Modelling and Forecasting Crude Oil Volatility
| Author | : Emin Musayev |
| Publisher | : |
| Total Pages | : 80 |
| Release | : 2016-12-13 |
| Genre | : |
| ISBN | : 9783330008939 |
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Forecasting Volatility of Oil Prices & Their Effect on the Economy
| Author | : May Al- Issa |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023-09-27 |
| Genre | : |
| ISBN | : 9781916761629 |
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With the importance of crude oil and its effect on the macro and micro economy alike and with the fluctuations of oil prices mainly due to geopolitical reasons -speculators taking this advantage in raising the prices in 2008; forecasting crude oil volatility becomes vital. This project addresses three main areas: modelling volatility, forecasting and calculating options premiums and finally examining the effect of oil prices on the economy. Five year daily prices of OPEC, being the reference to oil prices, Brent being one of the main oil markets, BP.plc as one of the giant oil companies, and S&P500 being the important market index are obtained from different approved resources. Auto Regressive Conditional Heteroskedasticity series proved, as examined by vast number of studies in the literature reviewed; to be better in forecasting volatility in time series. GARCH and EGARCH are estimated under normality using random walk with drift for a better fit. Upon choosing the optimal models according to the Akaike and Schwartz Information Criteria; EGARCH(1,2) is of better fit to volatility for OPEC containing recent shocks to the prices, yet GARCH(1,2) and GARCH(5,4) provided almost similar results. EGARCH(1,1) proves to be yet another good model for both modelling and forecasting volatility of Brent crude returns by covering the asymmetry and the leverage effects. Options premiums calculated of 31-day forecast period using Black-Scholes model show different outcome to that obtained from Bloomberg implying the attraction of more investors to buy more profitable options since higher risk leads to higher profits. By performing the Johansen cointegration method, it is evident that oil price fluctuations have longer term relationship between OPEC and BP than between OPEC and S&P500 yet all three are in equilibrium portraying for more downturn in the economy.
Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility
| Author | : Jozef Baruník |
| Publisher | : |
| Total Pages | : 34 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
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In the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specific estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the pre-crisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with artificial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.
Oil Price Uncertainty
| Author | : Apostolos Serletis |
| Publisher | : World Scientific Publishing Company Incorporated |
| Total Pages | : 142 |
| Release | : 2012 |
| Genre | : Business & Economics |
| ISBN | : 9789814390675 |
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The relationship between the price of oil and the level of economic activity is a fundamental issue in macroeconomics. There is an ongoing debate in the literature about whether positive oil price shocks cause recessions in the United States (and other oil-importing countries), and although there exists a vast empirical literature that investigates the effects of oil price shocks, there are relatively few studies that investigate the direct effects of uncertainty about oil prices on the real economy. The book uses recent advances in macroeconomics and financial economics to investigate the effects of oil price shocks and uncertainty about the price of oil on the level of economic activity.
Advances in DEA Theory and Applications
| Author | : Kaoru Tone |
| Publisher | : John Wiley & Sons |
| Total Pages | : 579 |
| Release | : 2017-04-12 |
| Genre | : Mathematics |
| ISBN | : 1118946707 |
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A key resource and framework for assessing the performance of competing entities, including forecasting models Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting. Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource: Explores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications Provides rich, detailed examples and case studies Advances in DEA Theory and Applications includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.
Artificial Neural Network Models for Forecasting Global Oil Market Volatility
| Author | : Saud Al-Fattah |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
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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.
Forecasting Accuracy of Crude Oil Futures Prices
| Author | : Mr.Manmohan S. Kumar |
| Publisher | : International Monetary Fund |
| Total Pages | : 54 |
| Release | : 1991-10-01 |
| Genre | : Business & Economics |
| ISBN | : 1451951116 |
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This paper undertakes an investigation into the efficiency of the crude oil futures market and the forecasting accuracy of futures prices. Efficiency of the market is analysed in terms of the expected excess returns to speculation in the futures market. Accuracy of futures prices is compared with that of forecasts using alternative techniques, including time series and econometric models, as well as judgemental forecasts. The paper also explores the predictive power of futures prices by comparing the forecasting accuracy of end-of-month prices with weekly and monthly averages, using a variety of different weighting schemes. Finally, the paper investigates whether the forecasts from using futures prices can be improved by incorporating information from other forecasting techniques.
Oil Price Volatility and the Role of Speculation
| Author | : Samya Beidas-Strom |
| Publisher | : International Monetary Fund |
| Total Pages | : 34 |
| Release | : 2014-12-12 |
| Genre | : Business & Economics |
| ISBN | : 1498333486 |
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How much does speculation contribute to oil price volatility? We revisit this contentious question by estimating a sign-restricted structural vector autoregression (SVAR). First, using a simple storage model, we show that revisions to expectations regarding oil market fundamentals and the effect of mispricing in oil derivative markets can be observationally equivalent in a SVAR model of the world oil market à la Kilian and Murphy (2013), since both imply a positive co-movement of oil prices and inventories. Second, we impose additional restrictions on the set of admissible models embodying the assumption that the impact from noise trading shocks in oil derivative markets is temporary. Our additional restrictions effectively put a bound on the contribution of speculation to short-term oil price volatility (lying between 3 and 22 percent). This estimated short-run impact is smaller than that of flow demand shocks but possibly larger than that of flow supply shocks.
Forecasting the Term Structure of Volatility of Crude Oil Price Changes
| Author | : Ercan Balaban |
| Publisher | : |
| Total Pages | : 3 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
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This is a pioneering effort to test the comparative performance of two competing models for out-of-sample forecasting the term structure of volatility of crude oil price changes employing both symmetric and asymmetric evaluation criteria. Under symmetric error statistics, our empirical model using the estimated growth factor of volatility through time is overall superior, and it beats in most cases the benchmark model of the square-root-of-time for holding periods between one and 250 days. Under asymmetric error statistics, if over-prediction (under-prediction) of volatility is undesirable, the empirical (benchmark) model is consistently superior. Relative performance of the empirical model is much higher for holding periods up to fifty days.
Exploiting Dependence
| Author | : Stefan Lyocsa |
| Publisher | : |
| Total Pages | : 25 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
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This paper investigates volatility forecasting for crude oil and natural gas. The main objective of our research is to determine whether the heterogeneous autoregressive (HAR) model of Corsi (2009) can be outperformed by harnessing information from a related energy commodity. We find that on average, information from related commodity does not improve volatility forecasts, whether we consider a multivariate model, or various univariate models that include this information. However, superior volatility forecasts are produced by combining forecasts from various models. As a result, information from the related commodity can be still useful, because it allows us to construct wider range of possible models, and averaging across various models improves forecasts. Therefore, for somebody interested in precise volatility forecasts of crude oil or natural gas, we recommend to focus on model averaging instead of just including information from related commodity in a single forecast model.
