Forecasting the red lentils commodity market price using SARIMA models
Author
Abstract
Suggested Citation
DOI: 10.1007/s43546-020-00020-x
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Anna Borucka, 2023. "Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021.
"El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements,"
Sustainability, MDPI, vol. 13(14), pages 1-23, July.
- Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
- Gavriilidis, Konstantinos & Kambouroudis, Dimos S. & Tsakou, Katerina & Tsouknidis, Dimitris A., 2018.
"Volatility forecasting across tanker freight rates: The role of oil price shocks,"
Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 376-391.
- Konstantinos Gavriilidis & Dimos S. Kambouroudis & Katerina Tsakou & Dimitris S. Tsouknidis, 2018. "Volatility forecasting across tanker freight rates: the role of oil price shocks," Working Papers 2018-27, Swansea University, School of Management.
- Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
- Apergis, Nicholas & Payne, James E., 2017. "Volatility Modeling of U.S. Metropolitan Retail Gasoline Prices: An Empirical Note," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 48(2), September.
- Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
- Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
- Baruník, Jozef & Malinská, Barbora, 2016.
"Forecasting the term structure of crude oil futures prices with neural networks,"
Applied Energy, Elsevier, vol. 164(C), pages 366-379.
- Jozef Barunik & Barbora Malinska, 2015. "Forecasting the term structure of crude oil futures prices with neural networks," Papers 1504.04819, arXiv.org.
- Jozef Barunik & Barbora Malinska, 2015. "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES 2015/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2015.
- Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal & Imtiaz Hussain Khan, 2023.
"Oil price volatility and stock returns: Evidence from three oil‐price wars,"
International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3162-3182, July.
- Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal, 2020. "Oil Price Volatility and Stock Returns: Evidence from Three Oil-price Wars," PIDE-Working Papers 2020:22, Pakistan Institute of Development Economics.
- Taiyong Li & Zhenda Hu & Yanchi Jia & Jiang Wu & Yingrui Zhou, 2018. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning," Energies, MDPI, vol. 11(7), pages 1-23, July.
- Klein, Tony, 2018. "Trends and contagion in WTI and Brent crude oil spot and futures markets - The role of OPEC in the last decade," Energy Economics, Elsevier, vol. 75(C), pages 636-646.
- Getachew Nigatu, 2016. "Assessing the effects of climate change policy on the volatility of carbon prices in reference to the Great Recession," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 5(2), pages 200-215, July.
- Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
- Martínez, Beatriz & Torró, Hipòlit, 2018.
"Hedging spark spread risk with futures,"
Energy Policy, Elsevier, vol. 113(C), pages 731-746.
- Beatriz Martínez Martínez & Hipolit Torro Enguix, 2017. "Hedging spark spread risk with futures," Working Papers. Serie EC 2017-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Feriel Gharbi, 2019. "Time-varying volatility spillovers among bitcoin and commodity currencies," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-2.
- Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
- Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
- Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
- Lv, Xiaodong & Shan, Xian, 2013. "Modeling natural gas market volatility using GARCH with different distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5685-5699.
- Martina Assereto & Julie Byrne, 2020. "The Implications of Policy Uncertainty on Solar Photovoltaic Investment," Energies, MDPI, vol. 13(23), pages 1-20, November.
- Charlot, Philippe & Marimoutou, Vêlayoudom, 2014.
"On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree,"
Energy Economics, Elsevier, vol. 44(C), pages 456-467.
- Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
More about this item
Keywords
Forecasting; Lentils; Model; SARIMA; Seasonal index;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snbeco:v:1:y:2021:i:1:d:10.1007_s43546-020-00020-x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.