|Strategic Economic Analysis of Agri-Food Markets|
|Instructor:||James (Jim) Vercammen|
|Class Schedule:||Class: Tues/Thurs 9:30 – 11:00 am, 1003 FSC
Lab: Tues 2:00 – 3:00 pm, Location TBD
|Important Course Pages|
Dr. James (Jim) Vercammen: firstname.lastname@example.org
Room 268 Henry Angus
Phone (604) 827 3844.
Class Schedule: Days /Time Lectures & Labs
Class: Tues/Thurs 9:30 – 11:00 am, 1003 FSC.
Lab: Tues 2:00 – 3:00 pm, Location TBD (check schedule, the lab will not meet every week).
Course Description and Structure
This course focuses on the economics of agricultural commodity prices, with a particular emphasis on commodity futures. The course is divided into two modules: (1) Pricing Fundamentals; and (2) Commodity Futures. Both modules contain a blend of institutional description, theoretical analysis and R‐enabled empirical analysis. Module 1 consists of a series of case studies which highlight commodity pricing over space and time (e.g., crude palm oil, lumber, potatoes and fertilizer). Module 2 focuses on the U.S. corn market and the Chicago Mercantile Exchange (CME) futures market for corn. In addition to a midterm and final exam, students will complete a project which relates to Module 1 in the first half of the semester, and a multi‐part assignment which relates to Module 2 in the second half of the semester. During most lectures students will use R for interactive class activities. R will also be used extensively in the project and the assignment. Despite the extensive use of R, the course emphasis is on economic concepts, which means that students will not be tested on R in the midterm and final exams.
After this course, students will be able to:
- Explain how the spatial and intertemporal laws‐of‐one‐price theoretically link commodity prices over space and time.
- Model spatial and intertemporal laws‐of‐one‐price outcomes in commodity case studies.
- Describe the rules and procedures for trading commodity futures.
- Use the theoretical determinants of commodity spot and futures prices to explain pricing seasonality, futures market price spreads, basis adjustments, hedging effectiveness and profits for institutional investors in the U.S. corn market.
- Implement R programming to visualize commodity prices (graphs and maps), optimize welfare functions (for the purpose of price prediction) and randomly simulate spot and futures prices.
|Activity||Date||Percent of Grade|
|“Overview of Futures” Take‐Home Quiz||Sept 13||2%|
|Module 1 Report||Proposal: Sept 26
Report: Oct 8
|Module 2 Assignment||Nov 26||8%|
|Midterm Exam||Oct 26||35%|
Course Material and Schedule
Modules 1 and 2 each have eight sections. The course material for the 16 sections is organized as follows:
- 16 HTML files serve as course readings – one for each section. These files can be accessed from https://vercammen.github.io/. Most of the HTML files were created using R Markdown.
- The data files and R functions which are used during the interactive lectures can be accessed from https://github.com/vercammen/MFRE (see the Read Me file for instructions).
- The course introduction, lecture slides, project and assignment descriptions, course quiz and course recordings can be accessed using the Canvas site for this course.
Use Piazza (accessed from Canvas or directly) to post course‐related questions for Prof Vercammen or teaching assistant Krisha Lim. Use e‐mail to contact Prof Vercammen or Krisha for matters which require a confidential response (e.g., class absence notification).
The landing page of https://vercammen.github.io/ shows the topics of the two modules and 16 sections. The sequence of lecture delivery is as follows.
Week 1 (September 6 – 12)
- Module 2A (Futures Overview) is covered first to ensure that students who are opting to participate in the “Introduction to Commodity Futures” module by Nishant Kalia are familiar with the mechanics of trading commodity futures.
- Prof Vercammen is out of town this week:
- Tues Sept 13: Krisha will play a Zoom recording of Prof Vercammen’s Module 2A lecture.
- Thurs Sept 15: Krisha will use this lecture to show you some R essentials for FRE 501.
- An at‐home Canvas quiz which is worth 2% of your final course grade must be completed by 11:59 pm on Monday, September 13th. This quiz will cover the concepts covered in the September 13th Zoom recording and the Module 2A reading on https://vercammen.github.io/.
