Price Volatility of Ornamental Plants in Batu Municipality

Hamidatul Khofifah, Tri Wahyu Nugroho, Sujarwo Sujarwo

Abstract


The imbalance between supply and demand of ornamental plants in the market cause fluctuations that lead to price volatility. This study aimed to analyze the price volatility of ornamental plants with high economic value, such as orchids, adenium, aglaonema, anthurium, and palm. This study also analyzed the long-term and short-term relationship between the production and prices of these ornamental plants. The data used were the productions and prices of orchid, adenium, aglaonema, anthurium, and palm at the producer level from 2012 to 2020 obtained from the Agriculture Office of Batu Municipality. Volatility analysis was carried out using the ARCH/GARCH method, the long-term relationship was analyzed using the Johansen cointegration test, and the short-term relationship was carried out using the Error Correction model. The results of volatility analysis showed that all the ornamental plants studied had low price volatility. In addition, the productions and prices of the ornamental plants were cointegrated in the long run, but only the orchid had a short-term relationship with an adjustment period of 2.6 months.


Keywords


ARCH/GARCH; cointegration; ECM; ornamental plants; price volatility

Full Text:

PDF

References


Agustina, T. P., Nisyawati, & Walujo, E. B. (2019). Plant Diversity and Uses of The Home Garden in Pujon Sub-District, Malang Regency, East Java, Indonesia. AIP Conference Proceedings, 2120. https://doi.org/10.1063/1.5115659

Ahmed, H. J., Bashar, O. H. M. N., & Wadud, I. K. M. M. (2012). The Transitory and Permanent Volatility of Oil Prices: What Implications are There for The US Industrial Production? Applied Energy, 92, 447–455. https://doi.org/10.1016/j.apenergy.2011.11.013

Al-Sagheer, N. A. (2021). Magnolia champaca (L.) Baill. ex Pierre (Magnoliaceae): A First Report and A New Record in The Arabian Peninsula (Yemen). Journal of the Saudi Society of Agricultural Sciences, 20(4), 243–247. https://doi.org/10.1016/j.jssas.2021.02.003

Anugerah, A. R., Muttaqin, P. S., & Purnama, D. A. (2021). Effect of Large-Scale Social Restriction (PSBB) during COVID-19 on Outdoor Air Quality: Evidence from Five Cities in DKI Jakarta Province, Indonesia. Environmental Research, 19, 111164. https://doi.org/10.1016/j.envres.2021.111164

Assaf, A., Kristoufek, L., Demir, E., & Kumar Mitra, S. (2021). Market Efficiency in The Art Markets Using A Combination of Long Memory, Fractal Dimension, and Approximate Entropy Measures. Journal of International Financial Markets, Institutions and Money, 71. https://doi.org/10.1016/j.intfin.2021.101312

Assouto, A. B., Houensou, D. A., & Semedo, G. (2020). Price Risk and Farmers’ Decisions: A Case Study from Benin. Scientific African, 8. https://doi.org/10.1016/j.sciaf.2020.e00311

Berger, J., Dalheimer, B., & Brümmer, B. (2021). Effects of Variable EU Import Levies on Corn Price Volatility. Food Policy, 102. https://doi.org/10.1016/j.foodpol.2021.102063

Besanko, D. A., & Braeutigam, R. R. (2014). Microeconomics (5th ed.). Hoboken: John Wiley & Sons, Inc.

Bohl, M. T., & Sulewski, C. (2019). The Impact of Long-Short Speculators on The Volatility of Agricultural Commodity Futures Prices. Journal of Commodity Markets, 16. https://doi.org/10.1016/j.jcomm.2019.01.001

Badan Pusat Statistik Kota Batu. (2020). Statistik Hortikulura Kota Batu Tahun 2019. Batu: Badan Pusat Statistik Kota Batu.

BPS-Statistics Indonesia. (2020). Gross Regional Domestic Product of Provinces in Indonesia. Jakarta: BPS-Statistics Indonesia.

