- Docente: Simone Giannerini
- Credits: 8
- SSD: SECS-S/01
- Language: Italian
- Moduli: Simone Giannerini (Modulo 1) Greta Goracci (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Rimini
- Corso: First cycle degree programme (L) in Finance, Insurance and Business (cod. 8872)
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
- Basic definitions and main motivations.
- Introduction to stochastic processes. Linear processes. Autocovariance e autocorrelation.
- ARIMA modelling.
- The Box-Jenkins procedure.
- Introduction to forecasting methods.
- Decomposition and seasonal adjustments.
- Deterministic trends and stochastic trends. Unit root tests and complex dependence.
Modulo 2 - Analysis of financial time series
- Features of financial time series.
- Analysis of financial returns.
- Measuring volatility.
- Models for financial time series
- volatility modelling;
- ARCH/GARCH models; - Forecasting with ARIMA models.
Readings/Bibliography
MODULE 1:
Recommended textbook
- T. Di Fonzo, F. Lisi, Serie storiche economiche, Carocci, Roma, 2015.
MODULE 2:
Recommended textbook
- G. M. Gallo, B. Pacini, Metodi quantitativi per i mercati finanziari, Carocci, Roma, 2013 (VII Ristampa).
Further readings
- R.S. Tsay, Analysis of Financial Time Series, 3rd edition, Wiley, 2010.
Teaching methods
- Lectures.
- Classes.
- Lab sessions with case studies analysed with R.
Assessment methods
MODULE 1:
- A two-hour lab exam with R
MODULE 2:
A two-hour written examination composed of
- Theoretical questions
- Exercises
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It is possible to take separately the exam for each of the two modules.
There are no intermediate exams.
Teaching tools
- Slides of the course
- Slides on time series analysis with R
- Exercises
- Exercises solved with R
The material is available on the IOL platform https://iol.unibo.it/
Office hours
See the website of Simone Giannerini
See the website of Greta Goracci
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.