NON LINEAR MODELS AND FINANCIAL ECONOMETRICS
- Anno accademico
- 2022/2023 Programmi anni precedenti
- Titolo corso in inglese
- NON LINEAR MODELS AND FINANCIAL ECONOMETRICS
- Codice insegnamento
- EM2064 (AF:358833 AR:188872)
- Lingua di insegnamento
- Inglese
- Modalità
- In presenza
- Crediti formativi universitari
- 6
- Livello laurea
- Laurea magistrale (DM270)
- Settore scientifico disciplinare
- SECS-P/05
- Periodo
- 2° Periodo
- Anno corso
- 2
- Sede
- VENEZIA
- Spazio Moodle
- Link allo spazio del corso
Inquadramento dell'insegnamento nel percorso del corso di studio
Risultati di apprendimento attesi
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.
Prerequisiti
Contenuti
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
Testi di riferimento
A reading list will be provided for every topic.
Modalità di verifica dell'apprendimento
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.
Modalità di esame
Metodi didattici
Altre informazioni
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