EMPIRICAL FINANCE

Giacomo Morelli

Instructional goals

After following this course, students should be able to: - Evaluate and reflect upon empirical studies using financial markets data. - Apply econometric methods in analysing prices, returns and volatilities from financial markets. - Generalise the results from empirical analyses to financial market theories. - Adopt a computational tool (MATLAB) for the implementation of econometric methods for the analysis of financial time series

Prerequisites

The prerequisites are: - Applied Statistics and Econometrics - Econometric Theory - MATLAB

Intended learning outcomes

Knowledge and understanding: The course will offer theoretical and empirical tools to analyze financial data. This course provides advanced knowledge and analytical resources that will enable students to understand and test autonomously the implications of the main financial theories and to construct efficient and robust prections of key financial variables, such as volatility. Applying knowledge and understanding: The students will be able to: • Carry out univariate and multivariate time series analysis of financial variables • Establish the effect of macroeconmic announcements (or of other specific events) on financial prices (event studies analysis) Making judgements: We expect students to be able to analyze financial models to demonstrate a critical understanding of the scope and empirical challenges of financial theories, through their empirical assessment with the application of econometric methods to real data. Students are asked to use the software MATLAB to gain insights on the econometric methodologies and their implementation Learning skills: This course will contribute to empower learners giving them the theoretical, econometric and programming tools to autonomously study financial data and verify financial theories.

Course Contents

1) The econometrics of univariate and multivariate time series models 2) Volatility modeling: a) Univariate GARCH models b) Multivariate GARCH models 3) The predictability of asset returns 4) Event Study Analysis 5) Present value relations 6) Market Microstructure 7) Risk Management Models

Reference Books

Main references: - John Y. Campbell, Andrew W. Lo, Archie Craig Mackinlay, The Econometrics of Financial Markets, Princeton University Press 1996 - Christian Gourieroux and Joann Jasiak, Financial Econometrics: Problems, Models, and Methods, Princeton University Press, 2001 - Oliver Linton, Financial Econometrics: Models and Methods, Cambridge, 2019 - Specific material for the first part of the course on the econometrics of time series will be provided by the teacher

Teaching Methods

Lectures on relevant empirical issues. Students’ participation during lectures is strongly encouraged and will be considered in the final assessment. Weekly assignments require the use of MATLAB to solve empirical exercises based on real data.

Assessment Method

Oral exam + weekly problem sets + take home assignment

Thesis assignment criteria

None

Week 1

Univariate time-series analysis

Week 2

ARMA, random walk, testing for unit-root

Week 3

Return predictability and testing EMH

Week 4

Event Studies

Week 5

The vector autoregressive model: properties and estimation

Week 6

Cointegration, cointegrated systems, estimation and testing

Week 7

Present value relations

Week 8

Volatility modeling: Univariate

Week 9

Volatility Modeling: Multivariate

Week 10

Market Microstructure

Week 11

Risk Management Models

Week 12

Non parametric volatility measurements