Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities

Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities

opiribo_olaniyo

Contact

Opiribo Olaniyo

Contact

Adebanjo Adeniyi

Contact

Franklyn Ogbeide Okogun

Contact

WORLDQUANT University

Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities

Article Fingerprint

ReserarchID

CSTSDE2L666

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

As indicated by various works of literature, climate change has a significant impact on agricultural commodities resulting in variation between demand and supply. The research study adopted quantitative analysis for comparative analysis of price relationships for three pairs of agricultural commodities against closely related products and how weather impacts them. As an interesting comparison, we also selected a pair of non-agricultural commodities for analysis. Downloaded data for the analysis were daily historical price data for the commodities, and daily summary of weather data for precipitation and temperature for the regions were the selected commodities are most produced. Using programming languages like Python and R, we carried out exploratory data analysis using the following statistics, such as graphs, scatter plots of returns, QQ plots for normality, time series diagnostics (AC, PAC) ARIMA, correlation. An exciting part of our work is our model selection, where we used SARIMAX for regressing endogenous data, i.e., commodity prices and exogenous data weather data.

References

43 Cites in Article

Reference Format

T Addison,A Ghoshray,M Stamatogiannis (2016). Agricultural commodity price shocks and their effect on growth in sub-Saharan Africa.
Adi Bhat (2021). Thinking of Different Data Sources: Introduction to Data Collection Methods.
Alan Hylands (2019). Chapter 7: Data Wrangling.
Andrew Beattie,Investopedia (2019). Silver Thursday: How two wealthy traders cornered the Market.
W Easterling,P Aggarwal,P Batima,K Brander,L Erda,S Howden,A Kirilenko,J Morton,J.-F Soussana,J Schmidhuber,F Tubiello (2007). Food, fiber and forest products.
Ellen Friedman,Ted Dunning Time Series Databases: New Ways to Store and Access Data.
Eva Ludi,Christopher Stevens,Leo Peskett,Lídia Cabral,Overseas (2007). Climate change and agriculture: Agricultural trade, markets, and investment.
Patrizia Ghisellini,Marco Setti,Sergio Ulgiati (2015). Energy and land use in worldwide agriculture: an application of life cycle energy and cluster analysis.
Indexmundi (2019). Figure 1.23. Single commodity transfers to beef and veal, all countries, 2000-2016 (real USD).
Indexmundi (2019). Unknown Title.
Indexmundi (2019). Beef and veal projections: consumption, per capita.
Indexmundi (2019). MAP 16: Production of chicken meat (2022).
Indexmundi (2019). MAP 16: Production of chicken meat (2021).
Indexmundi (2019). Unknown Title.
Indexmundi (2019). Unknown Title.
Indexmundi (2019). Unknown Title.
Indexmundi (2019). Capitalism, petty commodity production and the farm enterprise.
Indexmundi (2019). Unknown Title.
Indexmundi (2019). Unknown Title.
Indexmundi (2019). LAS VEGAS SANDS CORP., a Nevada corporation, Plaintiff, v. UKNOWN REGISTRANTS OF www.wn0000.com, www.wn1111.com, www.wn2222.com, www.wn3333.com, www.wn4444.com, www.wn5555.com, www.wn6666.com, www.wn7777.com, www.wn8888.com, www.wn9999.com, www.112211.com, www.4456888.com, www.4489888.com, www.001148.com, and www.2289888.com, Defendants..
Indexmundi (2019). Figure 1.6 Quantity of staple foods available for consumption (per capita, per day) and gross domestic product per capita, 1961-2021.
(2019). 10 Most Censored Countries.
J Lhomme,R Mougou,Mohsen Mansour (2009). Potential impact of climate change on durum wheat cropping in Tunisia.
James Chen (2018). Silver Thursday.
Jasmien De,Winne,Gert Peersman (2018). Agricultural Price Shocks and Business Cycles; A Global Warning for Advanced Economies.
Jeffrey White,Gerrit Hoogenboom (2009). Crop Response to Climate: Ecophysiological Models.
Maria Salvo,Diego Begalli,Giovanni Signorello (2013). Measuring the effect of climate change on agriculture.
G Masters,P Baker,J Flood (2010). Climate change and agricultural commodities.
R Mendelsohn,W Nordhaus,D Shaw (1994). The Impact of Global Warming on Agriculture: A Ricardian Analysis.
R Mendelsohn,A Dinar (2003). Climate, Water, and Agriculture.
Robert Mendelsohn (2007). Measuring Climate Impacts With Cross-Sectional Analysis.
Robert Mendelsohn,Ariel Dinar (2009). Modeling Adaptation to Climate Change.
Lalith Munasinghe,Tackseung Jun,David Rind (2012). Climate change: A new metric to measure changes in the frequency of extreme temperatures using record data.
J Ritchie,U Singh,D Goodwin,L Hunt (1989). A User's Guide to CERES Maize 2.10.
R Robert,Defilippi (2018). Basic Python Programming.
Cynthia Rosenzweig,Ana Iglesias,Günther Fischer,Yuanhua Liu,Walter Baethgen,James Jones (1999). Wheat yield functions for analysis of land-use change in China.
Cynthia Rosenzweig,Francesco Tubiello,Richard Goldberg,Evan Mills,Janine Bloomfield (2002). Increased crop damage in the US from excess precipitation under climate change.
John Porter,Mikhail Semenov (2005). Crop responses to climatic variation.
Santosh Poudel,Koji Kotani (2013). Climatic impacts on crop yield and its variability in Nepal: do they vary across seasons and altitudes?.
S Schneider,W Easterling,L Mearns (2000). Adaptation Sensitivity to Natural Variability, Agent Assumption, and Dynamic Climatic Changes.
Silverprice (2002). Online resources for Silver data.
Osama Elsalih,Kamil Sertoğlu,Mustafa Besim,Abdelhakim Embaya (2019). The Comparative Advantage of Crude Oil in the Top 10 Oil-Producing Countries.
Worldatlas (2019). Economic importance of cocoa to producing countries.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

opiribo_olaniyo. 2021. \u201cAssessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 21 (GJCST Volume 21 Issue C1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 21 Issue C1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification G.1

Submission ReceivedJune 11, 2021
Peer Review Double Blind
Handling Editor
Accepted July 3, 2021
Published July 15, 2021

Version of record

v1.2

Issue date

August 18, 2021

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 3790

Total Downloads: 962

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]