پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,230 |
| تعداد مقالات | 67,759 |
| تعداد مشاهده مقاله | 115,136,483 |
| تعداد دریافت فایل اصل مقاله | 89,899,889 |
مدیریت ریسک قیمت محصول خرما با استفاده از بازار آتی (کاربرد مدل گارچ دو متغیره) | ||
| تحقیقات اقتصاد و توسعه کشاورزی ایران | ||
| مقاله 3، دوره 45، شماره 4، دی 1393، صفحه 601-611 اصل مقاله (832.71 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijaedr.2014.53835 | ||
| نویسندگان | ||
| حبیبه شرافتمند* 1؛ سعید یزدانی2؛ رضا مقدسی3 | ||
| 1دانشجوی دکتری اقتصاد کشاورزی دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران | ||
| 2استاد گروه اقتصاد کشاورزی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| 3دانشیار گروه اقتصاد کشاورزی دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران | ||
| چکیده | ||
| کشاورزی فعالیتی است که مخاطرات زیادی دارد. در این فعالیت، انواع ریسک‌های طبیعی، اجتماعی و اقتصادی دست‌به‌دست هم می‌دهند و مجموعة شکننده و آسیب‌پذیری برای تولیدکنندگان فراهم می‌کنند. در این میان، ریسک قیمتی در محصولات کشاورزی، مشکلات مالی فراوانی برای تولیدکنندگان ایجاد کرده است، این مطالعه بر امکان استفاده از بازار آتی به‌عنوان ابزار مدیریتی ریسک قیمتی محصول خرما تمرکز کرد؛ بنابراین ابتدا با استفاده از قیمت‌های ماهانة خرما و به‌کارگیری چارچوب تئوری میانگین واریانس، نسبت تأمین با روشOLS تعیین شد. سپس به‌دلیل وجود واریانس‌های شرطی خودهمبسته، نسبت تأمین متغیر طی زمان با استفاده از مدل گارچ دومتغیره برآورد شد. ماتریس واریانس کوواریانس شرطی متغیر طی زمان براساس مدل‌های چندمتغیرة ناهمسان واریانسBekk (1/1) تخمین زده شد. سپس با استفاده از نتایج این ماتریس‌ها، نسبت تأمین متغیر طی زمان برآورد شد. پیش‌بینی قیمت آتی خرما با استفاده از الگوی شبکة عصبی و الگوی گارچ است. نسبت تأمین استخراجی از روش گارچ دومتغیره به‌طور متوسط برابر 7/0 برآورد شد و بیانگر این است که حدود 70 درصد از ریسک قیمتی محصول خرما می‌تواند با فروش در بازار آتی کاهش یابد. | ||
| کلیدواژه‌ها | ||
| بازار آتی؛ خرما؛ ریسک قیمت؛ مدل گارچ | ||
| عنوان مقاله [English] | ||
| Dates price risk management using futures markets tools (Bivariate GARCH model) | ||
| نویسندگان [English] | ||
| Habibe Sherafatmand1؛ Saied Yazdani2؛ Reza Moghadasi3 | ||
| 1PhD. Candidate and Associate Professor, Agricultural Economics, Azad University, Branch of Sciences and Research, University of Tehran, Iran | ||
| 2Professor, Agricultural Economics, University of Tehran, Iran | ||
| 3Professor, Agricultural Economics, University of Tehran, Iran | ||
| چکیده [English] | ||
| Agricultural activities are risky activities. In these activities, various natural, social and economic risks have created fragile and vulnerable situation for producers. Price risk in agricultural products has caused financial problems for many producers and farmers. To deal with these price risks and price fluctuations, there are varieties of tools. This paper focused on futures markets instruments as risk management tools for date's price risks management. This study used monthly futures prices of dates and used mean-variance framework to determine the optimal hedge ratio with OLS method. Due to the conditional variances autocorrelated in residuals in regression models, time varying hedge ratios with Bivariate GARCH models were determined. Bivariate GARCH model was used in this study to determine hedge ratios. Therefore, first, time varying conditional variance covariance matrix with using multivariate models based on heterogeneous variance BEKK (1, 1) was estimated. Then, using the results of this matrix, the optimal time varing hedge ratios was calculated. Dates future price series were predicted using artificial neural networks pattern and the GARCH model. The results of hedge performance showed that time varying hedge ratios were eliminated more price risk than the OLS method. The results showed that the average hedge ratios of Bivariate GARCH model is 0.7, meaning that about 70% of the date's price risk could be reduced with sales in the futures market. | ||
| کلیدواژه‌ها [English] | ||
| BGARCH models and dates, future market, price risk | ||
| مراجع | ||
|
Abdolahi Ezatabadi, M., Najafi, B. (2003).survey the possibilities of using futures and options markets in reducing price volatility in agricultural products of Iran.case of study: pistachio. Seasonal journal of agricultural economic and development. 41-42. 1-25.(in farsi).
