ECONOMETRICS FOR FINANCE
Instructional goals
The students will learn some basic topics of Financial Econometrics. The students will be able to formulate and solve problems related to quantitative finance from an empirical point of view. Finally, they will implement and interpret outputs of a statistical software.
Intended learning outcomes
Knowledge and understanding:
the course will offer basic techniques useful for quantitative finance and financial econometrics. The knowledge will enable students to know how to program and interpret outputs of a statistical software (MATLAB) useful for empirical quantitative analysis questions in finance.
Applying knowledge and understanding:
the students will be able to implement in MATLAB financial econometrics models.
Applying knowledge and understanding:
the students will develop a critical knowledge and will be able to find the best econometric model for a given financial problem.
Communication Skills: the student will be able to communicate precisely results and discuss and interpret the theory of financial econometrics.
Learning skills: the students will be able to formulate and solve problems in financial econometrics.
Course Contents
1. Ordinary Least Squares and their implementation
2. Estimation of models for longitudinal data (panel data models)
3. Time Series Analysis: estimation and forecasting.
4. Volatility models – ARCH e GARCH.
5. Risk management.
Reference Books
Financial Econometric Modelling – Hurn, Martin, Yu and Phillips
Teaching Methods
The course will be held with on line sessions and exercise sessions. The students will learn how to use MATLAB for empirical econometric analysis.
Assessment Method
Written exam.
If the number of students in an exam session is less than 5, the exam will be oral.
Thesis assignment criteria
Interest for the subject
Does the syllabus cover sustainability topics?
No
Week 1 Contenuto sessioni on line e on campus
On line sessions: Linear Algebra and Probability- Markowitz portfolio theory.
On campus session: Probability and review of concepts in statistics.
Week 2 Contenuto sessioni on line e on campus
On line sessions:
Statistics: basic concepts, tests, consistency, asymptotic normality. Maximum Likelihood Estimators.
On campus session:
Exercise session.
Week 3 Contenuto sessioni on line e on campus
On line sessions:
Trinity of tests: Likelihood Ratio, Wald and Lagrange multiplier tests. OLS in simple regression.
On campus session:
Exercise session.
Week 4 Contenuto sessioni on line e on campus
On line sessions:
OLS and multiple regression.t and F tests. Statistical significance in a regression.
On campus session: Exercise session.
Week 5 Contenuto sessioni on line e on campus
On line session:
GLS and miss-specification tests. Empirical application: CAPM and two pass regression.
On campus session:
Exercise session.
Week 6 Contenuto sessioni on line e on campus
On line session:
Pooled OLS and Panel Data: Fixed and Random effects.
On campus session:
Exercise session.
Week 7 Contenuto sessioni on line e on campus
On line session:
Introduction to Time series and stochastic processes. White Noise, i.i.d. and martingale processes. Autocovariance function and its estimator. Wald representation. MA process.
On campus session:
Exercise session.
Week 8 Contenuto sessioni on line e on campus
On line sessions:
AR processes. Stationarity. Non stationarity. Estimation of parameters of AR and MA processes.
On campus session: Exercise session
Week 9 Contenuto sessioni on line e on campus
On line session:
Forecasting with AR and MA processes. Specification in ARMA models. VAR models.
On campus session: Exercise session.
Week 10 Contenuto sessioni on line e on campus
On line session:
Returns and their empirical characteristics. Conditional volatility. ARCH and GARCH processes.
On campus session:
Exercise session.
Week 11 Contenuto sessioni on line e on campus
On line sessions:
Estimation and forecasting of ARCH and GARCH. Specification Tests. Alternative models of volatility.
On campus session:
Exercise session.
Week 12 Contenuto sessioni on line e on campus
On line sessions:
Risk Management. The basics. VaR and ES.
On campus session
Review of the contents of the course.