NON LINEAR MODELS AND FINANCIAL ECONOMETRICS
- Academic year
- 2022/2023 Syllabus of previous years
- Official course title
- NON LINEAR MODELS AND FINANCIAL ECONOMETRICS
- Course code
- EM2064 (AF:358833 AR:188872)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Master's Degree Programme (DM270)
- Educational sector code
- SECS-P/05
- Period
- 2nd Term
- Course year
- 2
- Where
- VENEZIA
- Moodle
- Go to Moodle page
Contribution of the course to the overall degree programme goals
Expected learning outcomes
Attendance and active participation in lectures, exercise sessions, tutoring activities, together with the individual study will allow the student to acquire the following knowledge and understanding skills:
- know and use the main mathematical tools necessary to represent complex economic phenomena;
- know the mathematical techniques useful to solve and analyze the proposed models.
- know the statistical techniques useful to test the validity of theoretical economic models on data.
Ability to apply knowledge and understanding.
Through the interaction with the instructors, the tutors, and peers and through the individual study the student acquires the following abilities:
- know how to use quantitative instruments to cope with complex problems related to economic and financial environments;
- know how to choose the most appropriate technique in order to solve the concrete problem under analysis.
Judgment skills, communication skills, learning skills.
Regarding the autonomy of judgment, communication skills and learning abilities, through the personal and group study of the concepts seen in class, the student will be able to:
- formulate rational justifications to the approach used to solve economic and financial problems, understanding their relative strengths and weaknesses;
- know how to formulate and communicate sofisticated quantitative analysis of economic and financial data through the use of mathematical models.
Pre-requirements
Contents
1.1 Decision Theoretical Foundation of Statistics
1.2 Least square and maximum likelihood inference principles
1.3 Bayesian inference: Prior Distribution, Posterior Distribution, Bayesian Estimator
1.4 Bayesian nonparametric methods
2 Numerical methods for simulation-based inference
2.1 Markov-chain Monte Carlo
2.2 Gibbs Sampling
2.3 Metropolis-Hastings
3 Bayesian Linear Regression.
4 Probit and Logit models. Truncation and censoring. Models for count data.
5 Bayesian SUR and VAR
6 Bayesian Latent Variable Models
7 Nonlinearities in Financial Data
7.1 Conditional Heteroscedasticity: ARCH and GARCH Models, Stochastic Volatility Models
7.2 Switching Regime Models
8 State-space models
8.1 Kalman filter
8.2 Hamilton filter
8.3 Particle filters
Referral texts
A reading list will be provided for every topic.
Assessment methods
The assignments are intended to verify the progress in the learning activity and the abilities to go deep autonomously to the heart of the topics of the course. The assignments consist of problems to solve and questions to reply regarding additional reading material properly referenced in the text of assignments.
The final project develops or extends further the topics of the course and includes an original contribution of the student, such as new models, analysis of their properties, or original applications to real data. The project preparation aims at putting into practice the knowledge acquired. The oral presentation of the project aim at verifying the level of knowledge of the topics in the projects and the ability to communicate them in a clear and rigorous way.
Teaching methods
Teaching language
Further information
Type of exam
2030 Agenda for Sustainable Development Goals
This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development