سرریز نوسان قیمت در بازار دام و طیور ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه تبریز

چکیده

در این مقاله سعی شده است که چرخش رژیم و سرریز تلاطم قیمت در سطوح عمودی بازار دام و طیور کشور با کاربرد مدل چندمتغیره‌ی مارکف سوئیچینگ GARCH الگوسازی شود. برای این منظور از سری‌های قیمت ماهانه‌ی دان مرغ، مرغ زنده، گوشت مرغ (سطوح عمودی بازار مرغ)، علوفه، گوسفند زنده، گوشت گوسفند (سطوح عمودی بازار گوسفند)، علوفه، گوساله‌ی زنده و گوشت گوساله (سطوح عمودی بازار گوساله) در دوره زمانی 92-1376 استفاده شد. نتایج بدست آمده مبین وجود دو رژیم تلاطمی در سطوح عمودی هر سه بازار مرغ، گوسفند و گوساله و چرخش‌های پی در پی رژیم تلاطم قیمت بویژه بازار گوشت قرمز کشور می‌باشند که ریسک و عدم حتمیت زیادی را به بازار دام و طیور کشور تحمیل می‌نماید. در سطوح عمودی هر سه بازار هر چند دوره‌ی دوام رژیم پرتلاطم کمتر از رژیم کم‌تلاطم می‌باشد ولی تعداد و طول دوره‌های زمانی همراه با تلاطم بالای قیمت بسیار زیاد می‌باشد. بر اساس نتایج سرریز تلاطم قیمت، سرریزهای مختلف شوک‌ها و نیز تلاطم قیمت‌ها بین سطوح مختلف هر سه بازار و در هر دو رژیم تلاطم قیمت رخ داده است؛ هرچند سرریزها در رژیم پرتلاطم در هر سه بازار شدیدتر و یا بیشتر از رژیم کم‌تلاطم بوده است. بر این اساس، می‌توان بیان نمود که سطوح عمودی بازار دام و طیور کشور ارتباطات قیمتی متقابل و قوی باهم دارند که بایستی در سیاستگذاری‌های مربوط به بخش کاملاً مدنظر قرار گیرد. از سوی دیگر، به علت چرخش‌های پی در پی رژیم تلاطم قیمت بویژه در بازار گوشت قرمز، عدم حتمیت و به تبع آن، عدم قابلیت پیش‌بینی شرایط آینده، ویژگی بارز این بازارها می‌باشد. این ویژگی شرایط نامطمئنی را برای سرمایه‌گذاران و تولیدکنندگان در این بخش بوجود آورده و از سوی دیگر رفاه مصرف‌کنندگان نیز پی در پی دچار تغییر می‌شود. لذا باید برای تشویق سرمایه‌گذاری تلاش‌هایی در جهت توسعه‌ی ابزارهای مدیریت ریسک از قبیل بیمه و نیز کاهش تلاطم قیمت و چرخش پی در پی آن در بازار دام و طیور کشور صورت گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Price Volatility Spillover Effect in Iran's Poultry and Livestock Market

نویسندگان [English]

  • Mohammad Ghahremanzadeh
  • Ghahder dashti
  • Zahra Rasouli Birami
University of Tabriz
چکیده [English]

Introduction: The relationship between different market levels is an essential issue in economy. Understanding of linkages between different market levels will help to assess the potential impact of agricultural policies. Given the importance of the vertical market relationship, the present study examines price volatility spillover in vertical market levels of Iranian livestock and poultry market prices. For this, we use monthly returns series of broiler feed, chicken, broiler (as substitutes for broiler vertical market levels), hay, sheep, mutton (as substitutes for mutton vertical market levels), hay, calf and beef (as substitutes for beef vertical market levels) during the period of April 1997 to March 2014. Another important aspect that is considered in this study is the volatility regime switching. Many variables undergo events that seem their time series’ behavior has changed quite dramatically. Such structural breaks are mainly observed in the economic and financial time series data. The regime switching must not be totally considered as a predictable and deterministic event. As if the process has changed in the past, it could obviously change in the future too. Therefore, it should be considered as a random variable and hence a full-time series model will include probabilistic inference about switching from one regime to another regime. Hence, by doing a present study, we will be able to answer the questions such as: whether a significant regime change happened in livestock and poultry vertical market levels? Is there any significant volatility spillover in vertical market levels? Is there any difference between the volatility spillovers in high and low volatility regimes? To what extent the price volatility spills over the vertical markets?
Materials and Methods: A multivariate Markov switching model, that is best for our study aims, has been introduced by Haas and Mittnik (2008) to the volatility spillover literature which is a generalization of Haas et al. (2004) univariate model. In fact, it is a multi-regime version of Bollerslev et al. (1988) VECH model. Regime depended on variance matrices are defined by equation below:

