Bibliographies thématiques / Commodity price dynamics

Littérature scientifique sur le sujet « Commodity price dynamics »

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Publié le 11 mars 2023

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Articles de revues sur le sujet "Commodity price dynamics"

Onour, Ibrahim. « Dynamics of Crude Oil Price Change and Global Food Commodity Prices ». Finance & ; Economics Review 3, n^o 1 (28 avril 2021) : 38–50. http://dx.doi.org/10.38157/finance-economics-review.v3i1.248.

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Purpose: This study investigates the effect of crude oil price fluctuations (price change as well as volatility) on wheat, sugar, corn, and fertilizers price changes. Methods: The study employs Markov switching dynamic regression, Dynamic Conditional Correlation (DCC), and Generalized Autoregressive Conditional Hetrosekadicity (GARCH) on monthly data covering the period from January 1988 to April 2018. Results: The findings of the research support evidence of two states. State 1, pertains to the low volatility of crude oil price, and state 2 belong to the case of the high volatility of crude oil prices. Our results indicated that at state 1, an increase in crude oil prices leads to a decline in food commodity prices, while in state 2, an increase in crude oil price levels causes an increase in food commodity prices. Results of Dynamic Conditional Correlation (DCC) GARCH estimates indicate the coefficients of oil price levels are significant and positively associated with the conditional volatility of the four commodity prices. Implications: The findings of the research imply that volatility in global food commodity prices is not due to oil price volatility but due to the oil price levels attained at extreme points. Originality: The paper investigates the impact of different volatility levels of crude oil prices on global food commodity prices.

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Nikonenko, Uliana, Tetiana Shtets, Andrii Kalinin, Iryna Dorosh et Larysa Sokolik. « Assessing the Policy of Attracting Investments in the Main Sectors of the Economy in the Context of Introducing Aspects of Industry 4.0 ». International Journal of Sustainable Development and Planning 17, n^o 2 (26 avril 2022) : 497–505. http://dx.doi.org/10.18280/ijsdp.170214.

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The purpose of this work is to study and evaluate the impact of world commodity prices on the dynamics of investment in exporting countries of natural resources using the developed methodology under the influence of industry 4.0 aspects. Modern economic activity is accompanied not only by the impact of COVID-19, but also by the impact of the first manifestations of industry 4.0. This applies not only to export-import operations but also to the very need for them due to the cost of new technologies. Using mathematical methods, we investigate the impact of world commodity price indices, in particular, the general commodity price index, the agricultural commodity price index, the food price index, the metal price index, and the crude oil price index, on the dynamics of investment in commodity-type economies in both dimensions – level and volatility. The innovativeness of the study lies in determining the significance of the impact of world commodity prices on the dynamics of foreign direct investment (FDI) of raw material exporting countries (on the example of three groups of countries with different levels of economic development). The proposed methodology makes it possible to empirically evaluate the mechanisms of the macroeconomic impact of commodity prices on investment dynamics.

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Rizvi, Syed Aun R., et Sahminan Sahminan. « COMMODITY PRICE AND INFLATION DYNAMICS : EVIDENCE FROM BRIICS ». Buletin Ekonomi Moneter dan Perbankan 23, n^o 4 (22 janvier 2021) : 485–500. http://dx.doi.org/10.21098/bemp.v23i4.1418.

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In this study, we use a commodity augmented Phillips curve to investigate the impact of global commodity prices on domestic inflation in Brazil, Russia, India, Indonesia, China, and South Africa. Oil and energy prices cause inflationary pressures in all countries, except Russia, where they cause deflationary pressures. In Indiaand Indonesia, global food prices are highly significant and positively related to inflation, while in South Africa precious metal prices impact inflation negatively. For policymakers, this study provides insights on the domestic adjustments required for inflation targeting in response to global commodity price volatility.

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Pindyck, Robert S. « Volatility and commodity price dynamics ». Journal of Futures Markets 24, n^o 11 (2004) : 1029–47. http://dx.doi.org/10.1002/fut.20120.

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Gnidchenko, A., et V. Salnikov. « Russian foreign trade price competitiveness ». Voprosy Ekonomiki, n^o 1 (20 janvier 2014) : 108–29. http://dx.doi.org/10.32609/0042-8736-2014-1-108-129.

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We examine export and import prices for Russian commodities relative to world prices during 2002—2011 across aggregated and disaggregated commodity groups. We also propose an aggregated export price competitiveness index as a tool of monitoring quality dynamics and a composite price competitiveness rating by commodity groups.

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He, Xue-Zhong, et Frank H. Westerhoff. « Commodity markets, price limiters and speculative price dynamics ». Journal of Economic Dynamics and Control 29, n^o 9 (septembre 2005) : 1577–96. http://dx.doi.org/10.1016/j.jedc.2004.09.003.

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Eickmeier, Sandra, et Markus Kühnlenz. « CHINA'S ROLE IN GLOBAL INFLATION DYNAMICS ». Macroeconomic Dynamics 22, n^o 2 (28 septembre 2016) : 225–54. http://dx.doi.org/10.1017/s1365100516000158.

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We apply a structural dynamic factor model to a large quarterly data set covering 38 countries between 2002 and 2011 to analyze China's role in global inflation dynamics. We identify Chinese supply and demand shocks and examine their contributions to global price dynamics and the transmission mechanism. Our main findings are as follows: (i) Chinese supply and demand shocks affect prices in other countries significantly. Demand shocks matter slightly more than supply shocks. Producer prices tend to be more strongly affected than consumer prices by Chinese shocks. The overall share of international inflation explained by Chinese shocks is notable (about 6 percent on the average over all countries but not more than 13 percent in each region). (ii) Direct channels (via import and export prices) and indirect channels (via greater exposure to foreign competition and commodity prices) both matter. (iii) Differences in trade and in commodity exposure help explain cross-country differences in price responses.

