بررسی همبستگی قیمت نفت، نرخ ارز و قیمت نهاده‌های وارداتی صنعت طیور در ایران: بکارگیری رهیافت مفاصل تاکی شکل

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه تبریز

2 تبریز

چکیده

صنعت طیور یکی از حیاتی‌ترین بخش‌های کشاورزی است که در زمینه تولید گوشت و تأمین پروتئین نقش اساسی دارد. به دلیل وجود رقابت بالا در مصرف آب بین غذای انسان و طیور، زیربخش طیور اغلب نهاده‌های خود را از طریق واردات تأمین می‌کند. از آنجاکه هرگونه نوسان و شوک در بازارهای بین‌المللی به دلیلی گسترش ارتباطات بازارهای داخلی را تحت تأثیر قرار می‌دهد، بروز هرگونه شوک در قیمت نهاده‌ها در بازار‌های جهانی بازار داخلی را تحت تأثیر قرار خواهد داد. این وضعیت بعد از شروع بحران قیمت نفت از سال 1384، بیشتر از قبل قیمت‌ها را تحت تأثیر قرار داده است که به نظر می‌رسد در ایران هم صنعت طیور به واسطه‌ای وارداتی بودن حجم بالای اغلب نهاده‌ها متأثر بوده است. با توجه به مسئله گفته شده هدف این مطالعه بررسی همبستگی بین قیمت نفت، نرخ ارز و قیمت نهاده‌های وارداتی صنعت طیور در دو بازه زمانی داده‌های ماهانه‌ی سا‌ل‌های83-1374 (قبل بحران) و 93-1384 (بعد بحران) می‌باشد. این هدف با استفاده از رهیافت واین کاپیولا بر اساس ARMA-MGARCH بررسی شده است. نتایج حاصل از این مطالعه نشان داده نهاده‌های ذرت، سویا در دوره بعد بحران نسبت به دوره قبل بحران همبستگی مثبت و بالایی با قیمت نفت و همبستگی منفی با نرخ ارز از خود نشان‌ داده‌ است. بنابراین می‌توان گفت قیمت نهاده‌های صنعت طیور با شروع شوک‌های قیمت نفت از سال 1384 به دلیل شروع بحران‌های جهانی مانند جنگ عراق و آمریکا، بحران مالی جهانی و افزایش جهانی قیمت نهاده‌های کشاورزی، بیشتر تحت تأثیر تحولات جهانی قرار گرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

An Analysis Correlation between Oil Prices, Exchange Rate and Imported Inputs of Poultry Industry in Iran: Using Vine-Copula Approach

نویسندگان [English]

  • E. Pishabar 1
  • P. Pakrooh 2
  • Mohammad Ghahremanzadeh 1
1 University of Tabriz
2 University of Tabriz
چکیده [English]

Introduction: Thepoultry industry, as sub-sectors of the agricultural sector,isone of the economic activities as considered risky and play a significant role in the public life of our community. The poultry industryin Iran has a bilateral relationship with global markets because on the one hand is exporters of agriculture production and on the other hand is a major importer of inputs such a cornandsoybean. So in terms of high transactions volume, poultry industriesare influenced by international prices and volatilities. Crude oil is one of the most important commodities in the global economyand in Iran has a comparative advantage that is seen as a strategic resource. A significant portion of Iran's revenue is from oil exports account and crude oil price. Therefore, oil prices in the world is an important factor that affecting the ability of our import volume. Recent observations show that the volatility and uncertainty in oil prices are transmitted through exchange rate (USD America) to real economic markets, other markets, the exchanges and the domestic agricultural and food products markets. This articleseems clearly impressive after Iraq-USA war in 2002 and the Global Financial Crisis in 2005. So in this paper, we try to analysis correlation between oil prices, exchange rates and the price of poultry inputs for the two periods, before the Global Financial Crisis and Iraq-USA war (1995-2004) and after that (2005-2014).
Material and Method: Theperiods ofstudy are pre and after the Iraq-USA war and the Global Financial Crisis. Our monthly data collected from the Central Bank of Iran, Animal Support Company since 1995 to 2014. For the purpose of this paper, we used Vine Copula-MGARCH approaches. Before everything at first, we controlled the stationary and seasonal unitroots behavior in data with ADF,KPSS and HEGY stationary and seasonal tests.After that for analysis the correlation of prices, we used MGARCH models for modeling volatilities and collecting the residual of equations. Because of the limitation in linear correlation coefficients and the advantages of copulas for modeling and analysis correlation, we used copula approaches for this sector. At first, we modeledvolatilities with kind of MGARCH models such as CCC and DCC GARCHes and after that for collection pure residuals we must eliminate the past effect of each variable or in other means we can tell, using ARMA with MGARCHmodel can give us residuals that have not any effect of past behaviors in variables.
Results and Discussion: The results of the ADF unit-root test has indicatedthat all variables are not stationary and accumulated from the first stages. Similarly, the KPSS unit root test has shownsuch as ADF test results. Based on these tests our variables are not stationary and in two periods of study and the first stage of a difference,they are accumulated. Seasonal unit root test or "HEGY test" results also showed there arenot seasonal behaviors in two periods of variables. After these tests for modeling volatilities at first, we needed to detect ARCH behaviors in variables. Because of that, we controlled ARCH effect behaviors in variables and for this aim, we use an ARCH-LM test. Detecting ARCH behaviors help us to use the kind of MGARCH models for modeling volatilities. Our results indicate that CCC and DCC models with ARMA model have flexibility for modeling. So after that examination, we have collected the residuals of equations and collected the residuals of each equationin ARIMA-CCC-MGARCH model. We calculated the correlation of oil price, exchange rate and input prices with kind of Vine-Copulas. Results of R-Vine, C-Vine and D-Vine models indicated that the correlation between oil price and exchange rate are different in two periods, as the positive correlation of oil and exchange rate, in the first period, change to a negative correlation in the second periods. Correlation of oil price and input pricesin second time are more than beforethe crisis. Clarke and Voung tests for choosing Vine models indicate that R-Vine models for after and before period are the best.
Conclusions: Based on R-Vine models our results indicated that correlation between oil price and input prices are more than before the crisis and this is not a suitable situation for Iran's industries. At last, we offer that, using oil incomes forincreased infrastructures of input productions it may be better than importing inputs.

