بررسی ریسک قراردادهای پسته فندقی در بازار بورس کالای ایران

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

• سهراب صدفی‎آبکنار ¹
• امیرحسین چیذری ^{2، 1}
• حامد رفیعی ¹
• حبیب الله سلامی ¹

¹ گروه اقتصاد کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

² گروه اقتصاد کاربردی، دانشگاه جان هاپگین، بال‎تیمور، ماری‎لند، ایالات متحده امریکا

10.22067/jead.2024.89445.1289

چکیده

این مطالعه بر روی پوشش ریسک پسته فندقی با استفاده از قراردادهای آتی و سپرده سرمایه‌گذاری تمرکز دارد و تغییرات ریسک و انتخاب پرتفوی بهینه را از 27 مهرماه 1397 تا 28 دی‎ماه 1400 بررسی می‎کند. با استفاده از نظریه سبد کالایی مارکویتز، این مطالعه از مدل‎های اقتصادسنجی مختلفی از جمله تحلیل رگرسیون، مدل‌های GARCH و آزمون ریشه واحد فصلی برای تعیین سبد کالایی بهینه جهت پوشش ریسک استفاده می‎کند. نتایج کلیدی شامل نوسانات فصلی معنادار در فرکانس‎های مختلف است که نشان‎دهنده الگوهای فصلی قوی و پایدار است. تحلیل رگرسیون روابط معناداری بین بازده‎های گذشته و کنونی را نشان می‎دهد که تأثیر بازده‎های گذشته بر عملکرد کنونی را برجسته می‎کند. مدل‌های GARCH ضرایب مثبت معناداری برای اثرات شوک و وقفه‎های واریانس نشان می‎دهند که به معنای تأثیرات قابل توجه شوک‎ها و اثرات خودرگرسیونی قوی نوسانات گذشته بر نوسانات کنونی است. نسبت بهینه قراردادهای سپرده کالایی پسته فندقی بر اساس فصل و روز متغیر است که تقاضای بازار و ترجیحات سرمایه‎گذاران را منعکس می‎کند. این مطالعه چندین پیشنهاد سیاستی ارائه می‎دهد که عبارتند از؛ تقویت و مدیریت نوسانات فصلی از طریق مشتقات مالی، حمایت از ابزارهای پوشش ریسک، توجه به الگوهای فصلی و روزانه در ترکیب سبد کالایی، پایش و کنترل نوسانات در دوره‎های حساس و توسعه سیاست‎های حمایتی در ماه‎های کم نوسان. این پیشنهادها هدف دارند عملکرد بازار پسته فندقی را بهبود بخشیده و کارایی تصمیم‎گیری‎های سرمایه‎گذاری را افزایش دهند.

کلیدواژه‌ها

• رفتار فصلی داده‌ها
• سبد بهینه کالایی
• نسبت بهینه پوشش ریسک

موضوعات

• بازاریابی و تجارت محصولات کشاورزی

عنوان مقاله [English]

Risk Analysis of Round Fandoghi Pistachio Contracts in the Iran Mercantile Exchange Market

نویسندگان [English]

• S. Sadafi Abkenar ¹
• A.H. Chizari ^{2 1}
• H. Rafiee ¹
• H. Salami ¹

¹ Department of Agricultural Economics, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

² Johns Hopkins University, Institute for Applied Economics, Baltimore, Maryland, United States

چکیده [English]

Iran Mercantile Exchange is striving to become a regional hub for price discovery of essential commodities and raw materials, providing producers with financial instruments and risk management tools. This study investigates the optimal hedge ratio in future and commodity deposit receipts (spot) contracts for Round Fandoghi pistachios. Using the BEKK-VAR-TARCH model, the impact of seasonal and daily volatility on returns and hedge ratios was assessed over the period from 19 October 2018 to 18 January 2022. The results showed that volatility on specific days of the week and during different seasons affect speculative and investment decisions in the commodity exchange. Particularly, sharp volatility during certain periods can lead to significant changes in returns and hedge ratios. These findings suggest that investors should update their investment strategies based on seasonal and daily volatilities. Additionally, the importance of utilizing financial instruments suited to market conditions for managing existing risks was confirmed. Ultimately, investors, speculators, and policymakers in the commodity exchange are advised to pay special attention to temporal changes and existing volatilities when composing their investment portfolios and adjusting hedge strategies. Furthermore, the use of futures contracts and derivative instruments is recommended as risk management approaches. This study contributes to a better understanding of volatility behavior and offers strategies for improved risk management in the Round Fandoghi pistachio market.

کلیدواژه‌ها [English]

• Optimal commodity portfolio
• Optimal hedge ratio
• Seasonal data behavior

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0). 