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Lai, Ching‐chong, Shih‐wen Hu et Vey Wang. « Commodity Price Dynamics and Anticipated Shocks ». American Journal of Agricultural Economics 78, n^o 4 (novembre 1996) : 982–90. http://dx.doi.org/10.2307/1243854.

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Deaton, Angus, et Guy Laroque. « Competitive Storage and Commodity Price Dynamics ». Journal of Political Economy 104, n^o 5 (octobre 1996) : 896–923. http://dx.doi.org/10.1086/262046.

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Thèses sur le sujet "Commodity price dynamics"

Alam, Md Rafayet. « MACROECONOMIC ASPECTS OF COMMODITY PRICE DYNAMICS ». OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1175.

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Fluctuation in commodity prices is a significant and timely issue to be studied. My first chapter examines the impact of monetary policy and other macroeconomic shocks on the dynamics of agricultural commodity prices. The major contributions of this study are twofold. First, unlike other studies that use indexes, this study analyzes the commodities individually, affording the inclusion of commodity-specific fundamentals such as the level of inventory -- an important determinant of commodity price -- in a structural VAR framework. Second, it exploits a rich dataset of agricultural commodity prices which includes commodities that are usually overlooked in the literature, and extracts a common factor using the dynamic factor model to understand the extent of co-movement of the prices and to gauge the extent to which macroeconomic shocks drive the ‘co-movement’ in a factor-augmented VAR (FAVAR) framework. The findings show that monetary policy, global economic conditions and the US dollar exchange rates play an important role in the dynamics of agricultural commodity prices. My second chapter examines the role played by Wal-Mart in price convergence among US cities. Despite the fact that market structure is an important determinant of price convergence and that US retail architecture has been changed over the past two decades by the expansion of big box stores and supercenters, the role played by such rapidly-expanding ‘big-box’ chain-stores like Wal-Mart in price convergence is completely over-looked in the literature. The possible symmetry in costs and mark-up among Wal-Mart stores, and their influence over the city level prices motivate us to test if their presence helps price convergence among US cities. After controlling for distance, local costs such as wage and rent, and city and time specific fixed effects this study finds that prices are significantly closer in two cities if they have Wal-Mart than if none or only one of them has Wal-Mart. Though the results are mostly robust to the analysis using disaggregate price data and sub-samples, they are more pronounced for grocery items than non-grocery items, within high income cities than low income cities. Moreover, our regional analysis uncovers the regional variations in the effect of Wal-Mart on price convergence, and Wal-Mart’s more prominent role in inter-region rather than intra-region price convergence. Since the presence of Wal-Mart accelerates the rate of price convergence and thus reduces the potential for misallocation of resources, our results suggest that the existence of a positive welfare impact of Wal-Mart cannot be overruled. My third chapter uses county level data to see the effect of Wal-Mart on local economic activities and revenue in Florida. The OLS estimation shows that the presence of Wal-Mart significantly increases total retail sales and decreases sales tax rate, but have no significant effect on total taxable retail sales and total revenue from sales tax. The instrumental variable (IV) estimation shows that presence of Wal-Mart significantly decreases sales tax rate but has no significant effect on total retail sales, total taxable retail sales and total revenue from sales tax. Thus, according to our analysis, Wal-Mart does not necessarily increase local economic activities and tax revenue. However, interestingly, Wal-Mart is found to play an important role in decreasing local sales-tax rate.

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Zhou, Feng. « Nonparametric Analysis of Commodity Futures Price Dynamics and Market Risk Measurements ». The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376578061.

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He, Dequan. « MODELING TRANSACTIONS COSTS BAND AND NONLINEAR PRICE DYNAMICS IN FOREST COMMODITY MARKETS ». NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-07212005-080614/.

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This study first shows that a transactions costs band may exist in commodity spot and futures markets and spatially separated markets as a result of the arbitrage process. Then, by using a bivariate vector error correction model (VECM), this thesis shows that the null hypothesis of linearity can be rejected against the alternative of nonlinearity for both lumber spot and futures prices in U.S. and oriented strand board (OSB) prices across regions in North America. The nonlinearity is identified by a transition variable that governs switching between two regimes: one within the transactions cost band and one outside of the band. In the empirical analysis, a bivariate smooth transition vector error correction model (STVECM) is used to test market efficiency for lumber spot and futures prices and for the law of one price (LOP) as pertains to OSB prices across six regions in North America. Results support the market eficiency hypothesis and the LOP in the forest commdity markets. Furthermore, the empirical analysis suggests that when price differences surpass transactions costs by a large margin or are far away from the transactions band, a faster adjustment to the long run equilibrium is observed in part due to adjustment costs. When price differences are within the transactions band, they follow a random walk as no trade takes place. The in-sample analysis indicates that the STVECM model performs better than the linear VECM. Results from generalized impulse response functions (GIs) analysis show that system shocks may, in fact, change the time paths of prices permanently.

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Howell, James Andreas. « An analysis of speculator behavior and the dynamics of price in a futures market ». Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/24847.

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CASOLI, CHIARA. « The dynamics of commodity prices : common movement and latent factors ». Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274073.

