The Evaluation of Price Volatility of Beef and Chicken and Livestock’s Major Inputs in Iran

Document Type : Research Paper

Authors

1 Ph.D.student, Department of Agricultural Economics, Faculty of Agriculture, University of Payam- Noor East Tehran, Iran

2 Associate Professor, Department of Agricultural Economics, Faculty of Agriculture, University of Payam- Noor East Tehran, Iran

Abstract

Introduction: In recent years, price volatility of chicken and beef, along with volatility in livestock inputs have become one of the main problems of the livestock and poultry industries in Iran. Price volatility which in fact is the fluctuations of the price variances (logarithm of price changes), increasing production risk, investment insecurity, and predicting a decline in producer profits, also endangers consumer spending behaviors and food security. This is the fact especially in countries that do not have effective price policies. One of the important issues in the production of animal and poultry protein products is the supply of needed inputs, which imports a significant portion of the country's consumer demand for livestock and poultry inputs such as corn, barley and soybean meal. According to the Customs Statistics of the Islamic Republic of Iran in 2017, imports of corn, barley and soybean meal were 7283, 2563 and 1269 thousand tons, respectively. Therefore, due to the dependence of domestic production of these products on imported inputs, price fluctuations of these inputs can also lead to price fluctuations in protein products. Therefore, evaluating the volatility and price fluctuations of protein products and the inputs needed and solutions to counter the negative effects of these fluctuations is one of the policy makers' goals in this regard. The purpose of this study was to evaluate and analyze the price volatility of chicken and beef and their inputs including corn, barley and soybean meal. By identifying price volatility behavior, we can reduce this volatility by using appropriate policy tools.
Material and methods: For this purpose, we used ARCH family techniques (both the linear and non-linear models) and monthly time series data from 2002 to 2017. To this end, the ARCH test is first used to investigate the effects of ARCH (heterogeneity variance) on the series of returns on the prices of the goods under investigation, and if these effects are confirmed, these effects are linear or nonlinear with the Engel & Neg (1993) nonlinear test. Finally, linear and nonlinear GARCH models are used to analyze price volatility. For the studied series, nine patterns of GARCH, EGARCH, GJR-GARCH, TGARCH, SAGARCH, PGARCH, NGARCH, APGARCH and NPGARCH were estimated.
Results and discussion: Statistics analysis on the probability distribution characteristics of the price return series of show that the distribution normality for the series studied with the exception of corn and chicken has been rejected and only the two aforementioned series have normal distribution. Based on the results presented, all series except for chicken have positive Skewness . This indicates that the probability density on the left is greater for the distribution of maize, soybean meal, barley and beef. This means that the negative returns in these series are likely. The kurtosis, except for corn and chicken, which is approximately equal to the kurtosis value of the normal distribution, is greater in the other series than the normal elasticity. This means that the average monthly fluctuations in corn and chicken prices are less likely to occur than barley, soybean meal, and beef. Due to the time series nature of the data used in this study, the existence of a single root in the yield series of inputs and outputs was tested. The existence of a single root for the series of returns on the prices of the inputs and products under study is rejected, in other words, the series are at static or I (0) levels. Therefore, ARCH models can be used to model the price returns of these products and inputs. Findings show that non-linear ARCH effect confirmed for all commodities except corn. According to results, the EGARCH model is the best model for barley and soybean meal price series volatility modeling and SAGARCH model is the best model to survey volatility in price series of chicken and beef. Moreover, volatility in the prices of above mentioned goods has an asymmetric response to positive and negative shocks. The volatility in the prices of these commodities also has an asymmetric reaction to positive and negative price shocks, so that the effect of positive shocks on price volatility is greater than the effect of negative shocks. Estimated patterns show a high degree of shocks stability, low price adjustment rates, and price volatility of corn, soybean meal, and beef. However, due to the high rate of price adjustment in the barley and chicken market, the shocks are not sufficiently stable. Although various policies have been implemented by the government to manage the market of inputs and products under study, the price volatility in the market for these items is noticeable and significant, according to the results. In other words, the management of market volatility and price fluctuations was not desirable despite the use of various policy tools. Given that the effect of positive and negative shocks on price volatility of inputs and products excluding corn is asymmetric, therefore, it is recommended that policy makers plan the market for these commodities based on the type of shocks ( (Positive or negative) and their sustainability, and pave the way for reducing price volatility in the commodity market. Therefore, it is suggested that policies implemented to reduce the price volatility of these commodities be reviewed and alternative policy packages in this area be designed and implemented. Since these input as a major input in the broiler industry of the country shows high volatility and volatility in this market may be transmitted to the market of products of this industry, such as chicken and also a major part of imported inputs. Imports will have a significant impact on the price of these products, so it is recommended that in the short term a sufficient amount of this input be bought and stored so that both the input market and the poultry market are more stable and in the long run the government better With appropriate supportive policies to reduce inappropriate and unmanaged dependence on imports and production Move it.

