Download or read book Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks written by Jozef Baruník and published by . This book was released on 2015 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
Download or read book Forecasting commodity prices using long short term memory neural networks written by Ly, Racine and published by Intl Food Policy Res Inst. This book was released on 2021-02-10 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.
Download or read book Forecasting Accuracy of Crude Oil Futures Prices written by Mr.Manmohan S. Kumar and published by International Monetary Fund. This book was released on 1991-10-01 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Using Artificial Neural Networks to Forecast the Futures Prices of Crude Oil written by Hassan A. Khazem and published by . This book was released on 2008 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Application of Artificial Neural Networks in Forecasting Future Oil Price Return Volatilities written by Hamed Shafiee Hasanabadi and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting Crude Oil Prices written by Hassan Khazem and published by LAP Lambert Academic Publishing. This book was released on 2011-10 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Neural Network Models for Forecasting Global Oil Market Volatility written by Saud Al-Fattah and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Can the Dynamics of the Term Structure of Petroleum Futures be Forecasted Evidence from Major Markets written by Thalia Chantziara and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate whether the daily evolution of the term structure of petroleum futures can be forecasted. To this end, the principal components analysis is employed. The retained principal components describe the dynamics of the term structure of futures prices parsimoniously and are used to forecast the subsequent daily changes of futures prices. Data on the New York Mercantile Exchange (NYMEX) crude oil, heating oil, gasoline, and the International Petroleum Exchange (IPE) crude oil futures are used. We find that the retained principal components have small forecasting power both in-sample and out-of-sample. Similar results are obtained from standard univariate and vector autoregression models. Spillover effects between the four petroleum futures markets are also detected.
Download or read book Evolution of Crude Oil Price Term Structure written by Georgi Marinov and published by LAP Lambert Academic Publishing. This book was released on 2011-04 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of the book is on the lessons that can be learned by "reading" the crude oil price term structure correctly - i.e. in formation of trading strategies or making forecasts. The analysis goes back to the period when trading crude oil was becoming popular and traces the conditions that led to the inevitable need for creating oil futures and markets where they are traded. The main aim of the book is to go into the details of the crude oil price term structure in order to understand how it reflects the prevailing market conditions and how the various market participants use it in their strategic decision-making activities. This book could be helpful to students who are interested in crude oil and the economics behind crude oil and crude oil products; to post-graduate students and researchers who are interested in deepening their knowledge of term structure as used in the crude oil industry; to professionals who want to explore the practical implications of concepts such as "contango" and "backwardation" and the hints one can get by interpreting correctly the crude oil price term structure for building a successful trading strategy.
Download or read book The Role of Speculation in Oil Markets written by Bassam Fattouh and published by . This book was released on 2012 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting the Term Structure of Volatility of Crude Oil Price Changes written by Ercan Balaban and published by . This book was released on 2017 with total page 3 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Short term Forecasting Model for Crude Oil Price Based on Artificial Neural Networks written by Imad Haidar and published by . This book was released on 2008 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis examines the ability of Artificial Neural Networks (ANN) to predict crude oil spot price direction and short-term trends." --Abstract.
Download or read book The Term Structure of Oil Futures Prices written by Jacques Gabillon and published by . This book was released on 1991-01-01 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Advances in Processing Nonlinear Dynamic Signals written by Anna Esposito and published by Springer. This book was released on 2018-07-21 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors, and perturbations in the automatic control of industrial production processes. It also discusses the drastic changes in financial, economic, and work processes that are currently being experienced by the computational and engineering sciences community. Addresses key aspects, such as the integration of neural algorithms and procedures for the recognition, the analysis and detection of dynamic complex structures and the implementation of systems for discovering patterns in data, the book highlights the commonalities between computational intelligence (CI) and information and communications technologies (ICT) to promote transversal skills and sophisticated processing techniques. This book is a valuable resource for a. The academic research community b. The ICT market c. PhD students and early stage researchers d. Companies, research institutes e. Representatives from industry and standardization bodies
Download or read book Shock Propagation Across the Futures Term Structure written by Delphine Lautier and published by . This book was released on 2018 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: To what extent are futures prices interconnected across the maturity curve? Where in the term structure do price shocks originate, and which maturities do they reach? We propose a new approach, based on information theory, to study these cross-maturity linkages and the extent to which connectedness is impacted by market events. We introduce the concepts of backward and forward information flows, and propose a novel type of directed graph, to investigate the propagation of price shocks across the WTI term structure. Using daily data, we show that the mutual information shared by contracts with different maturities increases substantially starting in 2004, falls back sharply in 2011-2014, and recovers thereafter. Our findings point to a puzzling re-segmentation by maturity of the WTI market in 2012-2014. We document that, on average, short-dated futures emit more information than do backdated contracts. Importantly, however, we also show that significant amounts of information flow backwards along the maturity curve - almost always from intermediate maturities, but at times even from far-dated contracts. These backward flows are especially strong and far-reaching amid the 2007-2008 oil price boom/bust.
Download or read book Big Data in Energy Economics written by Hui Liu and published by Springer Nature. This book was released on 2022-02-08 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book combines energy economics and big data modeling analysis in energy conversion and management and comprehensively introduces the relevant theories, key technologies, and application examples of the smart energy economy. With the help of time series big data modeling results, energy economy managers develop reasonable and feasible pricing mechanisms of electricity price and improve the absorption capacity of the power grid. In addition, they also carry out scientific power equipment scheduling and cost–benefit analysis according to the results of data mining, so as to avoid the loss caused by accidental damage of equipment. Energy users adjust their power consumption behavior through the modeling results provided and achieve the effect of energy saving and emission reduction while reasonably reducing the electricity expenditure. This book provides an important reference for professionals in related fields such as smart energy, smart economy, energy Internet, artificial intelligence, energy economics and policy.
Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by MDPI. This book was released on 2020-04-01 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
