Investigation of Long-Term Memory and Price Bubble in Red Meat and Chicken Meat (Case Study of West Azarbaijan Province)

Document Type : Research Paper

Authors

1 Master's Degree in Economics,Faculty of Economics and Management ,urmia university,urmia,iran.

2 Associate Professor of Economics,Faculty of Economics and Management ,urmia university,urmia,iran.

Abstract

As an essential and strategic product, meat has a special importance
in the household basket of every Iranian. The statistics of 2019 of
Iran Statistics Center show that in the West Azarbaijan province, the
expenses related to the purchase of meat (both white and red) include
21% of the total expenses related to the food basket of an urban
household. Fluctuations in the prices of agricultural and livestock
products have consequences for both the supply and demand sides.
Therefore, knowing the price behavior of these products is important,
and if this structure is accurately identified, it is possible to
prevent excessive market price fluctuations with right time
intervention. we try to investigate the presence
or absence of long-term memory in the price of the aforementioned meat
types and the presence or absence of a price bubble in them. For this
purpose, the autoregressive fractional integrated moving average model
(ARFIMA) and the generalized Dickey-Fuller supremum test were used.
The results of the present study indicate that the price of sheep and
calves meat in West Azarbaijan province has a degree of fractional
integration and possess long-term memory characteristics, so that when
a shock occurs, its effect remains for a long time. However, the
investigation of the price behavior of chicken meat indicates the
existence of long-term memory in some periods and the existence of a
unit root in some other periods. On the other hand, the investigation
of the price bubble in chicken meat revealed that the price of chicken
meat has had a bubble behavior for 4 periods and has moved away from
its long-term value due to high volatility and explosive behavior.
Also, the investigation of the price bubble in lamb and calves meat
revealed that these items also experienced price bubbles in some
periods.