Cheung, Y., & Lai, K. S. (1993). Finite-Sample Sizes of Johansen’s Likelihood Ratio Test for Cointegration. Oxford Bulletin of Economics and Statistics, 55(3), 313–328. https://doi.org/10.1111/j.1468-0084.1993.mp55003003.x

Deng, S., Prodius, D., Nlebedim, I. C., Huang, A., Yih, Y., & Sutherland, J. W. (2021). A Dynamic Price Model based on Supply and Demand with Application to Techno-Economic Assessments of Rare Earth Element Recovery Technologies. Sustainable Production and Consumption, 27, 1718–1727. https://doi.org/10.1016/j.spc.2021.04.013

González-Gómez, J. I., & Marrero, S. M. (2009). A Model for Cost Calculation and Management in A Multiproduct Agricultural Framework. The Case for Ornamental Plants. Spanish Journal of Agricultural Research, 7(1), 12–23. https://doi.org/10.5424/sjar/2009071-394

Greenlaw, S. A., & Taylor, T. (2017). Principles of Microeconomics for AP Courses. Houston: OpenStax.

Gujarati, D. N. (2011). Econometrics by example. New York: Palgrave Macmillan.

Hau, L., Zhu, H., Huang, R., & Ma, X. (2020). Heterogeneous dependence between Crude Oil Price Volatility and China’s Agriculture Commodity Futures: Evidence from Quantile-on-Quantile Regression. Energy, 213. https://doi.org/10.1016/j.energy.2020.118781

Hui-shang, L., Chen-pei, H., Zheng, L., Mei -qi, L., & Xin-zhu, G. (2021). African Swine Fever and Meat Prices Fluctuation: An Empirical Study in China based on TVP-VAR Model. Journal of Integrative Agriculture, 20(8), 2289–2301. https://doi.org/10.1016/S2095-3119(20)63307-X

Piot-Lepetit, I., & M’Barek, R. (2011). Methods to Analyse Agricultural Commodity Price Volatility. In Methods to Analyse Agricultural Commodity Price Volatility (pp. 1–11). New York: Springer. https://doi.org/10.1007/978-1-4419-7634-5_1

Jin, S., Min, S., Huang, J., & Waibel, H. (2021). Falling Price induced Diversification Strategies and Rural Inequality: Evidence of Smallholder Rubber Farmers. World Development, 146. https://doi.org/10.1016/j.worlddev.2021.105604

Kacou, K. Y. T., Kassouri, Y., Evrard, T. H., & Altuntaş, M. (2022). Trade openness, Export Structure, and Labor Productivity in Developing Countries: Evidence from Panel VAR Approach. Structural Change and Economic Dynamics, 60, 194–205. https://doi.org/10.1016/j.strueco.2021.11.015

Khan, N., Ray, R. L., Zhang, S., Osabuohien, E., & Ihtisham, M. (2022). Influence of Mobile Phone and Internet Technology on Income of Rural Farmers: Evidence from Khyber Pakhtunkhwa Province, Pakistan. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2022.101866

Kühl, M. (2010). Bivariate Cointegration of Major Exchange Rates, Cross-Market Efficiency and The Introduction of The Euro. Journal of Economics and Business, 62(1), 1–19. https://doi.org/10.1016/j.jeconbus.2009.07.002

Ligot, S., Gillet, R., & Veryzhenko, I. (2021). Intraday Volatility Smile: Effects of Fragmentation and High Frequency Trading on Price Efficiency. Journal of International Financial Markets, Institutions and Money, 75. https://doi.org/10.1016/j.intfin.2021.101437

Lin, Z. (2018). Modelling and Forecasting The Stock Market Volatility of SSE Composite Index using GARCH Models. Future Generation Computer Systems, 79, 960–972. https://doi.org/10.1016/j.future.2017.08.033

Maguire, P., Kelly, S., Miller, R., Moser, P., Hyland, P., & Maguire, R. (2017). Further Evidence in Support of A Low-Volatility Anomaly: Optimizing Buy-and-Hold Portfolios by Minimizing Historical Aggregate Volatility. Journal of Asset Management, 18, 326–339. https://doi.org/10.1057/s41260-016-0036-1

Mandala, G. S., and Kim, I.-M. (1999). Unit Roots, Cointegration, and Structural Change. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511751974

Manurung, H. (2011). Exotic and Brightly Colored Ornamental Plants. Jakarta: Directorate General for National Export Development, Ministry of Trade Republic of Indonesia

Moledina A.A., Roe T.L., Shane M. (2003). Measurement of commodity price volatility and the welfare consequences of eliminating volatility. Working Paper at the USDA/ERS and the Economic Development Center, University of Minnesota, Minneapolis.