مراجع

1. Ahangari, S., Mojaverian, S.M., & Hosseini Yekani, S.A. (2017). Prioritization of the factors affecting the success of a commodity in agricultural ring of Iran mercantile exchange. Journal of Agricultural Economics and Development, 31(1), 27–35. https://doi.org/10.22067/JEAD2.V31I1.56950
2. Alausa, W.B. (2014). Three essays on the application of the markov switching multifractal model [Doctor of philosophy, department of economics university of Alberta]. In ERA. https://doi.org/10.7939/R39G5GN72
3. Ameur, H. Ben, Ftiti, Z., & Louhichi, W. (2022). Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework. Annals of Operations Research, 313(1), 171–189. https://doi.org/10.1007/s10479-021-04172-3
4. Amjadi, A., Hosseini Yekani, S.A., & Ahmadi Kaliji, S. (2017). The role of agricultural commodity exchange on hedging (Case study: Selected agricultural product). Journal of Agricultural Economics and Development, 25(98), 1–17. https://sid.ir/paper/24596/en
5. Asai, M. (2015). Bayesian Analysis of General Asymmetric Multivariate GARCH Models and News Impact Curves. Journal of the Japan Statiatical Society, 45(2), 129–144. https://doi.org/10.14490/jjss.45.129
6. Baba, Y., Engle, R.F., Kraft, D.F., & Kroner, K.F. (1990). Multivariate simultaneous generalized arch, department of economics, university of California at San Diego.
7. Baghestani, A.A., Yazdani, S., & Ahmadian, M. (2016). The application of GMDH neural network approach in forecasting the price of soybean meal in Iran mercantile exchange. Journal of Financial Economics (Financial Economics and Development), 9(33), 9(33), 1–13. https://sid.ir/paper/ 229315/en
8. Borzabadi Farahani, M., Gholizadeh, M., & Chirani, E. (2021). Dynamic modeling of estimating the optimal hedge ratio of gold coin with saffron futures contracts. Journal of Securities Exchange, 14(55), 5–37. https://doi.org/10.22034/jse.2020.11238.1450
9. Castro, T. del B., Osborn, D.R., & Taylor, A.M.R. (2012). On augmented Hegy tests for seasonal unit roots. Econometric Theory, 28(5), 1121–1143. https://doi.org/10.1017/S0266466612000060
10. Chance, D.M. (1989). An introduction to options and futures. In The Dryden Press, a division of Holt, Rinehart, and Winston, Inc. https://archive.org/details/isbn_0030284392
11. Chen, S.-S., Lee, C., & Shrestha, K. (2003). Futures hedge ratios: a review. The Quarterly Review of Economics and Finance, 43(3), 433–465. https://doi.org/10.1016/S1062-9769(02)00191-6
12. Choudhry, T. (2009). Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets. International Review of Financial Analysis, 18(1–2), 58–65. https://doi.org/10.1016/j.irfa.2008.11.003
13. Edwards, F.R., & Ma, C.W. (1992). Futures and Options. McGraw-Hill. https://api. semanticscholar.org/CorpusID:152550058
14. Enders, W. (2014). Applied Econometric Time Series. In Wiley. https://bcs.wiley.com/he-bcs/Books?action=index&itemId=1118808568&bcsId=9288
15. Engle, R.F., & Kroner, K.F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150. https://doi.org/10.1017/S0266466600009063
16. Geman, H. (2005). Commodities and commodity derivatives modeling and pricing for agricultural, metals and energy. In John Wiley & Sons, Ltd (1st ed.). wiley.com/en-us/Commodities+and+Commodity+Derivatives%3A+Modeling+and+Pricing+for+Agriculturals%2C+Metals+and+Energy-p-9780470012185
17. Ghazali, A., Nasrabadi, M.B., & Nasrabadi, H.B. (2016). Identification of factors affecting lack of boom in the agricultural ring in Iran mercantile exchange. Strategic Management Thought (Management Thought), 10(1(19)), 181–214. https://doi.org/10.30497/SMT.2016.1872
18. Haj Seyed Javadi, S.M.R., & Heydari, R. (2022). Designing the most suitable hybrid model for forecasting the future price of saffron in the agricultural commodity exchange. Iranian Journal of Agricultural Economics and Development Research, 53(4), 1023–1041. https://doi.org/10.22059 /IJAEDR.2022.336850.669122
19. Han, L., Jin, J., Wu, L., & Zeng, H. (2020). The volatility linkage between energy and agricultural futures markets with external shocks. International Review of Financial Analysis, 68. https://doi.org/10.1016/j.irfa.2019.01.011
20. Hylleberg, S., Engle, R.F., Granger, C.W.J., & Yoo, B.S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. https://doi.org/10.1016/0304-4076(90)90080-D
21. Iran Mercantile Exchange. (2024. August). https://en.ime.co.ir
22. Johnson, L.L. (1960). The theory of hedging and speculation in commodity futures. The Review of Economic Studies, 27(3), 139–151. https://doi.org/10.2307/2296076
23. Kashiri Kolaei, F., & Hosseni Yekani, S.A. (2016). Investigating the effects of commodity exchange on competition of pistachios suppliers in Iran. Iranian Journal of Agricultural Economics and Development Research, 472(3), 581–587. https://sid.ir/paper/146267/en