Aij, i = 0, …, q, and Bij, i = 1, …, p, are parameter matrices. Index j is determining the volatility regime. This model does not directly estimable and to ensure positive definite covariance matrices some limitation should be imposed. For this purpose, Haas and Mittnik (2008) used Engle and Kroner (1995) proposed approach as:

Where are lower triangular matrices and Aij* and Bij* are M  M parameter matrices that must be estimated (Hess and Mittnik, 2008). The Aij* elements are the coefficients that explain the effect of volatility shocks or news and the Bij* elements are the coefficients that explain the effect of past volatilities on the current volatility of prices.
Results and Discussion: The results indicate the existence of two volatility regimes in vertical market levels of all three studied markets and also successive switches of volatility regimes, especially for meat (mutton and beef) market. According to evidence obtained in this study, although the durability of low volatility regime is lower than the high volatility regime in all cases and unconditional probability of staying in the high volatility regime is lower, but still the number of months and the length of periods in the high volatility regime are not in acceptable ranges. Based on the results, different shock and volatility spillovers between the different levels of the three markets have been occurring in both regimes; although the spillovers in high volatility regimes were more severe.
Conclusion: Price spikes like those we have witnessed for Iranian poultry and livestock products in recent years are not just part of a trend of higher prices. They are also part of a different phenomenon, price volatility, as its presence can be proved from our findings of broiler feed, chicken, broiler, hay, calf, sheep, beef and mutton – a combination of the abnormal unpredictability of prices and of unusually large variations, particularly upward. Although our findings differ in the magnitude of price volatility in each studied market, we agree that livestock market is more volatile than the poultry market and that volatility will persist in the coming years as past. While higher food prices can be an opportunity for farmers, price volatility hurts both consumers and producers. This extreme range of price volatilities hurts net food consumers and makes their welfare to change time to time. Moreover, the unpredictability of prices inhibits planning, makes investment risky and discourages farmers from producing more for the market. This represents a lost opportunity for farmers to raise their incomes, and for the country to develop the potential of programs to contribute to food security.

کلیدواژه‌ها [English]