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Questa tesi analizza prezzi di diverse tipologie di commodities, con lo scopo di capire se esiste un movimento comune tra le varie serie, e se questo è il risultato di determinanti comuni di breve periodo o deriva da delle dinamiche condivise di lungo periodo. Il lavoro è organizzato in tre capitoli. Il primo introduce l’argomento del movimento comune, focalizzandosi anche in una rassegna della letteratura sulle dinamiche dei prezzi delle commodities e proponendo un’analisi univariata di 38 serie storiche mensili di prezzi spot del database dell’IMF. Il secondo capitolo propone un modello in tre equazioni strutturali (consumo, produzione e stoccaggio), con l’aggiunta della condizione di equilibrio, in cui due fattori latenti responsabili per il co-movimento sono incorporati. L’equazione di prezzo in forma ridotta è poi stimata per 10 prezzi sfruttando la metodologia proposta nel terzo capitolo. Quest’ultimo sviluppa una nuova tecnica di stima per Dynamic Factor Models non stazionari e cointegrati con una decomposizione trend-ciclo; inoltre, la procedura è applicata alle 38 serie storiche presentate nel primo capitolo. Dai risultati emerge che mentre la componente ciclica è quasi irrilevante, la parte non stazionaria del co-movimento catturata dalla componente trend ha più peso. Dai fattori estratti è impossibile concludere con chiarezza se l’ipotesi di Prebish e Singer del trend decrescente dei prezzi delle commodities nel lungo periodo è stata surclassata da un’epoca di prezi crescenti dovuta alla maggiore scarsità delle risorse, con conseguente pressione della domanda sull’offerta.
This thesis concerns an analysis of commodity prices belonging to different categories, with the aim of understanding if there is room for a common movement among different price series, and if this co-movement is the result of short-run common drivers or it implies a long-run shared dynamic. The analysis is developed in three Chapters. Chapter 1 introduces the topic of co-movement, also focusing in reviewing other studies on commodity prices general dynamics and providing some univariate results by exploiting a set of 38 commodity spot monthly prices available from the IMF primary commodity database. Chapter 2 proposes a first attempt of modelling commodity markets by including latent factors responsible for co-movement. The model consists in three structural equations determining consumption, production and storage on a multi-commodity framework, plus a market clearing condition which allows to find the equilibrium price. The reduced form model is then estimated for a subset of 10 commodity prices by exploiting the methodology developed in Chapter 3, which contributes both to propose a new estimation procedure for non-stationary and cointegrated Dynamic Factor Models with a Trend Cycle decomposition and further exploits this methodology to empirically assess the co-movement of the 38 commodity prices considered in the firts Chapter. Results assess that whether the short-run common movement of commodity prices is rather marginal, the non-stationary Trend component has more weight. From the extraction of both stationary and non-stationary factors, neither the so called Prebish and Singer Hypothesis of declining commodity prices (with respect to manufactured good prices) nor a paradigm shift due to increasing resource scarcity and consequent higher demand pressure can be fully confirmed.

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Otunuga, Olusegun Michael. « Stochastic Modeling and Analysis of Energy Commodity Spot Price Processes ». Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5289.

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Supply and demand in the World oil market are balanced through responses to price movement with considerable complexity in the evolution of underlying supply-demand expectation process. In order to be able to understand the price balancing process, it is important to know the economic forces and the behavior of energy commodity spot price processes. The relationship between the different energy sources and its utility together with uncertainty also play a role in many important energy issues. The qualitative and quantitative behavior of energy commodities in which the trend in price of one commodity coincides with the trend in price of other commodities, have always raised the questions regarding their interactions. Moreover, if there is any interaction, then one would like to know the extent of influence on each other. In this work, we undertake the study to shed a light on the above highlighted processes and issues. The presented study systematically deals with the development of stochastic dynamic models and mathematical, statistical and computational analysis of energy commodity spot price and interaction processes. Below we list the main components of the research carried out in this dissertation. (1) Employing basic economic principles, interconnected deterministic and stochastic models of linear log-spot and expected log-spot price processes coupled with non-linear volatility process are initiated. (2) Closed form solutions of the models are analyzed. (3) Introducing a change of probability measure, a risk-neutral interconnected stochastic model is derived. (4) Furthermore, under the risk-neutral measure, expectation of the square of volatility is reduced to a continuous-time deterministic delay differential equation. (5) The by-product of this exhibits the hereditary effects on the mean-square volatility process. (6) Using a numerical scheme, a time-series model is developed and utilized to estimate the state and parameters of the dynamic model. In fact, the developed time-series model includes the extended GARCH model as special case. (7) Using the Henry Hub natural gas data set, the usefulness of the linear interconnected stochastic models is outlined. (8) Using natural and basic economic ideas, interconnected deterministic and stochastic models in (1) are extended to non-linear log-spot price, expected log-spot price and volatility processes. (9) The presented extended models are validated. (10) Closed form solution and risk-neutral models of (8) are outlined. (11) To exhibit the usefulness of the non-linear interconnected stochastic model, to increase the efficiency and to reduce the magnitude of error, it was essential to develop a modified version of extended Kalman filtering approach. The modified approach exhibits the reduction of magnitude of error. Furthermore, Henry Hub natural gas data set is used to show the advantages of the non-linear interconnected stochastic model. (12) Parameter and state estimation problems of continuous time non-linear stochastic dynamic process is motivated to initiate an alternative innovative approach. This led to introduce the concept of statistic processes, namely, local sample mean and sample variance. (13) Then it led to the development of an interconnected discrete-time dynamic system of local statistic processes and (14) its mathematical model. (15) This paved the way for developing an innovative approach referred as Local Lagged adapted Generalized Method of Moments (LLGMM). This approach exhibits the balance between model specification and model prescription of continuous time dynamic processes. (16) In addition, it motivated to initiate conceptual computational state and parameter estimation and simulation schemes that generates a mean square sub-optimal procedure. (17) The usefulness of this approach is illustrated by applying this technique to four energy commodity data sets, the U. S. Treasury Bill Yield Interest Rate and the U.S. Eurocurrency Exchange Rate data sets for state and parameter estimation problems. (18) Moreover, the forecasting and confidence-interval problems are also investigated. (19) The non-linear interconnected stochastic model (8) was further extended to multivariate interconnected energy commodities and sources with and without external random intervention processes. (20) Moreover, it was essential to extend the interconnected discrete-time dynamic system of local sample mean and variance processes to multivariate discrete-time dynamic system. (21) Extending the LLGMM approach in (15) to a multivariate interconnected stochastic dynamic model under intervention process, the parameters in the multivariate interconnected stochastic model are estimated. These estimated parameters help in analyzing the short term and long term relationship between the energy commodities. These developed results are applied to the Henry Hub natural gas, crude oil and coal data sets.

