## Admission Requirements

No particular requirement for students having successfully completed a BSc in physics or mathematics. Students with a different background are advised to seek prior interaction with the instructor.

## Description

The course provides an overview of the subject of Econophysics, presented as an active research field with many open questions, rather than an established discipline with settled results. Economic and financial systems are regarded as complex systems composed of many interacting and networked units. Extensive empirical results and theoretical tools, including probability theory, random matrix theory and graph theory, are introduced. The course has a modular structure and its schedule is organized into four intense weeks, roughly one per month, each covering a relatively self-contained topic of increasing complexity.

Topics: * Introduction to Econophysics and elements of probability theory; * Single financial time series: empirical properties versus the random walk model; * Multiple financial time series: empirical cross-correlations and Random Matrix Theory; * Financial and economic networks: empirical properties and random graph models.

## Course objectives

Understanding what types of scientific problems the field of Econophysics deals with;

Understanding the evolution of some key research questions in the field;

Acquiring a sufficient knowledge of probability theory to be applicable to Econophysics problems;

Acquiring a scientific perspective giving priority to reconciling models with empirical data.

## Timetable

See timetable in Brightspace.

## Mode of instruction

Combination of frontal lectures, group work, and seminars/presentations. More information and all material will be available on Brightspace.

## Assessment method

Student presentations and written exam.

As homework assignment during the course, students will be forming small groups (of 2-3 students each) and each group will prepare a short presentation, to be delivered to the rest of the class, about a pre-assigned recent research article in the field of Econophysics. The topic of such articles, while being connected to the course content, will also expand beyond it and provide extra learning material for the class. Students will receive a grade for their presentations, based on the demonstrated level of understanding and clarity.

At the end of the course, a compulsory written examination will take place. If the grade of the written exam is greater than or equal to 5.5, then the final grade of the course will be calculated as the (rounded) maximum of the grade of the written exam and the 50%-50% average of the written exam and the presentation. If the final grade is not successful (below 6/10), or if the written exam is not successful (below 5.5), students can take a written retake exam and the final grade will be recalculated using the same rules as above.

## Reading list

Required: course slides and other notes/material (available on BrightSpace).

Suggested: textbook “Econophysics: An Introduction” by S. Sinha, A. Chatterjee, A. Chakraborti, B.K. Chakrabarti (Publisher: Wiley-VCH, 2010; ISBN: 978-3-527-40815-3).

## Registration

From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.

## Contact

Lecturer: Dr. D. Garlaschelli

## Remarks

**Transferable skills**

Scientific curiousity, creating connections among different contexts and disciplines, thinking out of the box, being able to formulate hypotheses about problems for which one has no prior knowledge, abstraction and generalization in a multidisciplinary context.