Weeks 2 through 6 (approximately)
- Modules 1A through 1F, beginning with 1A (see https://vercammen.github.io/)
Weeks 7 through 13 (approximately)
- Modules 2B through 2G, beginning with 2B (see https://vercammen.github.io/)
- Modules 1G, 1H and 2H if time permits
There are relatively few readings for this course since the case study themselves are the main learning resource material. There are many books written about commodity futures but none are well suited to this course because of their focus on finance rather than economics, and their focus on energy/metals rather than on agricultural commodities. When readings are assigned, the assignment appears at the top of the case study.
Course Policy: Missed Exams and Late Assignment/Project
Late Assignment/Project not accepted: To be fair to all students, a late assignment/project will not be accepted and there is no partial score for late submissions, no makeup opportunity, and no reallocation of marks, do‐overs, or extra credit options.
Working with Others on the Assignment: You may work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, you will both be given a 0 in the question &/or assignment without recourse and this will be handled according to the policies of the program/university.
Free Riding in the Group Project: You are encouraged to work in teams of two but you do have the option to work solo. In both cases, you can work with non‐team members to get ideas and solve coding problems, but your group submission must be unique (i.e., no plagiarism – see below). If a problem with free riding is anticipated then be sure to document task assignments and task accomplishments so that differential grades for the project can be assigned when warranted.
Unable to Write Midterm Exam Due to Illness: If you are unable to write the midterm exam, you must have a verifiable doctor note and you must contact me before or immediately after the scheduled date/time and present documentation explaining your absence. If the excuse is considered legitimate, then the weight of the midterm exam will be transferred to the final exam. There will be no makeup midterm examinations.
COVID‐19 concerns and Exam Writing: The midterm and final exams are expected to be written in‐person rather than written on‐line. If COVID‐19 concerns prevent the exam from being written in person, either for an individual student or for the entire class, then Plan “C” will be used. An individual who wants to opt out of the in‐person exam because of illness with COVID‐19 must have written approval from the Director of the MFRE program.
Plan C with 3 or fewer students not writing the in‐person midterm or final exam
- Each student who chooses to not write the in‐person exam will have an individual Zoom exam, which covers the same material as the in‐class written version of the exam.
Plan C with more than 3 students not writing the in‐person midterm or final exam
- Each student who has written approval from the MFRE Director will write an essay‐based at‐home personalized questions version of the exam with Zoom coverage as required.
Plan C with in‐person exam being not allowed by UBC
- All students will write an at‐home exam with personalized questions.
Course Policy: Academic Honesty
Academic dishonesty and plagiarism are taken very seriously in the MFRE program and can result in a range of punitive measures, which could include failing the program. It is each student’s responsibility to review and understand what constitutes academic dishonesty and plagiarism and how to avoid them.
Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:
- Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
- Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
- Submitting others work as your own, may include but not limited to i. using, or attempting to use, another student’s answers; ii. providing answers to other students; iii. failing to take reasonable measures to protect answers from use by other students; or iv. in the case of students who study together, submitting identical or virtually identical assignments for evaluation unless permitted by the course instructor.
- Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
- Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.
Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non-acceptable conduct for examinations and graded assignments as presented via FRE code of conduct guidelines; course syllabus and instructors; and UBC academic misconduct policies, Review the following web sites for details: http://www.calendar.ubc.ca/Vancouver/index.cfm?tree=3,54,111,0 http://learningcommons.ubc.ca/academic-integrity/
Penalties for Academic Dishonesty: The integrity of academic work depends on the honesty of all those who work in this environment and the observance of accepted conventions. Academic misconduct is treated as a serious offence at UBC and within the MFRE program. Penalties for academic dishonesty are applied at the discretion of the course instructor. Incidences of academic misconduct may result in a reduction of grade or a mark of zero on the assignment or examination with more serious consequences being applied if the matter is referred to the Dean’s office and/or President’s Advisory Committee on Student Discipline. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.
The How, What and Why of FRE 501
Please read this document carefully before coming to the first class!
Why is FRE 501 a MFRE pillar term 1 course? This is because after you graduate from MFRE you may:
- Work for an agri‐business firm where agricultural commodities are traded for domestic processing or export.
- Work for an NGO or government agency with a focus on food security and agricultural commodity prices.
- Work for a firm whose input costs or product selling price depends directly or indirectly on commodity prices (e.g., commercial bakery, brewery, ethanol plant).
