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| Author | : Poh Giek Ding |
| Publisher | : |
| Total Pages | : 68 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
| Author | : Mohd Ariffudin Shamsudin |
| Publisher | : |
| Total Pages | : 65 |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
| Author | : Mr.Manmohan S. Kumar |
| Publisher | : International Monetary Fund |
| Total Pages | : 54 |
| Release | : 1991-10-01 |
| Genre | : Business & Economics |
| ISBN | : 1451951116 |
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.
| Author | : Chee Nian Lee |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2009 |
| Genre | : Finance |
| ISBN | : |
| Author | : May Al- Issa |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023-09-27 |
| Genre | : |
| ISBN | : 9781916761629 |
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.
| Author | : Adalat Muradov |
| Publisher | : Springer |
| Total Pages | : 184 |
| Release | : 2019-04-10 |
| Genre | : Business & Economics |
| ISBN | : 3030114945 |
This book develops new econometric models to analyze and forecast the world market price of oil. The authors construct ARIMA and Trend models to forecast oil prices, taking into consideration outside factors such as political turmoil and solar activity on the price of oil. Incorporating historical and contemporary market trends, the authors are able to make medium and long-term forecasting results. In the first chapter, the authors perform a broad spectrum analysis of the theoretical and methodological challenges of oil price forecasting. In the second chapter, the authors build and test the econometric models needed for the forecasts. The final chapter of the text brings together the conclusions they reached through applying the models to their research. This book will be useful to students in economics, particularly those in upper-level courses on forecasting and econometrics as well as to politicians and policy makers in oil-producing countries, oil importing countries, and relevant international organizations.
| Author | : Tony Klein |
| Publisher | : |
| Total Pages | : 33 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
First Version: 03/11/2015This Version: 04/01/2016We expand the literature of volatility and Value-at-Risk forecasting of oil price returns by comparing the recently proposed Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, FIGARCH, and HYGARCH). We incorporate an Expectation-Maximization algorithm for parameter estimation of the MMGARCH and find regimes that differ in volatility level as well as shock persistence. Furthermore, we observe dissimilar memory structure in variance of WTI and Brent crude oil prices which is confirmed by altering the mixture components. In regard of variance forecasting and Value-at-Risk prediction, we show that MMGARCH outperforms the aforementioned models due to its dynamic approach in varying the volatility level and memory of the process. We find MMGARCH superior for application in risk management as a result of its flexibility in adjusting to variance shifts and shocks.
| Author | : Wei-Chiang Hong |
| Publisher | : MDPI |
| Total Pages | : 262 |
| Release | : 2020-04-01 |
| Genre | : Computers |
| ISBN | : 3039283642 |
Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.
| Author | : Chris Chatfield |
| Publisher | : CRC Press |
| Total Pages | : 281 |
| Release | : 2000-10-25 |
| Genre | : Business & Economics |
| ISBN | : 1420036203 |
From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
| Author | : Hassan Khazem |
| Publisher | : LAP Lambert Academic Publishing |
| Total Pages | : 104 |
| Release | : 2011-10 |
| Genre | : |
| ISBN | : 9783846529416 |
Crude oil is the commodity de jour and its pricing is of paramount importance to the layperson as well as to any responsible government. However, one of the main challenges facing econometric pricing models is the forecasting accuracy. Historically, linear and non-linear time series models were used. Although, a great success was achieved in that regard, yet there were no definite and universal conclusions drawn. The crude oil forecasting field is still wide open for improvement, especially when applying different forecasting models and alternative techniques. Toward this end, the proposed research implemented Artificial Neural Network models (ANN). The models will forecast the daily crude oil futures prices from 1996 to 2006, listed in NYMEX. Due to the nonlinearity presented by the test results of the crude oil pricing, it is expected that the ANN models will improve forecasting accuracy. An evaluation of the outcomes of the forecasts among different models was done to authenticate that this is undeniably the situation.