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CIOCIOLA, GIUSEPPE. « Dynamics of Commodity Prices. A Potential Function Approach with Numerical Implementation ». Doctoral thesis, Università degli studi di Bergamo, 2013. http://hdl.handle.net/10446/28630.

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In the present analysis a nonlinear model is discussed in order to capture the presence of several forces acting in commodity markets and the difficulty to disentangle their relative price impacts. Global commodity markets have experienced significant price swings in recent years. Analysts offer two explanations: market forces and speculative expectations, not mutually exclusive. Commodity prices seem to indicate that various factors are acting in a very complex way. We start from one specific feature: price clustering phenomenon, which is the tendency to concentrate in a number of attraction regions, preferring some values over others. Commodities are in the process of becoming mainstream. The mean-reverting class of diffusion models are not able to model the phenomenon of multiple attraction regions. In the potential function approach the price is modelled as a diffusion process governed by a potential function. A fundamental step is to fit the multimodal density of the invariant distribution. We postulate a parametric form of the distribution in the framework of finite mixture models and Expectation-Maximization algorithm. The procedure for identifying and estimating potential function and diffusion parameter is provided. Applications to crude oil and soybean prices capture the essential characteristics of the data remarkably well. An underlying assumption is that potential function and long-term volatility do not change with time. New market conditions and new attraction regions can form, changing the shape of the potential and the magnitude of long-term volatility. We investigate changes in shape of the potential, which reflects new price equilibrium levels (attraction regions) and hence new market conditions. The model allows to generate copies of the observed price series with the same invariant distribution, useful for applications requiring a large number of independent price trajectories. A goodness-of-fit test for the SDE model is provided. A numerical implementation of the analysis is provided.

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Coulon, Michael. « Modelling price dynamics through fundamental relationships in electricity and other energy markets ». Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:ddc11641-920f-461f-85cd-a9e6351d9104.

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Energy markets feature a wide range of unusual price behaviour along with a complicated dependence structure between electricity, natural gas, coal and carbon, as well as other variables. We approach this broad modelling challenge by firstly developing a structural framework to modelling spot electricity prices, through an analysis of the underlying supply and demand factors which drive power prices, and the relationship between them. We propose a stochastic model for fuel prices, power demand and generation capacity availability, as well as a parametric form for the bid stack function which maps these price drivers to the spot electricity price. Based on the intuition of cost-related bids from generators, the model describes mathematically how different fuel prices drive different portions of the bid stack (i.e., the merit order) and hence influence power prices at varying levels of demand. Using actual bid data, we find high correlations between the movements of bids and the corresponding fuel prices (coal and gas). We fit the model to the PJM and New England markets in the US, and assess the performance of the model, in terms of capturing key properties of simulated price trajectories, as well as comparing the model’s forward prices with observed data. We then discuss various mathematical techniques (explicit solutions, approximations, simulations and other numerical techniques) for calibrating to observed fuel and electricity forward curves, as well as for pricing of various single and multi-commodity options. The model reveals that natural gas prices are historically the primary driver of power prices over long horizons in both markets, with shorter term dynamics driven also by fluctuations in demand and reserve margin. However, the framework developed in this thesis is very flexible and able to adapt to different markets or changing conditions, as well as capturing automatically the possibility of changes in the merit order of fuels. In particular, it allows us to begin to understand price movements in the recently-formed carbon emissions markets, which add a new level of complexity to energy price modelling. Thus, the bid stack model can be viewed as more than just an original and elegant new approach to spot electricity prices, but also a convenient and intuitive tool for understanding risks and pricing contracts in the global energy markets, an important, rapidly-growing and fascinating area of research.

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Cremaschi, Damien. « Prix des matières premières dans le domaine automobile : une analyse économétrique de la dynamique du prix des plastiques ». Thesis, Paris 9, 2012. http://www.theses.fr/2012PA090060.

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Le secteur automobile est de plus en plus dépendant aux matières plastiques dont le niveau et la volatilité des prix ont fortement augmenté au cours des dix dernières années, sous l’effet supposé des variations du prix du pétrole qui est le principal input nécessaire à leur fabrication. La thèse vise à fournir des outils économétriques permettant d’analyser et gérer le risque de variations des prix des principales matières plastiques utilisées dans l’industrie automobile. À l’aide des méthodologies de cointégration, nous montrons que les relations d’équilibre de long terme et les dynamiques de court terme mettent en évidence un mécanisme de transmission des variations des coûts de production sur le prix des plastiques situés en aval du processus productif. L’existence de relations de cointégration significatives entre les prix pétrochimiques et pétroliers justifie l’élaboration de stratégies de couverture contre les variations des coûts de production et l’estimation de modèles à correction d’erreur qui permettent d’affiner les prévisions des prix
The automotive industry is increasingly dependent on plastic materials whose price level and volatility have risen sharply over the past decade due to the assumed effect of fluctuations in crude oil prices, which is the key feedstock in the production of final products such as plastics. This thesis aims to provide econometric tools to analyze, understand, and manage the risk of price volatility of major plastics materials consumed in the automotive industry. Using the cointegration methodology, we show that long-term equilibrium relationship and short-term dynamics reveal the transmission mechanism of input prices changes from the upstream market to the prices of plastics materials on the downstream market. The significant cointegration relationships between petrochemical and crude oil prices justify the development of hedging strategies against inputs prices fluctuation and the estimation of error correction models that should produce better prices forecast

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Antonakakis, Nikolaos, et Renatas Kizys. « Dynamic Spillovers between Commodity and Currency Markets ». Elsevier, 2015. http://dx.doi.org/10.1016/j.irfa.2015.01.016.

