69062 - Statistical Methods for Financial Markets

Learning outcomes

The course deals with the introduction to financial risk analysis and portfolio management. In its first part, the course covers the basics of time series analysis with particular emphasis on ARIMA modelling and forecasting. The second part deals with the analysis of financial time series.

Course contents

Modulo 1 - Introduction to time series analysis

1. Basic definitions and main motivations.
2. Introduction to stochastic processes. Linear processes. Autocovariance e autocorrelation.
3. ARIMA modelling.
   
4. The Box-Jenkins procedure.
5. Introduction to forecasting methods.
6. Decomposition and seasonal adjustments.
7. Deterministic trends and stochastic trends. Unit root tests and complex dependence.
8. Fundamentals of time series analysis with R

Modulo 2 - Analysis of financial time series

1. Features of financial time series.
2. Analysis of financial returns.
3. Measuring volatility.
   
4. Models for financial time series
   - volatility modelling;
   - ARCH/GARCH models;
5. Forecasting with ARIMA models.
6. Forecasting with ARCH/GARCH models.
   

Readings/Bibliography

MODULE 1:

Recommended textbook

1. T. Di Fonzo, F. Lisi, Serie storiche economiche, Carocci, Roma, 2015.

MODULE 2:

Recommended textbook

1. G. M. Gallo, B. Pacini, Metodi quantitativi per i mercati finanziari, Carocci, Roma, 2013 (VII Ristampa).

FURTHER READINGS

1. R.S. Tsay, Analysis of Financial Time Series, 3rd edition, Wiley, 2010.
2. P. Dalgaard, Introductory Statistics with R, 2008, Springer, ISBN 978-0-387-79053-4.
3. Y. Xie, Dynamic Documents with R and knitr https://yihui.name/knitr/, 2nd Ed., 2015, Chapman & Hall/CRC.
4. Y. Xie, R Markdown: The Definitive Guide: Authoring Books and Technical Documents with R Markdown.

Teaching methods

• Lectures.
• Classes.
• Lab sessions with case studies analysed with R.
  

 

All students must attend Modules 1 and 2 on Health and Safety online

Assessment methods

 

MODULE 1:

A two-hour laboratory exam with R composed of

• exercises of time series analysis with R
• theoretical questions
  

MODULE 2:

Written examination composed of

• theoretical questions
• exercises

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Additional info

• It is possible to take separately the exam for each of the two modules.

• Every module will have its own list on AlmaEsami

• There are no intermediate exams.

Teaching tools

• Slides of the course
• Slides on time series analysis with R
  
• Exercises
• Exercises solved with R

Office hours

See the website of Simone Giannerini

See the website of Gery Andres Diaz Rubio