Martin's webpage - Teaching

Courses details

Title: Financial Econometrics, 6 ECTS, BSc. level, 5th semester.

Period: Academic years 2012/22, 2022/23, 2023/24.

Given at: University of Trieste.

Study program: International economics and financial markets.

Course coordinator: M. Magris.

Course instructor: M. Magris.

Course structure: Lectures on theory with exercises in class and take-home assignments.

Content and description: This course was given for the first time in the fall of 2021 and has been repeated in 2022. This is an introductory course to Financial econometrics for students with a solid background in economics and good quantitative skills. The syllabus of the course can be found here. The course merges theory with practice. Theoretical aspects are covered in detail with proofs, carried out on the blackboard, and numerous examples based on historical data from modern markets are provided throughout the course. Methods and models are empirically evaluated and discussed by the instructor, with examples and codes written and running in R. Students are required to solve take-home assignments related to the exercises shown in class and the theory covered on a chapter-by-chapter basis. The course material (slides, exercises, and codes) is entirely prepared by the instructor; an example can be found here. Sample recordings of the lectures are available upon request.

Title: Econometrics, 9 ECTS, MSc. level, 1st semester.

Period: Academic year 2022/23, 2023/24

Given at: University of Trieste

Study program: Economics, environment, and development & Statistics and Actuarial Sciences

Course coordinator: Prof. G. Carmeci.

Course instructors: G. Carmeci, M. Magris.

Course structure: The course is divided into two parts. The first is 45 hours (6 ECTS), and the second is 15 hours (3 ECTS). I provided lectures for the last 15 hours, of which two were in-class exercises.

Content and description: This course was given for the first time in the fall of 2022. This is an advanced course in econometrics for MS.s. students. The second part is compulsory for only those students who did not have an introductory course in econometrics during their former studies. The syllabus of the course can be found here. The second module introduces instrumental variable regression and time-series models by following the content of the reference book.

Title: 2018 & 2019 Financial Engineering, 3 ECTS, MSc. level, 4th semester.

Period: Academic years 2017/18, 2018/19.

Given at: Tampere University.

Study program: Elective course.

Course coordinator: Prof. J. Kanniainen.

Course instructor: M. Magris.

Course structure: Theory classes and exercises. I was responsible for the exercise classes and final projects.

Content: and description: the course merged theory and exercises and a final project accounting for 30% of the exam grade. The course covers advanced option and derivative pricing with discrete and continuous-time models. The course name is mutated, yet the syllabus is aligned with the one in the following link. In the first instance, I was responsible for exercise classes, 2 hours per week for eight weeks, by tutoring the students in solving hands-on assignments involving Matlab coding related to the theory discussed in class and extending some parts of it by providing additional details. In the second instance, I was responsible for designing the project work, assisting students in its completion, and evaluating their projects. The project consisted of pricing a complex financial derivative, an investment product issued by Nordea bank, with Monte-Carlo methods, that is, a (Matlab) code performing the pricing.

Teaching Philosophy

As a student, what I found to be most helpful in preparation were hands-on and real-world examples, yet this kind of learning approach needed to be more well-established and rooted in the teaching culture of many of the instructors I had. However, through the somewhat old-fashioned way of delivering lectures based on an extensive theory-proof scheme, I got to learn the whys and hows of the topics, making me curious to know more, leading me to doctoral studies. Yet the “Aha” moment of understanding how links to real life often came even years later.

In this light, the sweet point of useful but solid learning is between theory and practice, whereby practice, I mean solved exercises, and hands-on/take-home assignments. In my classes, I provide formalism and theory immediately followed by examples and applications to give the students a grasp of the theoretical arguments and what they translate into practice. On the other hand, I grasp students’ attention, engagement, and overall focus on the course with take-home assignments and small exercises to be solved in class. Indeed, given the theory and examples, there’s no better way to consolidate and verify the learning than asking to do. And it is by doing that we challenge ourselves and face limitations and leaks in our knowledge.

The above translates into an interaction of three factors, theory, examples, and assignments. If the theory is missing, the foundations are not in place, and if examples are missing, the theory may appear not useful or just a theory. If examples are missing, the actual/practical understanding of the problems and the procedure to solve them is lacking. Yet to provide some guidance on how problems can be solved and avoid a sense of frustration, I show solutions and entirely work out some examples in class.

Even though exercises and assignments are time-consuming for the instructor and often cause headaches for the students, they facilitate the learning and the understanding of the theory, which cannot live on its own. In such a way, the learning also becomes relevant, meaningful, and more interesting. Indeed students in departments like Engineering, Economics, and Statistics are much more oriented toward doing, and doing is what they are there for and expect.

In class, I try to facilitate a relaxed and collaborative atmosphere where the interactions are two-way, questions are welcome, and where students are encouraged to provide inputs and suggestions to calibrate the discussion, to the extent that this is possible, around the level and interests of the audience.

Teaching methods and materials

On the very first day of the course, I tell my students that they are about to embark on a long journey where theory, practice, and assignments are deemed to render the learning solid and useful. Indeed I tell them that my greatest hope is that they will find the class useful and that they will be able to recall the course content when needed. To achieve this and put in place the above philosophy, I use an extensive set of lecture notes as a reference for self-study. In class, I avoid their direct reading; instead illustrate their content in a more discursive way with extensive use of the blackboard for critical concepts and proofs, promoting a direct interaction with students, e.g., by asking them to comment on plots or providing inputs on solutions. Worked examples and codes are provided in widespread language codes such as R, scripts and data made available. Solutions for the take-home assignments are discussed and thoroughly commented on in class. In this regard, I often take examples and data from existing research papers.

Courses do generally have a reference book, yet I tend to provide a list of readings of accessible research papers that the interested student can read to learn more about and dig further into the topics presented in class.