The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Loretta Mastroeni - One of the best experts on this subject based on the ideXlab platform.
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a reappraisal of the chaotic paradigm for Energy Commodity prices
Energy Economics, 2019Co-Authors: Loretta Mastroeni, Pierluigi Vellucci, Maurizio NaldiAbstract:Abstract Energy Commodity prices have been examined over the last 20 years to detect the presence of chaos as an alternative to stochastic models, with contrasting results. In this paper, we wish to reassess the chaotic paradigm in the light of two new pieces of information with respect to the literature: the appearance of noise-aware estimation methods for the correlation entropy and the availability of longer time series. Our analysis shows that the literature has heavily underestimated the presence of noise, and that chaotic characteristics coexist with stochastic ones in the time series of prices. Through the recurrence quantification analysis, we have also observed the presence of intermittency, where periods of regular behaviour are replaced by periods of chaotic behaviour, which could explain the emergence of bubbles.
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on the predictability of Energy Commodity markets by an entropy based computational method
Energy Economics, 2016Co-Authors: Francesco Benedetto, Gaetano Giunta, Loretta MastroeniAbstract:This paper proposes a novel computational method for assessing the predictability of Commodity market time series, by predicting the entropy of the series under investigation. Assessing the predictability of a time series is the first mandatory step in order to further apply low-risk and efficient price forecasting methods. According to conventional entropy-based analysis (where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict (i.e. ex-ante) the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, usually used in many computational aspects of management science. Preliminary results, applied to Energy Commodity futures, show the effectiveness of the proposed method for application to Energy market time series.
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Maximum entropy estimator for the predictability of Energy Commodity market time series
2014Co-Authors: Francesco Benedetto, Gaetano Giunta, Loretta MastroeniAbstract:This paper proposes a novel method for assessing the predictability of Energy market time series, by predicting the entropy of the series. According to conventional entropy-based analysis where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict ex-ante the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm. Preliminary results, applied to Energy commodities, show the efficacy of the proposed method for application to Energy market time series.
John Baffes - One of the best experts on this subject based on the ideXlab platform.
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More On The Energy / Non-Energy Commodity Price Link - More on the Energy / non-Energy Commodity price link
Policy Research Working Papers, 2009Co-Authors: John BaffesAbstract:This paper examines the Energy/non-Energy Commodity price link, based on a reduced form econometric model and using annual data from 1960 to 2008. The transmission elasticity from Energy to the non-Energy index is estimated at 0.28. At a more disaggregated level, the fertilizer index exhibited the largest elasticity (0.55), followed by precious metals (0.46), food (0.27), metals and minerals (0.25), and raw materials (0.11). By contrast, only a few price indices responded strongly to inflation, although the trend parameter estimate (often viewed as a proxy for technological progress) is negative for agriculture and positive for metals. A key implication of the pass-through results is that for as long as Energy prices remain elevated, most non-Energy Commodity prices are expected to be high.
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more on the Energy non Energy Commodity price link
2009Co-Authors: John BaffesAbstract:This paper examines the Energy/non-Energy Commodity price link, based on a reduced form econometric model and using annual data from 1960 to 2008. The transmission elasticity from Energy to the non-Energy index is estimated at 0.28. At a more disaggregated level, the fertilizer index exhibited the largest elasticity (0.55), followed by precious metals (0.46), food (0.27), metals and minerals (0.25), and raw materials (0.11). By contrast, only a few price indices responded strongly to inflation, although the trend parameter estimate (often viewed as a proxy for technological progress) is negative for agriculture and positive for metals. A key implication of the pass-through results is that for as long as Energy prices remain elevated, most non-Energy Commodity prices are expected to be high.
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oil spills on other commodities
Resources Policy, 2007Co-Authors: John BaffesAbstract:This paper examines the effect of crude oil prices on the prices of 35 internationally traded primary commodities for the 1960-2005 period. It finds that the pass-through of crude oil price changes to the overall non-Energy Commodity index is 0.16. At a more disaggregated level, the fertilizer index had the highest pass-through (0.33), followed by agriculture (0.17), and metals (0.11). The prices of precious metals also exhibited a strong response to the crude oil price. In terms of individual commodities, the estimates of the food group exhibited remarkable similarity while those of raw materials and metals gave a mixed picture. The implication is that if crude oil prices remain high for some time, as most analysts expect, then the recent Commodity price boom is likely to last much longer than earlier booms, at least for food commodities. The other commodities, however, are likely to follow diverging paths. On the methodological side, the results show that price indices, while providing useful summary statistics, need to be supplemented by individual Commodity analysis.
Massimo Panella - One of the best experts on this subject based on the ideXlab platform.