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In this study, we examine the dynamic link between returns and volatility of commodities and currency markets. Based on weekly data over the period from January 6, 1987 to July 22, 2014, we find the following empirical regularities. First, our results suggest that the information contents of gold, silver, platinum, and the CHF/USD and GBP/USD exchange rates can help improve forecast accuracy of returns and volatilities of palladium, crude oil and the EUR/CHF and GBP/USD exchange rates. Second, gold (CHF/USD) is the dominant commodity (currency) transmitter of return and volatility spillovers to the remaining assets in our model. Third, the analysis of dynamic spillovers shows time{ and event{specific patterns. For instance, the dynamic spillover effects originating in gold and silver (platinum) returns and volatility intensified (degraded) in the period marked by the global financial crisis. After the global financial crisis, the net transmitting role of gold and silver (platinum) returns shocks weakened (strengthened), while the net transmitting role of gold, silver and platinum volatility shocks remained relatively high. Overall, our findings reveal that, while the static analysis clearly classifies the aforementioned variables into net transmitters and net receivers, the dynamic analysis denotes episodes wherein the role of transmitters and receivers of return (volatility) spillovers can be interrupted or even reversed. Hence, even if certain commonalities prevail in each identified category of commodities, such commonalities are time - and event - dependent. (authors' abstract)

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Livres sur le sujet "Commodity price dynamics"

Commodity price dynamics : A structural approach. Cambridge : Cambridge University Press, 2012.

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Pindyck, Robert S. Inventories and the short-run dynamics of commodity prices. Cambridge, MA : National Bureau of Economic Research, 1990.

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Yabuki, Nanae. Is commodity-dependence pessimism justified ? : Critical factors and government policies that characterize dynamic commodity sectors. Washington, DC : World Bank, International Economics Dept., Commodity Policy and Analysis Unit, 1996.

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Pirrong, Craig. Commodity Price Dynamics : A Structural Approach. Cambridge University Press, 2011.

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Pirrong, Craig. Commodity Price Dynamics : A Structural Approach. Cambridge University Press, 2012.

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Pirrong, Craig. Commodity Price Dynamics : A Structural Approach. Cambridge University Press, 2011.

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Pirrong, Craig. Commodity Price Dynamics : A Structural Approach. Cambridge University Press, 2014.

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Pirrong, Craig. Commodity Price Dynamics : A Structural Approach. Cambridge University Press, 2012.

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Medina, Leandro. Dynamic Effects of Commodity Prices on Fiscal Performance in Latin America. International Monetary Fund, 2010.

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Medina, Leandro. Dynamic Effects of Commodity Prices on Fiscal Performance in Latin America. International Monetary Fund, 2010.

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Chapitres de livres sur le sujet "Commodity price dynamics"

Fujiwara, Yoshi, Hideaki Aoyama, Hiroshi Iyetomi et Hiroshi Yoshikawa. « Dynamics of Commodity Price Fluctuations in Japan ». Dans Proceedings of ECCS 2014, 297–308. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29228-1_25.

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Saadi, Hadj. « Price Co-movements in International Markets and Their Impacts on Price Dynamics ». Dans Methods to Analyse Agricultural Commodity Price Volatility, 149–63. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7634-5_9.

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Lee, Jing You, Wan Yi Tan et Siok Kun Sek. « Commodity Price Persistency and Dynamics : A Smooth Transition Regression Approach ». Dans Studies in Systems, Decision and Control, 511–25. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_33.

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Brooks, Chris, et Marcel Prokopczuk. « The Dynamics of Commodity Prices ». Dans Commodities, 399–422. 2^e éd. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003265399-22.

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Clewlow, Les, Boda Kang et Christina Sklibosios Nikitopoulos. « On the Volatility of Commodity Futures Prices ». Dans Nonlinear Economic Dynamics and Financial Modelling, 315–34. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07470-2_18.

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Schwarz, Thomas, Hans-Joachim Lenz et Wilhelm Dominik. « Long-Term Projections for Commodity Prices—The Crude Oil Price Using Dynamic Bayesian Networks ». Dans Operations Research Proceedings, 81–87. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89920-6_12.

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Coronado, Semei, Omar Rojas, Rafael Romero-Meza, Apostolos Serletis et Leslie Verteramo Chiu. « Crude Oil and Biofuel Agricultural Commodity Prices ». Dans Dynamic Modeling and Econometrics in Economics and Finance, 107–23. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98714-9_5.

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Chodchuangnirun, Benchawanaree, Woraphon Yamaka et Chatchai Khiewngamdee. « A Regime Switching for Dynamic Conditional Correlation and GARCH : Application to Agricultural Commodity Prices and Market Risks ». Dans Lecture Notes in Computer Science, 289–301. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_24.

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Chadwick, Anna. « Regulation ». Dans Law and the Political Economy of Hunger, 107–35. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198823940.003.0005.

