Advanced Finance Modelling (2235.YR.015784.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Carlo Sala	Economía, Finanzas y Contabilidad	ENG
Year 1	Antonio Rubia Serrano	Economía, Finanzas y Contabilidad	ENG

The course will provide an overview of some of the most important empirical methods used in Finance to deal with datasets in the areas of Corporate Finance and Asset Pricing. After introducing programming in Stata, a powerful and versatile specialized software, we will cover a) panel data regression analysis, and b) volatility modelling and forecasting of financial returns using real data sets. The course is meant as a continuation of Financial Modelling.

Learn advanced estimation methods used in Corporate Finance and Asset Pricing involving panel data; modelling and forecasting of the volatility of financial returns; utilize Stata for computation, visualization, and analysis of time series data.

1. Induction to Stata 1.1 Basic Stata Commands 1.2 Loading data sets 1.3 Data transformation and workspace management 1.4 Programming in Stata 1.5 Time Series operators

2. Panel Data Regression 21. Introduction 2.2 Arranging panel data. 2.3 Pooled regressions 2.4 Panel data (fixed/random effect) regressions 2.5 Dynamic regressions.

3. Volatility modelling and forecasting 3.1 Introduction 3.2 Prices and returns. 3.3. Conditional volatility models 3.4 Forecasting volatility 3.5. Value at Risk.

The course is organized in three main blocks:

1. Introduction to Stata Programming
This part aims to provide students an introduction to Stata programming. Students will learn how to load and manage variables in Stata, how to merge different datasets, deal with time variables, building loops and running regression using Stata software.

2. Panel Data
The availability of big data makes it possible to use panel data models involving observations from individuals/firms sampled regularly over time. This allows us to characterize financial data models more efficiently than using simple regressions. We will cover the main estimation techniques implemented with Stata, including pooled and panel data regressions.

2. Volatility Modelling and Forecasting
The volatility of financial returns can be predicted accurately building on suitable time series models that exploit the information embedded in the own returns. We will review the main models used to capture time-varying volatility and learn how to construct forecasts useful for, among others, risk measures such as Value at Risk.

The handouts provided by the professor would cover the whole course. No additional material is needed. Handouts represent a guideline for the course preparation but not all technicalities contained in them are required to pass the exam, therefore the exam will only cover topics presented in class and students are required to actively participate. Interested students can also refer to additional bibliographic sources:

Introductory Econometrics: A Modern Approach, by Jefrey M. Wooldridge. 6th Edition, CENGAGE Learning Custom Publishing, 2016.
Analysis of Financial Time Series, by Ruey S. Tsay. 3rd Edition, John Wiley and Sons, 2010.
Introductory Econometrics for Finance, by Chris Brooks. 3rd Edition, Cambridge University Press, 2014.
Time Series: Theory and Methods, by Peter J. Brockwell and Richard A. Davis. Springer, 1991.
Time Series Analysis, by James D. Hamilton. Princeton, 1994.
Introduction to Modern Time Series Analysis, by Gebhard Kirchgässner, Jürgen Wolters, and Uwe Hassler. 2nd Edition, Springer, 2013.

Group	Teacher	Department
Year 1	Carlo Sala	Economía, Finanzas y Contabilidad
Year 1	Antonio Rubia Serrano	Economía, Finanzas y Contabilidad