Price dynamics in agricultural commodity markets: a comparison of European and US markets

Statnik, Jean-Christophe; Verstraete, David

doi:10.1007/s00181-014-0816-8

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…In the Indian agricultural scenario, the GARCH model has been used in several research papers across the literature [12][13][14][15]. Statnik and Verstraete [16] showed that the price dynamics of red winter wheat, corn, and soybeans traded on the Chicago Board of Trade (CBOT) follow the GARCH model.…”

Section: Introductionmentioning

confidence: 99%

Modeling Asymmetric Volatility: A News Impact Curve Approach

et al. 2023

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Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.

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Section: Introductionmentioning

confidence: 99%

Modeling Asymmetric Volatility: A News Impact Curve Approach

et al. 2023

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Prediction of soybean price in China using QR-RBF neural network model

Zhang

Zang

et al. 2018

Computers and Electronics in Agriculture

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Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic

Staugaitis

Vaznonis

2022

Agriculture

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Motivated by increased agricultural commodity price volatility and surges during the past decade, we investigated whether financial speculation is to blame. The aim of this paper is to build on prior research about to what extent and in which ways financial speculation undermines agricultural commodity prices. In our analysis, we utilized the daily returns on milling wheat, corn, and soybean futures from the Euronext Commodities Paris market (MATIF) as well as the short-term speculation index. To quantify this impact, we apply Granger noncausality tests as well as the GARCH (generalized autoregressive conditional heteroskedasticity) technique. We also propose a model using seasonal dummy variables to examine whether financial speculation has a greater impact on price volatility during more volatile months. According to our results, financial speculation, as an external factor, in most cases has no effect or reduces the volatility of the underlying futures prices. The opposite is observed in the corn market, where volatility has risen in the post-2020 period and has been pushed up even more by speculation in April. However, since the influence on other commodities is limited or nonexistent, more emphasis should be focused on speculation in the European corn futures market or its interdependence with energy markets.

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Price dynamics in agricultural commodity markets: a comparison of European and US markets

Cited by 3 publications

References 25 publications

Modeling Asymmetric Volatility: A News Impact Curve Approach

Modeling Asymmetric Volatility: A News Impact Curve Approach

Prediction of soybean price in China using QR-RBF neural network model

Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic

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