Unit MATHEMATICAL METHODS FOR RISK MANAGEMENT
- Course
- Finance and quantitative methods for economics
- Study-unit Code
- A000199
- Curriculum
- Data science for finance and insurance
- Teacher
- Marco Patacca
- Teachers
-
- Marco Patacca
- Hours
- 63 ore - Marco Patacca
- CFU
- 9
- Course Regulation
- Coorte 2023
- Offered
- 2023/24
- Learning activities
- Caratterizzante
- Area
- Matematico, statistico, informatico
- Academic discipline
- SECS-S/06
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English
- Contents
- 1) Linear algebra: linear systems, matrix diagonalization. Principal components analysis.
2)Functions of two variables: the graph, the Taylor's formula, free and constrained optimizations.
3) Continuous and discrete stochastic variables. Probability distributions: density function and cumulative probabilities, moments and quantiles of a distribution. Conditional expectations.
4) Financial Applications. - Reference texts
- 1) Linear algebra
“Eigenvalues and Eigenvectors”, P. Dawkins;
“Linear algebra for economists”, F.Aleskerov, H. Ersel, D.Piontkovski
2) Functions of 2 variables:
“Calculus II, Lecture Notes”, R. Tavakol;
“Essential Mathematics for Economic Analysis”, K.Sydsaeter, P. Hammond, A. Strom
3) Probability Theory:
“A first course in Probability”, S. Ross - Educational objectives
- The main objective of the course is to provide the students with the analytical instruments that are necessary for the management of financial instruments, corporate finance and asset pricing.
All the arguments are first introduced theoretically and then some related case studies are implemented in python. - Prerequisites
- In order to be able to understand and apply the majority of the techniques described within the course, you must have successfully passed the following exams:
- matematica generale
- matematica finanziaria
- teoria matematica del portafoglio
- statistica - Teaching methods
- Face-to-face and practical training
- Other information
- For further details contact the professor to the email address: marco.patacca@unipg.it
- Learning verification modality
- Written test. The exam consists in solving (either analytically or numerically with Python) some exercises on the main topics of the course. The written exam has the aim to test the competence acquired during the class.
- Extended program
- 1) Linear algebra:
Solution of linear system. Eigenvalues and eigenvectors. Diagonalization of a matrix. Spectral theorem for symmetric matrices. Principal components analysis (PCA).
2) A short introduction to the study of two variable functions:
The graph. Parallel and vertical sections. Partial derivatives. The gradient vector and the hessian matrix. The Taylor's formula. Stationary points. Free and constrained optimizations. Lagrange multipliers. Quadratic forms.
3) Probability:
Continuous and discrete stochastic variables. The binomial distribution and the CRR model.
The partition function and the moments of a distribution. Conditional mean. Quantiles. Some distributions: uniform, Pareto, exponential, normal and lognormal. The moment generating function.
4)Applications in finance:
Factorial models. PCA of the term structure of interest rates. Portfolio optimization: Markowitz model, Black-Litterman model. Bootstrap of the term structure of interest rates. Monte Carlo method.