  • Markov Switching
  • Smoothed probabilities
  • Transition probabilities
  • Volatility Spillover
1- Abounoori E., and Mojaverian M. 2002. Analysis of low of one price for Iranian crops market. Business Bulletin. 25, 85-126. (In Persian)
2- Alizadeh AH., Nomikos NK., and Pouliasis PK. 2008. A Markov regime switching approach for hedging energy commodities. Journal of Banking and Finance 32(9), 1970-1983.
3- Apergis N., and Rezitis A. 2003. Agricultural price volatility spillover effects: the case of Greece. European Review of Agricultural Economics, 30(3), 389–406.
4- Billio M., and Caporin M. 2005. Multivariate Markov switching dynamic conditional correlation GARCH representation for contagion analysis, Statistical Methods and Applications, 14(2), 145-161.
5- Bollerslev T., Engle RF., and Wooldridge JM. 1988. A capital asset pricing model with time varying covariances. Journal of Political Economy 96: 116–131.
6- Brummer B., Korn O., Schlubler K., Jaghdani T.J., and Saucedo A. 2013. Volatility in the after crisis period: A literature review of recent empirical research. Working Paper, No.1.
7- Chen R. 2009. Regime switching in volatilities and correlation between stock and bond markets. Discussion paper, 640. Financial Markets Group, London School of Economics and Political Science, London, UK.
8- Engle R., and Kroner FK. 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11: 122–150.
9- Ghahremanzadeh M., Eshtiagi M., and Pishbahar E. 2013. Price volatility in agricultural commodity market, Case Study: Mutton market in East Azerbaijan province, Journal of Agricultural Economics, forthcoming. (In Persian)
10- Ghahremanzadeh M., and Falsafian A. 2012. Volatility spillover effects in the Iran’s beef market. Journal of Agricultural Economics and Development, 1(26): 31-40. (In Persian)
11- Girapumthong N., Vansickle J. J., and Renwick A. 2003. A price asymmetry in the United States fresh tomato market. Journal of Food Distribution Research, 34: 51-59.
12- Gray S. 1996. “Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process”, Journal o f Financial Economics, 42, 27-62.
13- Haas M., and Mittnik S. 2007. Multivariate Regime Switching GARCH with an Application to International Stock Markets, Working paper.
14- Haas M., Mittnik S., and Paolella M. S. 2004. A new approach to Markov-switching GARCH models, Journal of Financial Econometrics, 2(4), 493-530.
15- Haihong Z. 2005. Regime switching in international securitized property markets. PhD thesis, Department of Real Estate School of Design and Environment National University of Singapore.
16- Hamilton J. D., and Susmel R. 1994. “Autoregressive Conditional Heteroskedasticity and Changes in Regime, Journal of Econometrics, 64, 307-333.
17- Hamilton J. D. 1994. Time Series Analysis. Princeton, N J: Princeton University Press.
18- Hansen P.R., and Lund A. 2001. A comparison of volatility models: Does anything beat a GARCH (1,1)? Working Paper Series, No.48.
19- Iran’s Livestock support firm. Available at http://http://www.iranslal.com/index php/fa/amar/price-‌dame-toyor/d/mounts (visited 2014) (In Persian)
20- Keshavarz Haddad G., Ebrahimi B., and Ja'far Abdi A. 2011. Evaluation of volatility spillover between cement industry stock returns and its related industries in Iran. Journal of Financial Research, 12(30), 50- 60.
21- Keshavarzian M., Zamani M., and Panahinezhad H. 2010. Spillover effect of American dollar exchange rate on crude oil. Energy Economics Studies, year 7 (27) 131-154.
22- 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.
23- Kim C.J. 1994. “Dynamic Linear Models with Markov Switching”, Journal o f Econometrics, 64, 1-22.
24- Klaassen F. 2002. “Improving GARCH Volatility Forecasts with Regime-Switching GARCH”, Empirical Economics, 27, 363-394.
25- Lee H. T., and Yoder J. K. 2007a. A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratio, Applied Economics.
26- Mogaddasi R., Khalig Khiavi P., Yousefi H., and Eskandarpoor B. 2012. Volatility spillover effects of Iranian agricultural products prices (Case Study: poultry market). 8th conference of Iranian Agricultural Economics, University of Shiraz.
27- Nomikos N., and Salvador E. 2011. The role of volatility regimes on volatility transmission patterns. Electronic copy available at: http://ssrn.com/abstract=1854403.
28- Pelletier D. 2006. “Regime Switching for Dynamic Correlations”, Journal of Econometrics, 131, 445-473.
29- Rapsomanikis G., and Mugera H. 2011. Price transmission and volatility spillovers in food markets of developing countries. I. Piot-Lepetit, R. M’Barek(eds), Methodes to Analyse Agricultural Commodity Price Volatility, Springer Science+Business Media, LLC 2011.
30- Rezitis A., and Stavropoulos K. S. 2011. Price volatility and rational expectations in a sectoral framework commodity model: a multivariate GARCH approach. Agricultural Economics, 42(3), 419–435.
31- Uchezuba D. 2010. Measuring asymmetric price and volatility spilover in the South African poultry market. Dissertation. Faculty of natural and agricultural sciences department of agricultural economics university of free state Bloemfontein.
32- Udoh E., and Egwaikhide F. O. 2012. Does International Oil Price Volatility Complement Domestic Food Price Instability in Nigeria? An Empirical Enquiry. International Journal of Economics and Finance, 4(1), 235–246.
33- Ward R. W. 1982. Asymmetry in retail, wholesale and shipping point pricing of fresh vegetables. American Journal of Agricultural Economics, 64: 205-212.
34- Zhang Y. J., Fan Y., Tsai H. T., and Wei Y. M. 2008. Spillover effect of US dollar exchange rate on oil prices. Journal of Policy Modeling, 30: 973-985.