- Work for a consulting firm or government agency with a focus on producer income and agricultural commodity prices.
- Start a small business that requires knowledge of the economics of agricultural commodity prices.
- Trade commodities yourself and view commodities as part of your investment portfolio.
Before starting this course, what should I know about how futures markets work?
- This course will be taught assuming no prior knowledge of commodity futures. For those of you familiar with derivatives such as futures and options from a previous course or work experience this course will expand your knowledge in a number of unique dimensions.
Will I be analyzing large datasets of commodity prices (e.g., time series analysis)?
- No, this course uses small data sets and simulated prices to illustrate the concepts.
- A course in time series, which includes the analysis of commodity prices, is generally offered in term 2. Look for details later in the term.
How is the FRE 501 course organized?
- The course is split into two modules, each containing sections which highlight the major concepts connected to strategic decision making within the agricultural commodity sector.
- Each section highlights a specific concept using a case study. Module 1 has a unique case study for each of the eight sections (e.g., palm oil, bananas). Module 2 has one extended case study for U.S. corn which carries through for each of the eight sections.
Why does this course utilize case studies?
- Case studies are a unique way to combine strategic decision making theory in the commodity sector with quantitative data analytics. Using case studies, we can illustrate the concept in a real‐world setting.
- Your ability to get the most out of these case studies requires you to be prepared for and fully engaged during the course lectures.
What is the specific content of the case studies?
- Module 1 separately focuses on the spatial and intertemporal laws‐of‐one‐price (LOP). In 1A we identify the dominant importers and exporters in the global market for crude palm oil and then check whether price differences across these countries conform to the spatial LOP. In 1B and 1C we rely on Adam Smith’s “invisible hand” to solve for the spatial price equilibrium, first in the North American market for lumber, and then in the global market for tomatoes. The focus of 1D is spatial price integration in the context of fertilizer prices across the African continent. Storage as the underlying driver of the intertemporal LOP is the focus of 1E, and Australian potatoes is the case study application. Module 1 contains two additional topics which go beyond the LOP. Specifically, price discrimination in the global banana market is the focus of 1F, and pricing in a vertical beef supply chain is the focus of 1E.
- Module 2 integrates the spatial and intertemporal LOP in the context of the U.S. corn market. Futures markets are introduced in 2A and the various mechanisms of price discovery in futures markets are featured in 2B. Convenience yield as a determinant of pricing seasonality is the topic of 2C. To connect spot prices and futures prices, the intertemporal basis and the spatial basis are examined in 2D and 2E, respectively. In 2F the concept of roll yield and profits for institutional investors in commodity futures is featured. The very important topic of hedging as a means to manage price risk is examined in 2G.
How do I work through the case studies to prepare for FRE 501?
Before each case study:
- Access the assigned case study at https://vercammen.github.io/ and carefully read the entire case study before coming to class (case study assignments are in the lecture slides).
- Case studies are organized as follows: assigned readings; learning outcomes; case content with questions; datasets and R code; and summary.
- Assigned readings: Read the readings to obtain relevant background.
- Learning outcome: Focus on the case study learning outcomes, especially the theoretical concepts (e.g., the spatial law‐of‐one price), in order to gain a deeper understanding of the case study. Doing this will allow you to dive into the case content with confidence. Always do extra research on your own if you are not familiar with the concept.
- Content/Questions: Read the entire case and review the questions provided throughout the case study. Be ready to discuss/provide your view/answer as this active participation is an expectation for the course.
- Datasets and R code: Keep in mind that R coding will be used in most classes but only as a way to manage the case study data and highlight the case study concepts. The concepts and not the R programming will always be the focus of the case studies. If you can spare the time, feel free to run the R code before you arrive at class. Doing so will allow you to focus more on the case study concept and less on the R code when in class. Note, the simpler components of the R code will be explicitly discussed in class. However, a more detailed discussion of the code and opportunity to sharpen your R coding skills will come from time spent with Krisha in the FRE 501 labs and in her office hours.
After each case study:
- Be sure you can clearly state the concepts covered and summarize the case, main discussion points, learning outcomes and conclusions. Doing this immediately after the case study will make preparing for the midterm and final exams much easier and much more effective.