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Chapter 4 analyses the new regulations introduced in the US and in the EU to respond to ‘excessive’ levels of speculation in commodity derivative markets. First, the chapter recalls the events of the global financial crisis and relates how concerns about the role that derivatives played in bringing it about have motivated reform. The chapter then outlines the new US and European regulations for over-the-counter derivatives focusing on those provisions with the potential to address concerns about food commodity speculation. After discussing a number of challenges that threaten the regulatory project, the author exposes a number of deeper, structural limitations of the Dodd Frank Act and EMIR-MiFID II reforms. The regulations are revealed to be predicated on a problematic ‘Neoliberal-Neoclassical’ understanding of how financial markets function that fundamentally fails to account for dynamics of price formation in contemporary markets.

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Nadeau, Robert. « The Old Story : Economic Globalization, the Market Consensus, and the New State Religion ». Dans Rebirth of the Sacred. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199942367.003.0011.

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The new york times editorial page attributed the lack of regulation that resulted in the meltdown of the financial markets in 2008 to the “Bush administration’s magical belief that the market, with its invisible hand, works best when it is left alone to self regulate and self correct.” But what the editorial failed to mention is that the Bush administration’s $700 billion economic stimulus plan and the Obama administration’s $789 billion American Recovery and Reinvestment Act were both predicated on this magical belief. The fundamental assumption in these plans was that the meltdown occurred because the self-correcting and self-regulating dynamics associated with the invisible hand ceased to function properly. And the intent of the plans was to create market conditions in which these dynamics could begin to function properly with a massive infusion of capital generated by deficit spending. This meltdown began after the collapse of the markets for derivative contracts that allow buyers to hedge against economic gains or losses. In the parlance of mainstream economists, a derivative is an agreement between two parties that the value of something is determined by the price movement of something else, and hedging allows a buyer or seller to protect assets or incomes against future rises in prices. In derivatives markets, debt is used to generate surplus capital, and this surplus is used to borrow increasingly larger sums of money in a process economists call financial leveraging. Traditional derivative trading was in commodity-related futures contracts, and the amount of debt that could be used as financial leverage was highly regulated. In these markets, buyers could hedge against unpredictable changes in the prices of real assets, such as wheat or cotton, and each commodity was traded separately. But this situation changed dramatically after December 2000, when the U.S. Congress banned the regulation of derivatives by passing the Commodity Futures Modernization Act. The rationale for passing this bill, which was largely written by representatives of the investment banks that would later make enormous profits in derivatives trading, appealed to two assumptions in neoclassical economic theory.

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Actes de conférences sur le sujet "Commodity price dynamics"

Tan, Li, Qi Zhong-ying et Niu Hong-yuan. « Stochastic beliefs learning and commodity futures price dynamics ». Dans 2010 International Conference on Management Science and Engineering (ICMSE). IEEE, 2010. http://dx.doi.org/10.1109/icmse.2010.5719945.

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Ildırar, Mustafa, et Erhan İşcan. « The Interaction between Stock Prices and Commodity Prices : East Europe and Central Asia Countries ». Dans International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01350.

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The sharp increase in commodity prices since 2000s has important effects on many economic variables. Especially the upward trend in commodity prices had substantial effects on stock prices. The literature has continuing and growing interest to the dynamics of commodity price and their significant impact on economic and financial developments. There is growing evidence that commodity prices, stock prices moved together, and that the correlations between them have increased. Many studies investigated the interaction between stock prices and real and commodity prices and find strong interaction for developed countries. However, the effect of the commodity prices on stock markets in relatively less investigated for ECA countries. The purpose of this study is to investigate the long-run relationship between commodity prices and stock prices in ECA countries can by using a panel cointegration test.

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Du, Xingwu, et Junwu Wang. « Analysis of Commodity Housing Price Factors from the Perspective of System Dynamics ». Dans International Conference on Construction and Real Estate Management 2017. Reston, VA : American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784481073.017.

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Wu, Zhenhua, Yun Li, Qin Tang et Yabei Wang. « Study on the System Dynamics Model and Simulation Data Analysis of the Commodity Residence Price in Shenzhen ». Dans International Conference on Construction and Real Estate Management 2013. Reston, VA : American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784413135.097.

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Jain, Neeti, et Niti Nandini Chatnani. « Financialization – Evidence from Dynamic Connectedness among Agricultural Index Futures ». Dans 8th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2022. http://dx.doi.org/10.31410/eraz.s.p.2022.35.

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The introduction of index futures was a landmark event for global commodity markets. It has been blamed by regulators and academicians for its role in food price surges from time to time. This paper examines the price discovery and volatility spillover relationship among agricultural index futures globally. Results from the study reveal that index futures play a dominant role in contributing to price discovery. The price leadership of the futures market, although found to be strong, is diminished in the presence of stringent regulatory trading curbs that were put in place as a response to the crisis. Furthermore, an improved Diebold & Yilmaz method based on TVP-VAR-SV model was used to analyze dynamic connectedness between the index and standalone contracts of agriculture commodity markets. The results show that the impacts on the net spillover of various indices are different. However, the evidence fails to support the argument that volatility is induced due to spillovers among the indices.

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Silva, Lucas Barth, Roberto Zanetti Freire et Osíris Canciglieri Junior. « Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine ». Dans Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-62.

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Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a computational intelligence model using Wavelet Transform on input values and the Extreme Machine Learning algorithm for training and prediction (W-ELM). The macro parameters were optimized using the Particle Swarm Optimization algorithm and for the selection of the input variables, a model based on Mutual Information (MI) was used. In the end, the methodology was compared with the traditional methods: Autoregressive Moving Averages (ARIMA) and General Autoregressive Conditional Heteroskedasticity (GARCH) models. Results showed that the W-ELM had better performance for forecasting 1 to 4 weeks of when compared to ARIMA. When the GARCH model results were considered, the proposed method provided worse performance only for 1 step ahead forecasting.

