Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Banca e finanza - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2024/2025
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Asset Pricing Models (2024/2025)
Teaching code
4S006069
Academic staff
Coordinator
Credits
9
Language
Italian
Scientific Disciplinary Sector (SSD)
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Period
Secondo semestre LM dal Feb 17, 2025 al May 23, 2025.
Courses Single
Authorized
Learning objectives
The course aims to provide a quantitative approach for dealing with investment and consumption problems under uncertainty. Fundamental topics presented in the course are also related to the efficient structure of financial markets and equilibrium theory.
Students may find useful to follow this course after those of Mathematical Finance, Financial Risk management and, Portfolio Management and Equity markets.
Prerequisites and basic notions
Students are advised to take this course after Finanza Matematica, Financial Risk Management and Derivati
Program
- Introduction to stochastic control through some simple examples
- Discrete-time stochastic control, with examples: theory and applications in Java
- Continuous-time stochastic control, dynamic programming and the HJB equation: theory and application in Java
- Introduction to machine learning: reinforcement learning, supervised learning, deep learning, artificial neural networks
- Using neural networks for a hedging problem ("deep hedging"): theory and application in Java
Didactic methods
Frontal lessons. Recordings will be made available to students after the lesson.
Learning assessment procedures
Written exam and delivery of a project in which the student will have to write a program for the numerical solution of an optimal control problem
Evaluation criteria
Regarding the written exam, the student must demonstrate a critical and in-depth knowledge of the topics covered in the course, both in terms of the theoretical and implementation aspects. The answers must be precise and relevant to the questions.
The numerical program must run without errors and provide the expected results. It must also follow as much as possible the best practices that we will discuss in the course and, in particular, be well documented.
Criteria for the composition of the final grade
The written exam constitutes 75% of the final score, the program the remaining 25%.
Exam language
Italiano. Inglese su richiesta