کلیدواژه‌ها [English]

  • Crisis
  • corn
  • Price Volatilities
  • Soybean
  • War
1- Abbott P., Hurt C., and Tyner W. 2008. What’s Driving Food Prices? Farm Foundation. Issue Report July 2008.
2- Anderse T.G., Davis R.A., Kreiss J.P., and Mikosch T. 2007. Multivariate GARCH models. Working Paper Series in Economics and Finance 669: 1-25.
3- Beaulieu J.J., and Miron J.A. 1993. Seasonal unit roots in aggregate US data. Journal of Econometrics, 55: 305-328.
4- Enders W. 1995. Applied Econometric Time Series. Iowa State University.
5- Gozgor G., and Kablamaci B. 2014. The linkage between oil and agricultural commodity prices in the light of the perceived global risk. MPRA paper 58659: 332-342.
6- Harri R., Nalley L., and Hudson D. 2009. The Relationship between Oil, Exchange Rates, and Commodity Prices. Journal of Agricultural and Applied Economics, 41(2):501–510.
7- Hanson K., Robinson S., and Schluter G. 1993. Sectoral Effects of a World Oil Price Shock: Economy wide Linkages to the Agricultural Sector. Journal of Agricultural and Resource Economics. 18(1): 96–116.
8- Hosseini A. 2016. Monthly poultry and livestock Journal. 47: 11-49.
9- Infrastructure studies office of Parliament. 2009. The livestock and poultry industry. 250:1-25.
10- Javadi A., and Ghahremanzadeh M. 2016. Analysis the fluctuation of poultry inputs industry market with DCC-MGARCH model. Tenth Biennial Conference of Agricultural Economics Iran. May 2015. University of Kerman.
11- Kamalzadeh A., and Shabani A. 2007. Maintenance and growth requirements for energy and nitrogen of Baluchi sheep. International Journal of Agriculture and Biology, 9(3): 523-529.
12- Kamalzadeh A., Rajabbaigy M., Moslehi H., and Torkashvand R. 2009. Poultry Production Systems in Iran. In Book of Proceedings, 2nd Mediterranean Summit of WPSA. 4:183-188.
13- Kamalabadi H., and Shahnoshi N. 2012. Price Transmition of imported inputs poultry sector from global markets to domestic markets case study of Soybean and Fish powder. Agricultural economic and development. 79.
14- Kiatmanaroch T., and Sriboonchitta S. 2014. Relationship between exchange rates, palm oil prices and crude oil prices: A Vine Copula based GARCH approach. Modeling Dependence in Econometrics: 399-413.
15- Khoung ND, Aloui R., and Aissa B.M. 2013. Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach. Journal of International Money and Finance )32(: 719-738.
16- Lim C., and Mcaleer M. 2000. A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32: 499-509.
17- Nazlioglu S., and Soytas U. 2011. World oil Price and Agriculture Commodity Price: Evidence from an emerging market. Energy Economics 33: 448-496.
18- Puarattanaarunkorn O., and Sriboonchitta S. 2014. Copula based GARCH dependence model of Chinese and Korean Tourist Arrivals to Thailand: Implications for risk Management. Modeling Dependence in Econometrics: 343-365.
19- Sensoy A., Turhan I.M., and Hacihasanoglu E. 2014. A Comparative Analysis of the dynamic relationship between oil price and exchange rates, Journal of International Financial Markets, Institution and Money, 32: 397-414.
20- Schnept R. 2008. High Agricultural Commodity Prices: What Are the Issues?. Congressional Research Service May 2008.
21- Sriboonchitta S., and Boonyanuphong P.H. 2014. An Analysis of Interdependence among Energy, Biofuel and Agricultural Markets Using Vine Copula Model. Modeling Dependence in Econometrics: 415-429.
22- Sklar A. 1973. Random Variables, Joint Distribution Functions, and copulas. Kybernetika 9: 449-460.
23- Shams S., and Zareshenas M. 2014. Copula Approach for modeling oil and gold price and exchange rate co-movement in Iran. International Journal of Statistics and Applications, 4(3): 172-175.
24- Shavalpour S., Jabbarzadeh A., and Khanjarpanah H. 2015. Modeling the Spillover of Oil Shocks on Crops Market: The Case of Soybean and Wheat. Growth and Development of Rural & Agricultural Economics. 1(2): 41-56.
25- Trostle R. 2008. Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices. Economic Research Service. United States Department of Agriculture.
26- Ziovet E. 2006. Unit-root and Stationary Tests. Unit root Lecture, Washington.
27- Wu Ch., Chung H., and Chang Y. 2012. The economic value of co-movement between oil price and exchange rate using Copula- based GARCH models. Energy Economics 34: 270-282.