critical in many financial and investment applications. The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Applying the novel approach of ANN, two models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the oil price volatility. These ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
Oil Markets and Price Movements: A Survey of Models
During the 1970s, oil market models offered a framework for understanding the growing market power being exercised by major oil producing countries. Few such models have been developed in recent years. Moreover, most large institutions do not use models directly for explaining recent oil price trends or projecting their future levels. Models of oil prices have become more computational, more data driven, less structural and increasingly short run since 2004. Quantitative analysis has shifted strongly towards identifying the role of financial instruments in shaping oil price movements. Although it is important to understand these short-run issues, a large vacuum exists between explanations that track short-run volatility within the context of long-run equilibrium conditions. The theories and models of oil demand and supply that are reviewed in this paper, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products. The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. There are several kinds of models have been proposed, including structural, computational and reduced form models. Recently, artificial intelligence was also introduced. This paper provides: (1) model taxonomy and the uses of models providing the motivation for its preparation, (2) a brief chronology explaining how oil market models have evolved over time, (3) three different model types: structural, computational, and reduced form models, and (4) artificial intelligence and data mining for oil market models.
ANNALS OF FOREST RESEARCH, 2022
As one of the most essential energy sources in our nation, crude oil is also one of the most important Imported goods. India imports around 84% of its total Crude oil needs. This clearly illustrates the extent to which fluctuations in crude oil prices effect our economy. The energy industry is one of the most volatile industries, and energy dependence is substantial. Additionally, crude oil is a significant commodity traded on the commodities market. Considering all of the aforementioned variables, fluctuations in the price of crude oil have a significant influence on the nation's economy as a whole, making crude oil forecasting a necessity. This study analysed data from 2000 to 2021 to develop a very accurate predictive model. In addition to crude oil prices, macroeconomic indicators such as gold prices and dollar exchange rates have been collected and evaluated using ARIMA model.
Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation
Sustainability
Due to multiple properties, the international crude oil price is influenced by various and complex interrelated factors from different determinants in different periods. However, the previous studies on crude oil price fluctuation with economic policy uncertainty (EPU) haven’t taken a wider range of volatility sources into their analysis frameworks. In this paper, the time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is introduced in order to avoid important information loss, as well as capture the time-varying impact on crude oil price fluctuation by EPU. Furthermore, the differences on crude oil fluctuations from net-oil exporting and net-oil importing country’s EPU are also elaborated. Here are three findings as follows. First, the impacts of global EPU on the crude oil price volatility show time-varying characteristics both in time duration and time-points. Second, the instantaneous impacts of global EPU on the price volatility of crude oil are dire...