ECONOMETRICS FOR FINANCE
Instructional goals
The goal of this course is to allow the students to get a basic preparation for being able to write and estimate financial econometric models.
Intended learning outcomes
The student is able to connect econometrics and mathematical financial modelling in such a way is able to interpret financial phenomena through the lens of case-specific data analysis.
Course Contents
Regression analysis and OLS for financial applications
Instrumental variables and errors of measures.
Time series and dynamic analysis, applied in macro-finance and financial problems.
Reference Books
Hurn, S., Martin, V., Phillips, P.C. and Yu, J., 2021. Financial econometric modeling. Oxford University Press.
Teaching Methods
The course will be held with on line sessions and exercise sessions. The students will learn how to use PYTHON for empirical econometric analysis.
Assessment Method
Attendants: 50% paper written in groups on a financial econometric topic chosen by the teacher + 50% written exam on the topic covered in the course (no Python)
Non attendants: written exam on the topics covered in the course (with Python) and papers that the student have to study independently.
Thesis assignment criteria
Passion for econometrics.
Week 1
Linear Algebra and Probability- Markowitz portfolio theory.
Probability and review of concepts in statistics.
Week 2
Statistics: basic concepts, tests, consistency, asymptotic normality. Maximum Likelihood Estimators.
Exercise session.
Week 3
Trinity of tests: Likelihood Ratio, Wald and Lagrange multiplier tests. OLS in simple regression.
Exercise session.
Week 4
OLS and multiple regression.t and F tests. Statistical significance in a regression.
Exercise session.
Week 5
GLS and miss-specification tests. Empirical application: CAPM and two pass regression.
Exercise session.
Week 6
On line session:
Pooled OLS and Panel Data: Fixed and Random effects.
Exercise session.
Week 7
Introduction to Time series and stochastic processes. White Noise, i.i.d. and martingale processes. Autocovariance function and its estimator. Wald representation. MA process.
Exercise session.
Week 8
AR processes. Stationarity. Non stationarity. Estimation of parameters of AR and MA processes.
Exercise session
Week 9
Forecasting with AR and MA processes. Specification in ARMA models. VAR models.
Exercise session.
Week 10
Returns and their empirical characteristics. Conditional volatility. ARCH and GARCH processes.
Exercise session.
Week 11
Estimation and forecasting of ARCH and GARCH. Specification Tests. Alternative models of volatility.
Exercise session.
Week 12
Risk Management. The basics. VaR and ES.
Review of the contents of the course.