Muflikh, Y. N., Smith, C., Brown, C., & Aziz, A. A. (2021). Analysing Price Volatility in Agricultural Value Chains Using Systems Thinking: A Case Study of The Indonesian Chilli Value Chain. Agricultural Systems, 192. https://doi.org/10.1016/j.agsy.2021.103179

Onatski, A., & Wang, C. (2019). Extreme Canonical Correlations and High-Dimensional Cointegration Analysis. Journal of Econometrics, 212(1), 307–322. https://doi.org/10.1016/j.jeconom.2019.04.032

Poplavskaya, K., Lago, J., Strömer, S., & de Vries, L. (2021). Making The Most of Short-Term Flexibility in The Balancing Market: Opportunities and Challenges of Voluntary Bids in The New Balancing Market Design. Energy Policy, 158. https://doi.org/10.1016/j.enpol.2021.112522

Puspitasari, Kurniasih, D., & Kiloes, A. M. (2019). The ARCH/GARCH Model Application in Analyzing Shallot Price Volatility. Informatika Pertanian, 28(1), 21–30. https://doi.org/10.21082/ip.v28n1.2019.p21-30

Raungpaka, V., & Savetpanuvong, P. (2017). Information Orientation of Small-Scale Farmers’ Community Enterprises in Northern Thailand. Kasetsart Journal of Social Sciences, 38(3), 196–203. https://doi.org/10.1016/j.kjss.2016.08.018

Rotta, T. N. (2021). Effective Demand and Prices of Production: An Evolutionary Approach. Structural Change and Economic Dynamics, 58, 90–105. https://doi.org/10.1016/j.strueco.2021.04.010

Suminah, Suwarto, Sugihardjo, Anantanyu, S., & Padmaningrum, D. (2021). Self Reliance of Ornamental Plants Agribusiness Actors during The Covid Pandemic in Surakarta. IOP Conference Series: Earth and Environmental Science, 905. https://doi.org/10.1088/1755-1315/905/1/012062

Taghizadeh-Hesary, F., Rasoulinezhad, E., & Yoshino, N. (2019). Energy and Food Security: Linkages through Price Volatility. Energy Policy, 128, 796–806. https://doi.org/10.1016/j.enpol.2018.12.043

Taib, N., Ali, Z., Abdullah, A., Yeok, F. S., & Prihatmanti, R. (2019). The Performance Of Different Ornamental Plant Species In Transitional Spaces In Urban High-Rise Settings. Urban Forestry and Urban Greening, 43. https://doi.org/10.1016/j.ufug.2019.126393

Vadlamannati, K. C., & de Soysa, I. (2020). Oil Price Volatility and Political Unrest: Prudence and Protest in Producer and Consumer Societies, 1980–2013. Energy Policy, 145. https://doi.org/10.1016/j.enpol.2020.111719

Wooldridge, J. M. (2013). Introductory Econometrics A Modern Approach (5th Ed.). Mason: South-Western.

Xie, H., & Wang, B. (2017). An Empirical Analysis of the Impact of Agricultural Product Price Fluctuations on China’s Grain Yield. Sustainability, 9(6), 1–14. https://doi.org/10.3390/su9060906

Zheng, J., Tarin, M. W. K., Jiang, D., Li, M., Ye, J., Chen, L., He, T., & Zheng, Y. (2021). Which Ornamental Features of Bamboo Plants will Attract The People Most? Urban Forestry & Urban Greening, 61. https://doi.org/10.1016/j.ufug.2021.127101

Živkov, D., Manić, S., & Đurašković, J. (2020). Short and Long Term Volatility Transmission from Oil to Agricultural Commodities – The Robust Quantile Regression Approach. Borsa Istanbul Review, 20, 11–25. https://doi.org/10.1016/j.bir.2020.10.008




DOI: https://doi.org/10.18196/agraris.v8i1.12342

Refbacks



Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indexed By:

     


Office Address:
Department of Agribusiness, Faculty of Agriculture, Universitas Muhammadiyah Yogyakarta

Ground Floor of F3 Building (Siti Walidah)
Jl. Brawijaya, Tamantiro, Kasihan, Bantul. 55183
Telp.: +62 274 387656, Ext.: 201
HP or WhatsApp: +62 85328737828
Email: agraris@umy.ac.id

AGRARIS is licensed under a Creative Commons Attribution-ShareAlike 4.0  (CC BY-SA 4.0) International License.