News and volatility of food prices

Zheng, Yuqing; Kinnucan, Henry W.; Thompson, Henry

doi:10.1080/00036840600892910

Cited by 34 publications

(25 citation statements)

References 30 publications

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“…It seemed that EGARCH model is the best prediction model for all commodities, except CPO. The predictability of EGARCH Model in predicting the returns volatility of Indonesia exported agricultural commodities is supported by the studies of Swaray (2002) and Zheng et al (2008). TGARCH model is found as the best model in predicting the returns volatility of CPO.…”

Section: Table 10 the Results Of Garch Construction Process For Blacmentioning

confidence: 63%

“…Many researchers have employed and extended the ARCH/GARCH methodology to examine various commodities price volatility issues. For example, Alom et al (2010);Sumaryanto (2009);O"Connor et al (2009);Zheng et al (2008); Rezitis (2003, 2011);Yang et al (2001);and Beck (2001), theyapplied GARCHtype models to analyze the price volatility of agricultural products. Some empirical studies reported the existence of price volatility in futures prices and spot prices of some commodities.…”

Section: Introductionmentioning

confidence: 99%

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The Predictability of GARCH-Type Models on the Returns Volatility of Primary Indonesian Exported Agricultural Commodities

Hatane¹

2012

jak

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Agricultural sector plays an important role in Indonesia"s economy; especially for the plantation sub-sector contributing high revenues to Indonesia"s exporting sectors. The primary agricultural commodities in Indonesian export discussed in this study would be Crude Palm Oil (CPO), Natural Rubber TSR20, Arabica Coffee, Robusta Coffee, Cocoa, White Pepper and Black Pepper. Meanwhile, the returns volatility nature of agricultural commodity is famous. The volatility refers to heteroscedasticity nature of the returns which can be modeled by GARCH-type models. The returns volatility can be describe by the residual of the mean equation and volatility of error variances in the previous periods. The aims of this study are to examine the predictability of GARCH-type models on the returns volatility of those seven agricultural commodities and to determine the best GARCH-type models for each commodity based on the traditional symmetric evaluation statistics. The results find that the predictability of ARCH, GARCH, GARCH-M, EGACRH and TGARCH, as type of GARCH models used in this study, are different for each commodity.

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Section: Table 10 the Results Of Garch Construction Process For Blacmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

The Predictability of GARCH-Type Models on the Returns Volatility of Primary Indonesian Exported Agricultural Commodities

Hatane¹

2012

jak

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“…For example, Pindyck(2004) pointed out that changes in commodity prices can influence the total cost of production as well as the opportunity cost of producing commodities currently rather than later. It has also been argued that price volatility reduces welfare and competition by increasing consumer search costs (Zheng, Kinnucan, & Thompson, 2008). In the same line, Apergis and Rezitis(2003b) noted down that price volatility leads both producers and consumers to uncertainty and risk and thus volatility of commodity prices has been studied to some extent.…”

Section: Introductionmentioning

confidence: 91%

“…They used univariate EGARCH models to check volatility spillover and provided evidence of volatility spillovers in agricultural markets. Zheng et al (2008) studied time varying volatility of US food consumer prices using Exponential GARCH models and news impact curves.…”

Section: Introductionmentioning

confidence: 99%

Modelling Food Price Volatility and Testing Heat Waves and/or Meteor Showers Effects: Evidence for Asia and Pacific

Alom¹,

Ward²,

Hu³

2012

ASS

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This paper assesses the volatility and cross country mean and volatility spillover effects of food prices within and across global and selected Asian and Pacific countries namely Australia, New Zealand, South Korea, Singapore, Hong Kong, Taiwan, India and Thailand. The principal method of analysis comprises the development of a set of component GARCH-type models of conditional variance. Volatility characteristics and spillover effects of food prices are examined across a full (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) and two subsamples (1995-2001 and 2002-2010) with daily food price indices. Main findings of the study are as follows: (1) like other asset prices, food price volatility can be modelled by CGARCH variant of GARCH-family models for world as well as country specific levels, (2) increased risk does not necessarily lead to increased returns for world and specified countries except few instances, (3) mixed evidence of cross country mean and volatility spillover effects are reported. No exact direction of spillover effects from exporter to importer or importer to exporter countries can be drawn rather mixed evidence of spillover from exporter to importer, exporter to exporter, importer to exporter and geographical proximity can be documented. The 'meteor shower' hypothesis that the conditional variance of the change in one market depends on the past information of other markets dominates 'heat wave' hypothesis that the conditional variance depends on the past information of that market while for shorter time period 'heat wave' effects dominate 'meteor shower' effects.

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“…This category is represented by the papers of Chavas and Mehta (2004), Zheng et al (2008), Mehta and Chavas (2008), Serra (2011 and Rezitis (2012). This category is also dominated by studies for the US and Greek markets, but shows some variations in the products studied.…”

Section: Price Volatility Transmission In Food Supply Chainsmentioning

confidence: 99%

The transmission and management of price volatility in food supply chains

Assefa¹

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The 2006-2011 period has been marked by increased volatility in food an agricultural commodity prices at a global level. In the EU, the continuous liberalization of agricultural markets under the Common Agricultural Policy has led to the exposure of EU agricultural to increasing market price volatility. This thesis has investigated the transmission and management of price volatility in EU food supply chains. The transmission of price volatility in various food supply chains is first investigated through a literature review followed by an empirical analysis of price volatility transmission in the case of the German fresh pork supply chain. The effect of market power was also taken into account in the latter empirical analysis. Next, the management of price volatility was investigated through interviews conducted with actors of selected EU food supply chains. This was followed by the analysis of the effectiveness of selected price volatility management strategies. Lastly, in light of the policy support for agricultural insurance within the Common agricultural policy, premium rates of an agricultural revenue insurance contract were calculated for the Dutch ware potato sector.One of the gaps identified in the reviewed literature is the lack of attention given to the effects of contextual factors on price volatility transmissions in food supply chains. Contextual factors include market power in the chain and pricing strategies (e.g. contracts) by chain actors. Results of the price volatility transmission analysis conducted in this thesis in the case of the German pork chain show that retail market power limited both the transmission of price levels and price volatility. This thesis shows that price volatility is perceived as risky by all actors in the food supply chain. Deviations of prices by more than 10 to 15 % from expected levels were perceived as price volatility by a majority of the chain actors. Results further show that price volatility management strategies in EU food chains are diverse and well beyond traditional instruments such as futures and forward contracts. Contrary to expectations, price fixing contracts were not found to be desirable by interviewed chain actors. This thesis also found that the effectiveness of contracts in reducing price volatility depended on how the contract price was set.Results of this thesis further show that premium rates of a revenue insurance contract for the Dutch ware potato sector across categories of farms. The average premium rates calculated were 32.1%, 22.2%, 33.1% and 24.0% on guaranteed revenue per hectare for the high expected yield, low expected yield, high yield variance to expected yield ratio and low yield variance to expected yield ratio categories of farm, respectively. The difference in premium rates across categories of farms implies that charging the same average premium rate to all Dutch ware potato farms can lead to adverse selection.

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News and volatility of food prices

Cited by 34 publications

References 30 publications

The Predictability of GARCH-Type Models on the Returns Volatility of Primary Indonesian Exported Agricultural Commodities

The Predictability of GARCH-Type Models on the Returns Volatility of Primary Indonesian Exported Agricultural Commodities

Modelling Food Price Volatility and Testing Heat Waves and/or Meteor Showers Effects: Evidence for Asia and Pacific

The transmission and management of price volatility in food supply chains

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