Keywords


Abdelradi F, Serra T, 2015. Asymmetric price volatility transmission between food and energy markets: The case of Spain. Agricultural Economics 46: 503–513
Banerjee A V, Duflo E, 2007. The economic lives of the poor. Journal of Economic Perspectives 21 (1): 141-168.
Bergmann D O, Connor D and Thümmel A, 2016. An analysis of price and volatility transmission in butter, palm oil and crude oil markets. Agricultural and Food Economics 4: 23.
Bollerslev T, 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31: 307–327.
Bórawski P, Gotkiewicz W, Dunn J W, Alter T, 2015. The impact of price volatility of agricultural commodities in Poland on alternative incomes of conventional, ecological and agritourism farms. Athens Journal of Business and Economics, 1: 299–310.
Brummer B, Korn O, Schlubler K, Jaghdani TJ and Saucedo A, 2013. Volatility in the after crisis period: A literature review of recent empirical research. Working Paper, No.1.  
Cenrak Bank of the Islamic Republic of Iran, 2017. Household budget. Accessible at: https://www.cbi.ir/simplelist/1600.aspx
Dewi I. Nurmalina R, Kilat Adhi A, Brümmer B. 2017. Price volatility analysis in
Indonesian beef market. 2nd International conference on sustainable agriculture and food security: A comprehensive approach.
Duan J C, Gauthier G, and Simonato J G, 1999. An analytical approximation for the GARCH option pricing model, Journal of Computational Finance 2(4): 75-116.
Engle RF, 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50: 987–1007. 
Engle R, Ng V K 1993. Measuring and testing the impact of news on volatility, the Journal of Finance 48(5): 1749-1778.
FAO, 2011. Price volatility in food and agricultural markets: policy responses. Economic and social development department.
Franses PH. van Dijk D, 2003. Non-Linear series models in empirical finance. Cambridge university press: Cambridge.
Gilbert CL, Morgan CW, 2010. Food price volatility. Philosophical Transactions of the Royal Society B, 365: 3023–3034. 
Gilbert C L, Morgan C W, 2014. Review food price volatility. Phil. Trans. R. Soc. B 365, 3023–303.doi:10.1098/rstb.2010.0139.
Ghahremanzadeh M, Javdan E, 2013. Investigation the impact of news on meat price volatility in Iran. Agricultural Economics 6(4): 37-54.
Ghahremanzadeh M, Aref Eshgi T, 2013. Modeling asymmetric price volatility for Tehran province’s chicken market. Agricultural Economics & Development 27(2): 134-143.
Ghahremanzadeh M, Rasouli Beyrami Z and Dashti G, 2017. Price volatility regime switches in Iran’s meat market useing Markov Switching GARCH models, Agricultural Economics 11(1): 133-162.
Glosten LR, Jagannathan R, Runkle DE, 1993. On the relation between expected value
and the volatility of the nominal excess return on stocks. Journal of Finance 48: 1779–
1801.
Hdizadeh M, 2007. Investigation the price fluctuation of selective agricultural products. MSc thesis, University of Shiraz, Shiraz, Iran.
Hagerud G, 1997. A new non-linear GARCH model, Stockholm school of economics, EFI, The economic research institute.
Haile G M, Kalkuhl M and Von Braun J, 2013.  Short-term global crop acreage response to international food prices and implications of volatility. ZEF-Discussion Papers on Development Policy 175. Center for Development Research, Bonn.
Hiale M G, Kalkuhl M and Braun von J, 2014. Agricultural supply response to international food prices and price volatility: a crosscountry panel analysis. Agri-Food and Rural Innovations for Healthier Societies, EAAE 2014 Congress.
Huchet-Bourdon M, 2011. Agricultural commodity price volatility - Papers - OECD iLibrary. Paris.
Karali B, Power G, 2013. Short- and long-run determinants of commodity price volatility. American Journal of Agricultural Economics 95(3): 724–738.
Khaligh P, Moghaddasi R, Eskandarpur B and Mousavi N, 2012. Spillover effects of
agricultural products price volatilities in Iran (Case study: poultry market). Journal of
Basic and Applied Scientific Research 2(8): 7906–7914.
Kornher L, Kalkuhl M, 2013. Food price volatility in developing countries
and its determinants. Quarterly Journal of International Agriculture 52(4): 277-308.
Ministry of Agricuture-Jahad, 2017. Agricultural statistics (Volume II), Department for livestock production. Tehran, Iran.
Mousavi S H, Dehghani R and Alipour A, 2016. Investigation the impact of exchange rate volatility on food price index in Iran. The 10th biennial conference of Iran’s agricultural economics. Kerman, Iran.
Nelson DB, 1991. Conditional heteroscedasticity in asset returns: a new approach.
Econometrica 59: 349–370
Piot-Lepetit I, M’Barek R, 2011. Methods to analyses agricultural commodity price volatility, dio: 10.1007/978-1-4419-7634-5_2, C _ Springer Science+Business Media, LLC 2011.
Pishbahar E, Ferdosi R, and Asadollahpour F, 2015. Price transmission of chicken: usage of vector autoregressive Markov-Switching (MSVAR) approach. Agricultural Economics 9(2): 55-72.
Rasouli Birami Z, Ghahremanzadeh M, Dashti G, and Mohammad Rezaee R, 2015, Estimating price volatility structure in Iran’s meat market: application of general GARCH models. Agricultural Economics & Development, 30(1): 19-36.
Sidhoum A A, Serra T, 2016. Volatility spillovers in the Spanish food marketing
Chain: The case of tomato. Agribusiness 32(1): 45-63.
Śmiech S, Papież M, Dąbrowski M A, Fijorek K, 2018. What drives food price volatility? Evidence based on a generalized VAR approach applied to the food, financial and energy markets. Economics discussion Paper, No.  2018-55.
Tahami pour M, Arabmazar A, and Hamedinasab M, 2019. Modeling the fluctuations in prices for agricultural products in Iran: A case study of Cucumber, Tomato, Potato and Onion, Agricultural Economics and Development 27(106): 209-259.
Wodon Q C, Tsimpo P, Backiny-Yetna G, Joseph F A and Coulombe H, 2008. Potential impact of higher food prices on poverty, Policy research working paper No. 4745, Washington, DC, World Bank.
Zakoian JM, 1994. Threshold heteroscedastic models. Journal of Economic Dynamics
and Control 18: 931–995.
Zavvar N, 2012, Factors affecting price fluctuations in the Iranian livestock products, MSc thesis, University of Ferdowsi, Mashhad, Iran.