24. Kavoosi Kalashami, M., & Kavoosi Kalashami, M. (2017). Price relationships and spillover effects of price volatilities in Iran’s rice market. International Journal of Agricultural Management and Development, 7(4), 429–438. com/p1773999
25. Li, M.-Y.L. (2012). Modeling the natural gas spot-futures markets as a regime switching vector error correction model. Energy Sources, Part B: Economics, Planning, and Policy, 7(3), 301–313. https://doi.org/10.1080/15567240903117609
26. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77. https://doi.org/10.2307/2975974
27. Miremadi, S.A., Chizari, A., Yazdani, S., Rafiee, H., & Mohtashami, T. (2021). Investigating the efficiency of Negin saffron futures contract in Iran Mercantile Exchange. Iranian Journal of Agricultural Economics and Development Research, 52(4), 851–862. https://doi.org/10.22059/ IJAEDR.2021.317027.669001
28. Mohammadi, M., Mohammadi, H., & Fakari, B. (2016). The impact of macroeconomic variables on the volatility of agricultural prices in the Iran mercantile exchange (the case of barley). Journal of Agricultural Economics and Development, 24(95), 1–23. https://www.sid.ir/paper/24562/fa
29. Moumouni, Z. (2016). Modeling and Hedging Strategies for Agricultural Commodities [Université Montpellier]. https://doi.org/tel-01563222
30. Myers, R.J., & Thompson, S.R. (1989). Generalized optimal hedge ratio estimation. American Journal of Agricultural Economics, 71(4), 858–868. https://doi.org/10.2307/1242663
31. Pakdaman, N., Javanshah, A., & Nadi, M. (2023). Investigating the effect of climate change on pistachio yield. Research in Horticultural Sciences, 2(2), 177–190. https://doi.org/10.22092 /rhsj.2023.360550.1034
32. Pishbahar, E., Baghestani, M., & Dashti, Gh. (2018). Application of binomial tree in determining the price of an Asian option and calculation of risk-sensitive parameters (Case study: Soybean meal and corn). Journal of Agricultural Economics and Development, 32(1), 1–16. https://doi.org/ 10.22067/JEAD2.V32I1.62744
33. Rezaei, H., Zare, M., & Falah Qalhari, G.A. (2021). The effect of climatic parameters on pistachio phenology according to day degree in Gonabad city. Natural Geography, 52(13), 1–14. https://journals.iau.ir/article_682962_c2c2cd866f23fce77964a291b304880b.pdf
34. Rezitis, A.N., Andrikopoulos, P., & Daglis, T. (2024). Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes. Journal of Futures Markets, 44(3), 451–483. https://doi.org/10.1002/fut.22477
35. Schneider, L., & Tavin, B. (2024). Seasonal volatility in agricultural markets: modeling and empirical investigations. Annals of Operations Research, 334(1–3), 7–58. https://doi.org/ 10.1007/s10479-021-04241-7
36. Shen, F. (2020). Spillover Effects from Swap Dealers and Index Traders on Agricultural Commodity Futures, a BEKK-MGARCH Approach [Master of Science, Texas A&M University]. https://oaktrust.library.tamu.edu/handle/1969.1/192881
37. Siegel, D.R., & Siegel, D.F. (1990). The futures markets: Arbitrage, risk management, and portfolio strategies. Probus Pub Co. https://archive.org/details/futuresmarketsar0000sieg/ page/n3/mode/2up
38. Sims, C.A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–76. https://doi.org/10. 2307/1912017
39. Singhal, S., & Biswal, P.C. (2021). Dynamic commodity portfolio management: A regime-switching VAR model. Global Business Review, 22(2), 532–549. https://doi.org/10.1177/ 0972150918811705
40. Tajabadipour, A., & Afarasteh, M. (2022). Preparation of pistachio orchards to reduce spring frost damage. Pistachio World, 7(78), 51–53.
41. Witt, H.J., Schroeder, T.C., & Hayenga, M.L. (1987). Comparison of analytical approaches for estimating hedge ratios for agricultural commodities. Journal of Futures Markets, 7(2), 135–146. https://doi.org/10.1002/fut.3990070204
42. Yahya, M., Oglend, A., & Dahl, R.E. (2019). Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach. Energy Economics, 80, 277–296. https://doi.org/10.1016/j.eneco.2019.01.011
43. Zhang, Z., & Chuan, L.I. (2006). Country-specific factors and the pattern of intra-industry trade in China’s manufacturing. Journal of International Development, 18(8), 1137–1149. https://doi.org/ 10.1002/jid.1288
44. Zhao, L., Meng, Y., Zhang, Y., & Li, Y. (2019). The optimal hedge strategy of crude oil spot and futures markets: Evidence from a novel method. International Journal of Finance & Economics, 24(1), 186–203. https://doi.org/10.1002/ijfe.1656
45. Zhao, R., Diao, G., & Chen, S. (2019). Study on the price fluctuation and dynamic relationship between log and sawn timber. Forest Products Journal, 69(1), 34–41. https://doi.org/10.13073/ FPJ-D-17-00048