Keywords

Main Subjects


Abbasi, G,. Moammadi, H,. Neshatavar, M,. (2018). Investigating the role of price bubble in creating fluctuations in Tehran Stock Exchange(selected companies of petrochemical and automobile industries). Financial Economics12(43), 133-152. (In Farsi)
Abbasinejad, H., & Gudarzi Farahani, Y. (2014). Estimating the Degree of Integration in CPI with ARFIMA-FIGARCH Model: Case study of Iran. Economics Research14(52), 26-1. (In Farsi)
Adämmer، P.،& Bohl، M. T. (2015). Speculative bubbles in agricultural prices. The Quarterly Review of Economics and Finance, 55, 67-76.
Alizadeh, S., & safarzadeh, H. (2019). A Survey of Long-Term Memory in the Digital Currency Index. Financial Engineering and Portfolio Management, 10(40), 169-183. (In Farsi)
Amiri, H., Salem, A., & Beshkhor, M. (2017). The Persistence of inflation in iran: a fractionally integrated approach. Economical Modeling11(39), 141-162. (In Farsi)
ansarinasab M, Manzari Tavakoli Z. (2020), Modeling Gasoline Consumption Behaviors in Iran Based on Long Memory and Regime Change. QEER. 16 (64) :125-149. (In Farsi)
Badri, A,. Abdolbagi, A,. (2017). An Introduction to Financial Econometrics, Data Analysis in Financial Sciences (Volume 1), Nass Publications. (In Farsi)
Baillie, R. T., Han, Y. W., Myers, R. J., & Song, J. (2007). Long memory models for daily and high frequency commodity futures returns. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 27(7), 643-668.
Borimnezhad V.  Shoushtarian A. (2008), Analyzing simultaneousness of supply and demand system equations of meats in iran, Agricultural Economics, 2(1): 67-86. (In Farsi)
Brooks، C.، Prokopczuk، M.،& Wu، Y. (2015). Booms and busts in commodity markets: bubbles or fundamentals? Journal of Futures Markets، 35(10),  916-938.
Chen, Z., Yan, B., & Kang, H. (2023). Price bubbles of agricultural commodities: Evidence from China’s futures market. Empirical Economics64(1), 195-222.
David, S. A., Machado, J. A., Trevisan, L. R., Inacio Jr, C., & Lopes, A. M. (2017). Dynamics of commodities prices: integer and fractional models. Fundamenta Informaticae, 151(1-4), 389-408.
Gil‐Alana, L. A., Cunado, J., & de Gracia, F. P. (2012). Persistence, long memory, and unit roots in commodity prices. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 60(4), 451-468.
Gilbert، C. L. (2010). How to understand high food prices. Journal of agricultural economics، 61(2)،  398-425.
Gutierrez، L. (2011). Looking for Rational Bubbles in Agricultural Commodity Markets (No. 726-2016-50062).
Jalali, O., & Hatefi Madjumerd, M. (2016). The Survey of Existence of Price Bubbles in Oil Market of Iran. Iranian Energy Economics, 5(20), 227-260. (In Farsi)
Khosdabakhsh, S,.  Zayandehrudi, M,. Jalaee Esfandabadi, S.A,. (2020). Health Bubbles Survey in Tehran Stock Exchange . Financial Economics14(50), 39-62. (In Farsi)
Liu، X.، Filler، G.،& Odening، M. (2013). Testing for speculative bubbles in agricultural commodity prices: a regime switching approach. Agricultural Finance Review.
Maddah, M.,  DOLFAN, N.,  SAMEI, N. (2018). Testing for Bubbles in the Import Market of Some Strategic Agricultural Commodities of Iran. Agricultural Economics Research, 10(39), 261-276. (In Farsi)
mahjoub, M., & Nabavi Chashmi, S. (2021). Existing bubble stock test with Generalized Supremum Augmented Dickey-Fuller techniques and Impulse Response Function and analysis of Variance Decomposition. Journal of Investment Knowledge, 10(38), 243-264. (In Farsi)
Mao, Q., Ren, Y., & Loy, J. P. (2021). Price bubbles in agricultural commodity markets and contributing factors: evidence for corn and soybeans in China. China Agricultural Economic Review13(1), 22-53.
Martins, L. F., & Rodrigues, P. M. (2014). Testing for persistence change in fractionally integrated models: An application to world inflation rates. Computational Statistics & Data Analysis, 76, 502-522.
Mitra, D., & Paul, R. K. (2021). Forecasting of Price of Rice in India Using Long-Memory Time-Series Model. National Academy Science Letters, 44(4), 289-293.
Mohammadi, M., Mohammadi, H., & Azami, H. (2016). Identifying Price Bubbles in Chicken and Beef Meat Markets with Rational Expectations. Journal Of Agricultural Economics and Development30(2), 88-96. (In Farsi)
Mohammadi, T., & Teleblou, R. (2010). Dynamics of Inflation and Inflation Uncertainty Using ARFIMA- GARCH Model. Economics Research10(36), 137-170. (In Farsi)
Nasrollahi, Z., Jalali, O., Hatefi Madjumerd, M. (2017). Multiple Bubbles of the Gold Market: Origination, Explosion and Complete Deletion. Journal of Econometric Modelling, 2(1), 81-111. (In Farsi)
Nikoomaram H.  Saeedi A.  Anbarestani M. (2011). Studying long memory of tehran stock exchange . Financial Engineering and Portfolio Management, 2(9), 47-63. (In Farsi)
Phillips P.C.B, Shi S-P Yu. J., (2012). “Testing for Multiple Bubbles, Cowles Foundation for Research in Economics”, Yale University, Paper No: 1843.
Phillips, P. C., Wu, Y., & Yu, J. (2011). Explosive behavior in the 1990s Nasdaq: When did exuberance escalate asset values?. International economic review, 52(1), 201-226.
Qu, Z. (2011). A test against spurious long memory. Journal of Business & Economic Statistics, 29(3), 423-438.
Rasekhi, S,. Shahrazi, M,. Elmi, Z,. (2016). Detecting the Price Bubbles Periods: A Case Study of Tehran Stock Exchange Market. Quarterly Journal of Quantitative Economics, 13(3), 25-55. (In Farsi)
Sadeghi Sharif, S. J., Osoolian, M., & Afsharian, A. (2017). Tests of Multiple Explosive Bubbles Behavior in Tehran Stock Exchange and Real State Market in Iran. Journal of Asset Management and Financing5(4), 129-142. (In Farsi)
safamanesh, H., Keshavarz Haddad, G., Piraee, K., & Zare, H. (2019). Estimation of quality elasticity for different types of meat in food basket of iranian households. Economics Research19(73), 47-74. (In Farsi)
shayan zeinvand, A. Kardgar. R,. Abotaleb, K (2015). A Study of the effects of asymmetry and long-run memory in volatility between the exchange rate and stock price returns in iran. Quarterly Journal of Quantitative Economics, 12(2), 23-55. (In Farsi)
Shokoohi, Z., & Tarazkar, M. (2022). Meat Price Bubble in Iran: An Empirical Evidence from State‐Space Model. Journal of Agricultural Economics and Development36(2), 157-167. (In Farsi)
Tahmasebi, A., Moghadasi, R. (2010). Factors Affecting the Chicken Meat Marketing Margin in Iran. Agricultural Economics and Development, 18(3), 163-178. (In Farsi)
Tehranchian, A., Balounejad Nouri, R. (2016). Examining the persistence of real exchange rate misalignment in iran. Quarterly Journal of Applied Theories of Economics, 2(4), 1-22. (In Farsi)
Tian, F., Yang, K., & Chen, L. (2017). Realized volatility forecasting of agricultural commodity futures using long memory and regime switching. Journal of Forecasting, 36(4), 421-430.
Trevisan, L. R., & David, S. A. (2016). Some Comments On Fractionally Integration Processes Involving Two Agricultural Commodities. European Scientific Journal.
Wenger, K., Leschinski, C., & Sibbertsen, P. (2018). A simple test on structural change in long-memory time series. Economics Letters, 163, 90-94.
zomorodian, G., & Mahboubi, B. (2022). Long memory in four main cryptocurrencies. Financial Knowledge of Securities Analysis, 15(53), 1-13. (In Farsi)