Unit STATISTICAL METHODS FOR DATA SCIENCE
- Course
- Informatics
- Study-unit Code
- A002172
- Curriculum
- In all curricula
- Teacher
- Gianna Figa'-talamanca
- Teachers
-
- Gianna Figa'-talamanca
- Hours
- 42 ore - Gianna Figa'-talamanca
- CFU
- 6
- Course Regulation
- Coorte 2024
- Offered
- 2025/26
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- MAT/06
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English
- Contents
- The course introduces some computational methods to solve some practical problems in quantitative finance.
- Reference texts
- Lecture notes provide by the instructor. Additional useful references are: 1) Glasserman, P., Monte Carlo Methods in Financial Engineering 2) Pascucci A., PDE and Martingale Methods in Option Pricing, Bocconi University Press, Springer Ed. 3) Cesarone F., Computational Finance, Routledge-Giappichelli Ed. 4) Pocci. C., Rotundo, G., De Kok, R., MATLAB for applications in Economics and Finance, Maggioli Ed. 5) Oliva. I., Renò Roberto, Principi di Finanzia Quantitativa, Apogeo Educazione, Maggioli Editore, 2021.
- Educational objectives
- The students should be able to implement most computational problems faced in quantitative finance and risk management.
- Prerequisites
- The students must be acquainted with Matlab software. Hence they are advised to pass the Matlab course provided in the first semester.
- Teaching methods
- Lectures and problem sets with Matlab. The lessons will take place in an interactive way and include the assignment of group work.
- Learning verification modality
- The exam consists in a final report to be discussed at the oral exam.
- Extended program
- The course introduces some computational methods to solve some practical problems in quantitative finance. In particular, the following problems will be examined through introductory lectures and exercise sessions: 1) Random number generation. The Monte Carlo method. Geometric Brownian motion: theoretical properties, estimation and simulation. 2) Monte Carlo method for the valuation of plain vanilla and exotic derivatives (Black and Scholes model). 3) Monte Carlo method to estimate the Greeks of financial derivatives (Black and Scholes model). 4) Discretization schemes of stochastic differential equations beyond Arithmetic or Geometric Brownian motion. 5) Short rate models: theoretical properties, simulation and evaluation of derivative products. 6) Stochastic volatility models: theoretical properties, simulation and evaluation of derivative products. 7) Estimation and calibration methods for model parameters 8) Examples related to insurance products.