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A higher-order fuzzy neural network for modeling financial time series
2014 International Joint Conference on Neural Networks (IJCNN), 2014Co-Authors: Massimo Panella, Luca Liparulo, Andrea ProiettiAbstract:This work investigates on the widespread use of fuzzy neural networks in time series forecasting, concerning in particular the Energy Commodity markets. We propose a new learning strategy suited to any neural model. The proposed approach is further assessed in the case of higher-order Sugeno-type fuzzy rules, which are able to replicate the daily data and to reproduce the same statistical features for various Commodity time series. The data used are obtained from the daily return series of specific Energy commodities, such as coal, natural gas, crude oil and electricity, over the period 2001-2010 for both the European and US markets. We will prove that our approach can obtain interesting results in terms of prediction accuracy and volatility estimation, compared to well-known neural and fuzzy neural models and to the ARMA-GARCH statistical paradigm.
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Subband prediction of Energy Commodity prices
2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012Co-Authors: Massimo Panella, Francesco Barcellona, Rita L. D'ecclesiaAbstract:The dynamics of Commodity prices has become a major field of analysis in the last 20 years. Standard econometric procedures to describe the behavior of prices have not been able to provide accurate description of the real dynamics. In this paper we apply filter banks to predict prices of specific Energy commodities: crude oil, natural gas and electricity, which play a crucial role in the international economic and financial context. Given the high volatility of Energy Commodity prices, an accurate short term prediction allows to set adequate risk management strategies for producers, retailers and consumers. Filter banks for subband decompositions of the sequences to be predicted are proposed in the paper, allowing the implementation of a parallel computing system to get faster and more accurate implementation. The prediction system is based on a neural model trained on each subband according to specific training and prediction techniques.
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SPAWC - Subband prediction of Energy Commodity prices
2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012Co-Authors: Massimo Panella, Francesco Barcellona, Rita L. D’ecclesiaAbstract:The dynamics of Commodity prices has become a major field of analysis in the last 20 years. Standard econometric procedures to describe the behavior of prices have not been able to provide accurate description of the real dynamics. In this paper we apply filter banks to predict prices of specific Energy commodities: crude oil, natural gas and electricity, which play a crucial role in the international economic and financial context. Given the high volatility of Energy Commodity prices, an accurate short term prediction allows to set adequate risk management strategies for producers, retailers and consumers. Filter banks for subband decompositions of the sequences to be predicted are proposed in the paper, allowing the implementation of a parallel computing system to get faster and more accurate implementation. The prediction system is based on a neural model trained on each subband according to specific training and prediction techniques.
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Forecasting Energy Commodity prices using neural networks
Advances in Decision Sciences, 2012Co-Authors: Massimo Panella, Francesco Barcellona, Rita L. D'ecclesiaAbstract:A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast Energy Commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics.
Nicolas Koch - One of the best experts on this subject based on the ideXlab platform.
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tail events a new approach to understanding extreme Energy Commodity prices
Energy Economics, 2014Co-Authors: Nicolas KochAbstract:This paper shows that extreme Energy price changes, located in the 10% tails of the distribution, cluster across Energy futures markets during the boom–bust cycle of 2006 to 2012. Using multinominal logit regressions, we find that the coincidence of such tail events cannot be explained solely by common supply and demand fundamentals. Instead, we provide evidence that the transmission of extreme price changes occurs through a financial demand channel. Specifically, changes in the net long position of hedge funds are associated with a significant increase in the probability of coincident large positive and negative returns across Energy markets. Evidence that index investments drive tail events is limited. Further, we identify adverse shocks to speculator funding liquidity as determinant of synchronized price drops across Energy markets. The likelihood of extreme negative returns in more than one market significantly increases when the TED spread rises.
Cheng Feng Wu - One of the best experts on this subject based on the ideXlab platform.
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Energy Commodity price forecasting with deep multiple kernel learning
Energies, 2018Co-Authors: Shianchang Huang, Cheng Feng WuAbstract:Oil is an important Energy Commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions, which provides us with sufficient information to predict them. Traditional models are linear and parametric, and are not very effective in predicting oil prices. To address these problems, this study developed a new strategy. Deep (or hierarchical) multiple kernel learning (DMKL) was used to predict the oil price time series. Traditional methods from statistics and machine learning usually involve shallow models; however, they are unable to fully represent complex, compositional, and hierarchical data features. This explains why traditional methods fail to track oil price dynamics. This study aimed to solve this problem by combining deep learning and multiple kernel machines using information from oil, gold, and currency markets. DMKL is good at exploiting multiple information sources. It can effectively identify the relevant information and simultaneously select an apposite data representation. The kernels of DMKL were embedded in a directed acyclic graph (DAG), which is a deep model and efficient at representing complex and compositional data features. This provided a solid foundation for extracting the key features of oil price dynamics. By using real data for empirical testing, our new system robustly outperformed traditional models and significantly reduced the forecasting errors.