The aim of the course is to strengthen the basic analytical techniques necessary for the management of financial instruments, corporate finance and asset pricing. The course consists in two parts: basic mathematics and statistics, and financial applications. All the arguments are first introduced theoretically and then some related case studies are implemented in Python.

Students will acquire the quantitative skills necessary for risk management and portfolio management. Alongside the more purely modeling aspects, application aspects are introduced through the use of dedicated software in multiple case studies. At the end of the learning path the students will be able to apply the acquired knowledge and techniques for risk and portfolio management, also through the implementation of the presented techniques by means of Python programming language. The implementation of case studies on real data will allow the student to gain experience on how a problem is formalized, how it is coded and finally on the interpretation of the results. The enhancement of quantitative methods for the analysis and solution of financial problems will help the student to focus attention on the really important variables, and therefore to communicate more effectively and clearly, to specialist and non-specialist interlocutors, the proposed solution and results. The classroom exercises, the discussion of the case studies and the exercises at home will help the student to become familiar with the analytical tools learned in order to be able to face the problems of risk measurement and management in a largely autonomous way, and the necessary updating of the knowledge and models in continuous evolution in the financial sector.

Main topics of the course: fundamentals of linear algebra; constrained and unconstrained optimization; fundamentals of probability theory; mean-variance optimization and portfolio management; efficient frontier; fundamentals of linear regression; CAPM, idiosyncratic and systematic risk; fixed income securities and term structure of interest rates; principal component analysis.

Sydsaeter, K., Hammond, P., & Strom, A. (2016). Essential mathematics for economic analysis. Pearson Education UK. Ross, S. M. (2020). A first course in probability (Tenth, Global ed.). Pearson. Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2010). Modern portfolio theory and investment analysis (8th, international student version ed.). John Wiley & sons. Stock, J. H. and Watson, M. W. (2014) Introduction to Econometrics (quarta edizione), Pearson Ed. Greene, W.H. (2011). Econometric Analysis (settima edizione), Prentice Hall.

Lectures complemented by Python implementations of case studies and in-class theoretical exercises.

The course grade is based on two assessments: an optional midterm exam and a final exam. The midterm exam is an individual written test covering topics from the first half of the course. Students’ knowledge will be assessed through theoretical questions, computational exercises, and programming tasks in Python. The final exam is an individual written test covering all topics taught in the course and has the same structure as the midterm exam. Students who have taken the midterm will complete a reduced version of the final exam. It is important to emphasize that the midterm does not exempt students from the first part of the course content. Even in its reduced form, the final exam covers the entire syllabus. The final grade for students who have taken the midterm is computed as follows: midterm exam (40%) and final exam (60%). For students who do not take the midterm, the final grade is based entirely on the final exam (100%). The midterm is considered valid only if all of the following conditions are met: it is the student’s first exam attempt, and it is taken during the winter examination session.

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