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De Santis, Alberto, Umberto Dellepiane et Stefano Lucidi. « A black box optimization approach to parameter estimation in a model for long/short term variations dynamics of commodity prices ». Dans 9TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES : ICNPAA 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4765506.

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Sim, Kwan-Hua, Kwan-Yong Sim et Patrick Then Hang-Hui. « Forecasting price volatility cluster of commodity futures index by using standard deviation with dynamic data sampling based on significant interval mined from historical data ». Dans 2014 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2014. http://dx.doi.org/10.1109/codit.2014.6996992.

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Lunney, Iain. « Cost-Effective Directional Drilling and Logging-While-Drilling Operational/Maintenance Model Aids an East Africa Operator to Deliver Its Remote Location Exploration Campaign ». Dans SPE/AAPG Africa Energy and Technology Conference. SPE, 2016. http://dx.doi.org/10.2118/afrc-2582954-ms.

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ABSTRACT In a cost-sensitive market driven by depressed commodity prices, significant capital challenges exist for operators interested in pursuing exploration activities in remote environments to define their producible reserves. This paper explores the organizational and operational model developed by a service company over several remote area mobilizations; this model resulted in an optimized low-cost service delivery model characterized by top quartile operational key performance indicators (KPIs). The model centralizes critical functions of an operational organization into discrete service units that are located near the operational location or that provide remote assistance with communication and reporting lines in place to function effectively. Top quartile operational performance and tool availability is a result of placing a remote repair and maintenance facility that includes containerized specialty modules near the operational area. The upfront bottomhole assembly engineering, 24/7 monitoring, and proactive feedback of logged data, drillstring dynamics, and wellbore hydraulics are performed by a core team of subject matter experts in their respective disciplines from an established centralized operating center. The operational KPIs over the course of the six well exploration campaign provided substantial evidence to support the reliability of the model and the high level of experience used in both the remote maintenance facility and the operations center support team.

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Rapports d'organisations sur le sujet "Commodity price dynamics"

Pindyck, Robert. Inventories and the Short-Run Dynamics of Commodity Prices. Cambridge, MA : National Bureau of Economic Research, mars 1990. http://dx.doi.org/10.3386/w3295.

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Heresi, Rodrigo. Reallocation and Productivity during Commodity Cycles. Inter-American Development Bank, avril 2021. http://dx.doi.org/10.18235/0003203.

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I study the firm-level dynamic response of a commodity-exporting economy to global cycles in commodity prices. To do so, I develop a heterogeneous-firms model that endogenizes declines in aggregate productivity through reallocation towards less productive firms. Within a given sector, commodity booms reallocate market share away from exporters because of currency appreciation and away from capital-intensive firms because of the increase in capital cost. I provide empirical evidence for these channels using microdata for Chile, the worlds largest copper producer. When fed with the commodity super-cycle of 2003-2012, the calibrated model generates about 50% of the observed productivity decline.

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Nin Pratt, Alejandro, et Héctor Valdés Conroy. After the Boom : Agriculture in Latin America and the Caribbean. Inter-American Development Bank, décembre 2020. http://dx.doi.org/10.18235/0002955.

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The convergence of a favorable macroeconomic environment and high prices of primary commodities between 2000 and 2011 contributed to the best performance of agriculture in Latin America and the Caribbean (LAC) since the 1980s, with steady growth of total factor productivity (TFP) and output per worker and a reduction in the use of input per worker. The end of the upward phase of the commodity cycle in 2011 together with less favorable external markets and a deterioration of the policy environment in several countries, motivates us to revisit the situation of agriculture in LAC in recent years to analyze how these changes have affected its performance. This study applies a framework that uses index numbers together with data envelopment analysis (DEA) to estimate levels of productivity and efficiency, incorporating technical change together with technical (TE) and environmental efficiency (EE) into the decomposition of TFP. The EE index adjusts the TFP measure for pollution, treating GHG emissions as a by-product of the desired crop or livestock outputs. TFP and efficiency of crop and livestock sub-sectors was calculated for 24 LAC countries from 2000 to 2016. Our results show that the period of fast agricultural growth in LAC, driven by technical change and resource reallocation, transformed agriculture in the region leaving it in a better position to cope with the more unfavorable regional macroeconomic environment and the less dynamic global markets observed after 2011.

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Monetary Policy Report - April 2022. Banco de la República, juin 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2022.

