Explaining commodity prices by a cointegrated time series-cross section model

Ahumada, Hildegart; Cornejo, Magdalena

doi:10.1007/s00181-014-0827-5

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…Against this background, the problem of endogeneity is briefly addressed here. Due to the use of appropriate methods, such as vector autoregression (VAR) and the Granger causality test recent studies (e.g., Ahumada and Cornejo 2014;Chai et al 2011;Klotz et al 2014;Papież et al 2014), one can distinguish between causes and effects. This is not the case in older studies, where it remains unclear whether a changing driver leads to the adaption of the price of a finite resource, or rather whether a changing price entails the putative driver's adaption.…”

Section: Discussionmentioning

confidence: 99%

“…However, changes in demand for finite resources are not acknowledged in the Hotelling approach, even though the recent huge increase in worldwide demand for finite resources, and in particular for new resources, has become ever more significant (Ahumada and Cornejo (2014). One reason for the supply side being largely ignored may be due to the fact that the raw materials inventory y -and thereby the quantities of raw materials available on the market and demanded -are not afforded any significance in the Hotelling model when calculating the optimal price path (see above).…”

Section: Change In Demandmentioning

confidence: 98%

“…Ahumada and Cornejo (2014) focus on eight raw materials (including aluminum, copper, gold and petroleum) and find significant price dependencies. Using the example of oil, Chai et al (2011) and Kaufmann et al (2004) also detect this tendency.…”

Section: Combined Examination Of Technical Progress and Degradationmentioning

confidence: 99%

“…Studying copper prices in the years 1994-2003, Cerda (2007 derives similar results and proves that increases in demand, particularly from Asia, lead to increases in raw material prices. In their empirical study, focusing on the years 1960-2010, Ahumada and Cornejo (2014) investigated the evolution of the price of eight commodities. They come to the conclusion that the demand-pull of China's economy has a positive impact on the development of commodities.…”

Section: Change In Demandmentioning

confidence: 99%

See 3 more Smart Citations

What drives resource prices? A qualitative review with recommendations for further development of the Hotelling model

Gaugler

2015

Miner Econ

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This paper examines which factors have an impact on the price of finite resources. Firstly, empirical and theoretical approaches used to study the development of resource prices are considered. These drivers are then studied, individually as well as altogether, starting with the Hotelling model. Implications for the future price of finite resources are subsequently reviewed, and theoretical hypotheses derived. These are then tested by means of a qualitative review based on empirical findings. This paper demonstrates how current approaches have limited applicability in determining which drivers impact the price of finite resources. One of the main reasons is identified as the Hotelling's model's lack of accounting for growth in demand for finite resources.

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Section: Discussionmentioning

confidence: 99%

Section: Change In Demandmentioning

confidence: 98%

Section: Combined Examination Of Technical Progress and Degradationmentioning

confidence: 99%

Section: Change In Demandmentioning

confidence: 99%

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What drives resource prices? A qualitative review with recommendations for further development of the Hotelling model

Gaugler

2015

Miner Econ

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“…">We also evaluate the forecast performance of what we call the fundamentals' model,

{\overset{S}{true}}_{t + h / t} = {boldX}_{t} \hat{β},

where

X_{t}

is a matrix that denotes the supply and demand determinants as well as monetary and financial variables (but excluding futures prices) that may affect the future spot price. We consider the same information set as in Ahumada and Cornejo (), but for time‐series in the case here analyzed. …”

Section: Forecast Model Specificationsmentioning

confidence: 99%

Out-of-sample testing price discovery in commodity markets: the case of soybeans

Ahumada

Cornejo

2016

Agricultural Economics

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Price discovery, a central function of futures markets, has been usually tested in-sample by studying the common stochastic trend between spot and futures prices. Instead, to evaluate futures as anticipatory prices, we develop a forecast approach to out-of-sample test price discovery in a multivariate framework. We apply it to the soybeans market. Results indicate futures prices as the best available "predictors" of future spot prices, although this finding holds only on average and for certain periods, other models show forecasting gains.JEL classifications: C53, Q11

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Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach

Cuaresma

Hlouskova

Obersteiner

2021

Journal of Forecasting

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We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem of model uncertainty is assessed explicitly by concentrating on specification selection based on the quality of short-term outof-sample forecasts (1 to 12 months ahead) for the price of wheat, soybeans and corn. Univariate and multivariate autoregressive models (autoregressive [AR], vector autoregressive [VAR] and vector error correction [VEC] specifications, estimated using frequentist and Bayesian methods), specifications with heteroskedastic errors (AR conditional heteroskedastic [ARCH] and generalized AR conditional heteroskedastic [GARCH] models) and combinations of these are entertained, including information about market fundamentals, macroeconomic and financial developments, and climatic variables. In addition, we assess potential non-linearities in the commodity price dynamics along the business cycle. Our results indicate that variables measuring market fundamentals and macroeconomic developments (and, to a lesser extent, financial developments) contain systematic predictive information for out-of-sample forecasting of commodity prices and that agricultural commodity prices react robustly to shocks in international competitiveness, as measured by changes in the real exchange rate.

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Explaining commodity prices by a cointegrated time series-cross section model

Cited by 18 publications

References 17 publications

What drives resource prices? A qualitative review with recommendations for further development of the Hotelling model

What drives resource prices? A qualitative review with recommendations for further development of the Hotelling model

Out-of-sample testing price discovery in commodity markets: the case of soybeans

Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach

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