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
Linear Algebra, Statistics and Basics of 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 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
On line sessions: 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.