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Macroeconomic summary Annual inflation continued to rise in the first quarter (8.5%) and again outpaced both market expectations and the technical staff’s projections. Inflation in major consumer price index (CPI) baskets has accelerated year-to-date, rising in March at an annual rate above 3%. Food prices (25.4%) continued to contribute most to rising inflation, mainly affected by a deterioration in external supply and rising costs of agricultural inputs. Increases in transportation prices and in some utility rates (energy and gas) can explain the acceleration in regulated items prices (8.3%). For its part, the increase in inflation excluding food and regulated items (4.5%) would be the result of shocks in supply and external costs that have been more persistent than expected, the effects of indexation, accumulated inflationary pressures from the exchange rate, and a faster-than-anticipated tightening of excess productive capacity. Within the basket excluding food and regulated items, external inflationary pressures have meaningfully impacted on goods prices (6.4%), which have been accelerating since the last quarter of 2021. Annual growth in services prices (3.8%) above the target rate is due primarily to food away from home (14.1%), which was affected by significant increases in food and utilities prices and by a rise in the legal monthly minimum wage. Housing rentals and other services prices also increased, though at rates below 3%. Forecast and expected inflation have increased and remain above the target rate, partly due to external pressures (prices and costs) that have been more persistent than projected in the January report (Graphs 1.1 and 1.2). Russia’s invasion of Ukraine accentuated inflationary pressures, particularly on international prices for certain agricultural goods and inputs, energy, and oil. The current inflation projection assumes international food prices will increase through the middle of this year, then remain high and relatively stable for the remainder of 2022. Recovery in the perishable food supply is forecast to be less dynamic than previously anticipated due to high agricultural input prices. Oil prices should begin to recede starting in the second half of the year, but from higher levels than those presented in the previous report. Given the above, higher forecast inflation could accentuate indexation effects and increase inflation expectations. The reversion of a rebate on value-added tax (VAT) applied to cleaning and hygiene products, alongside the end of Colombia’s COVID-19 health emergency, could increase the prices of those goods. The elimination of excess productive capacity on the forecast horizon, with an output gap close to zero and somewhat higher than projected in January, is another factor to consider. As a consequence, annual inflation is expected to remain at high levels through June. Inflation should then decline, though at a slower pace than projected in the previous report. The adjustment process of the monetary policy rate wouldcontribute to pushing inflation and its expectations toward the target on the forecast horizon. Year-end inflation for 2022 is expected to be around 7.1%, declining to 4.8% in 2023. Economic activity again outperformed expectations. The technical staff’s growth forecast for 2022 has been revised upward from 4.3% to 5% (Graph 1.3). Output increased more than expected in annual terms in the fourth quarter of 2021 (10.7%), driven by domestic demand that came primarily because of private consumption above pre-pandemic levels. Investment also registered a significant recovery without returning to 2019 levels and with mixed performance by component. The trade deficit increased, with significant growth in imports similar to that for exports. The economic tracking indicator (ISE) for January and February suggested that firstquarter output would be higher than previously expected and that the positive demand shock observed at the end of 2021 could be fading slower than anticipated. Imports in consumer goods, retail sales figures, real restaurant and hotel income, and credit card purchases suggest that household spending continues to be dynamic, with levels similar to those registered at the end of 2021. Project launch and housing starts figures and capital goods import data suggest that investment also continues to recover but would remain below pre-pandemic levels. Consumption growth is expected to decelerate over the year from high levels reached over the last two quarters. This would come amid tighter domestic and external financial conditions, the exhaustion of suppressed demand, and a deterioration of available household income due to increased inflation. Investment is expected to continue to recover, while the trade deficit should tighten alongside high oil and other export commodity prices. Given all of the above, first-quarter economic growth is now expected to be 7.2% (previously 5.2%) and 5.0% for 2022 as a whole (previously 4.3%). Output growth would continue to moderate in 2023 (2.9%, previously 3.1%), converging similar to long-term rates. The technical staff’s revised projections suggest that the output gap would remain at levels close to zero on the forecast horizon but be tighter than forecast in January (Graph 1.4). These estimates continue to be affected by significant uncertainty associated with geopolitical tensions, external financial conditions, Colombia’s electoral cycle, and the COVID-19 pandemic. External demand is now projected to grow at a slower pace than previously expected amid increased global inflationary pressures, high oil prices, and tighter international financial conditions than forecast in January. The Russian invasion of Ukraine and its inflationary effects on prices for oil and certain agricultural goods and inputs accentuated existing global inflationary pressures originating in supply restrictions and increased international costs. A decline in the supply of Russian oil, low inventory levels, and continued production limits on behalf of the Organization of Petroleum Exporting Countries and its allies (OPEC+) can explain increased projected oil prices for 2022 (USD 100.8/barrel, previously USD 75.3) and 2023 (USD 86.8/barrel, previously USD 71.2). The forecast trajectory for the U.S. Federal Reserve (Fed) interest rate has increased for this and next year to reflect higher real and expected inflation and positive performance in the labormarket and economic activity. The normalization of monetary policy in various developed and emerging market economies, more persistent supply and cost shocks, and outbreaks of COVID-19 in some Asian countries contributed to a reduction in the average growth outlook for Colombia’s trade partners for 2022 (2.8%, previously 3.3%) and 2023 (2.4%, previously 2.6%). In this context, the projected path for Colombia’s risk premium increased, partly due to increased geopolitical global tensions, less expansionary monetary policy in the United States, an increase in perceived risk for emerging markets, and domestic factors such as accumulated macroeconomic imbalances and political uncertainty. Given all the above, external financial conditions are tighter than projected in January report. External forecasts and their impact on Colombia’s macroeconomic scenario continue to be affected by considerable uncertainty, given the unpredictability of both the conflict between Russia and Ukraine and the pandemic. The current macroeconomic scenario, characterized by high real inflation levels, forecast and expected inflation above 3%, and an output gap close to zero, suggests an increased risk of inflation expectations becoming unanchored. This scenario offers very limited space for expansionary monetary policy. Domestic demand has been more dynamic than projected in the January report and excess productive capacity would have tightened more quickly than anticipated. Headline and core inflation rose above expectations, reflecting more persistent and important external shocks on supply and costs. The Russian invasion of Ukraine accentuated supply restrictions and pressures on international costs. This partly explains the increase in the inflation forecast trajectory to levels above the target in the next two years. Inflation expectations increased again and are above 3%. All of this increased the risk of inflation expectations becoming unanchored and could generate indexation effects that move inflation still further from the target rate. This macroeconomic context also implies reduced space for expansionary monetary policy. 1.2 Monetary policy decision Banco de la República’s board of directors (BDBR) continues to adjust its monetary policy. In its meetings both in March and April of 2022, it decided by majority to increase the monetary policy rate by 100 basis points, bringing it to 6.0% (Graph 1.5).

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