Abdolahi Ezatabadi, M., Najafi, B. (2006). Estimation of hedge ratios in futures and option markets for Iran agricultural products and identify the effecting factors on it .case of study: pistachio. Seasonal journal of Science and Technology of Agriculture and Natural Resources. 2. 1-15.(in farsi).
Ai D. (2012). Hedging effectiveness of constant and time varying hedge ratios using futures contracts: the case of Ontario and Alberta Feedlot industries. A thesis presented to the University of Guelph.
Baillie, R. T., Myers, R. J. (1991). Bivariate Garch estimation of the optimal commodity futures hedge. Journal of Applied Econometrics, 6, 109–124.
Bera, A., Garcia, P., & Roh, J. (1997). Estimation of Time-Varying Hedging Ratios for Corns and Soybeans: BGARCH and Random Coefficient Approaches. Sankhya, 59, 346-368.
Brooks, C., Henry O., & Persand, G. (2002). The effects of asymmetries on optimal hedge ratios. Journal of business. 75. 333-352.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31,307-327.
Cecchetti, S. G., Cumby, R. E., &Figlewski, S. (1988). Estimation of the optimal futures hedge. Review of Economics and Statistics, 70, 623–630.
Choudhry, T. (2009). Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets. International Review of Financial Analysis .18. 58–65
Collins, R. A. (2000). The Risk Management Effectiveness of Multivariate Hedging Models in the Soy Complex. Journal of Futures Markets, 20, 189-204.
Ederington, L. H. (1979). The hedging performance of the new futures markets. Journal of Finance, 34, 157–170.
Engle, R.F. (1982). “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50,987-1007.
Gagnon, L., Lypny G. (1995). “Hedging Short-term Interest Risk under Time-Varying Distributions,” Journal of Futures Markets, 15,767-783.
Garcia, P., Roh, J., & Leuthold, R. M. (1995). Simultaneously Determined, Time- Varying Hedge Ratios in the Soybean Complex. Applied Economics, 27, 1127-1134.
Heydari, H., Mola Bahrami A.(2010). Stock portfolio optimization based on multivariate Garch models: Evidence from Tehran Stock Exchange. Journal of financial research. 30. 35-56. (in farsi).
Hosseini Yekani, A., Zibaei, M.(2010).Determination of different goods for traded in the futures market, Case Study :Agricultural Products .Journal of economic and agricultural development.3. 268-278. ( in farsi).
Jalali Naeini, A.H., Kazemi Manesh, M.,(2004). Study on the change of optimum hedge ratio in the oil market .Seasonal journal of energy economic research. 1. 3-27. ( in farsi).
Junkus, J. C., Lee, C. F. (1985). Use of three index futures in hedging decisions. Journal of Futures Markets, 5, 201–222.
Kroner, K. F., Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28, 535–551.
Lien, D., Tse, Y. K., & Tsui, A. K. (2002). Evaluating the Hedging Performance of Constant-Correlation GARCH model. Applied Financial Economics, 12, 791-198. Malliaris, A. G., Urrutia, J. L. (1991). The impact of the lengths of estimation periods and hedging horizons on the effectiveness of a hedge: Evidence from foreign currency futures. Journal of Futures Markets, 3, 271–289.
Miffre , J. (2004). Conditional OLS minimum variance hedge ratios. Journal of futures markets. 24. 945-964.
Myers, R.J. (1991). “Estimating Time-Varying Optimal Hedge Ratios on Futures Markets,” Journal of Futures Markets, 11, 39-53.
Myers, R. J., Thompson, S. R. (1989). Generalized optimal hedge ratio estimation. American Journal of Agricultural Economics, 71, 858–868.
Park, T.H., Switzer, L.N. (1995). “Bivariate GARCH Estimation of the Optimal Hedge Ratios for Stock Index Futures: A Note,” Journal of Futures Markets, 15, 61-67.
Pendar M., Shakeri A., and Salami H. (2011). Price risk management of soybean oil Grain imports by futures market instruments. Iranian journal of agricultural development and economic. 4. 279-492. (in farsi).
Tong, W.H.S. (1996). “An Examination of Dynamic Hedging,” Journal of International Money and Finance, 15, 19-35.
Sephton, P. S. (1993a). Hedging wheat and canola at the Winnipeg commodity exchange. Applied Financial Economics, 3, 67–72.
Sephton, P. S. (1993b). Optimal hedge ratios at the Winnipeg commodity exchange. Canadian Journal of Economics, 26, 175–193.
Waldemar A., Caldarelli, C.E., Rocha, C.M., & Martines-Filho, J. G.(2010). Dynamic hedging effectiveness for soybean farmers in Rondonópolis (MT) with futures contracts of BM&F. Organizações Rurais & Agroindustriais, Lavras, v. 12, n. 1, p. 34-45.
Yazdani, S., Kiyani Rad, A.(2004). Revenue insurance, a new paradigm in risk management in agricultural products .Seasonal journal of agricultural economic and development. 47. 47-79. (in farsi). | ||
|
آمار تعداد مشاهده مقاله: 2,207 تعداد دریافت فایل اصل مقاله: 1,031 |
||