Time series analysis for economic and financial data

Academic Year 2025/2026 - Teacher: LUCA SCAFFIDI DOMIANELLO

Expected Learning Outcomes

Knowledge and understanding: the course presents the most common statistical models used in analysing financial and/or economic time series.

Applying knowledge and understanding: at the end of the course, the student will be able to apply the main techniques adopted in time series analysis and summarise the main features of the analysed datasets.

Making judgements: at the end of the course, the student will be able to select a suitable statistical model, apply it, and perform the analysis using statistical software.

Communication skills: at the end of the course, the student will be able to discuss the obtained results from the statistical analysis, and draw the conclusions.

Learning skills: at the end of the course, the student will be able to understand the structure of time series analysis.

Course Structure

lectures

Required Prerequisites

Basic of statistics, econometrics, and matrix algebra

Attendance of Lessons

In presence

Detailed Course Content

The basic of probability: random experiment; sample space; events; event space; probability; conditional probability;

Random variables: denifinition of a random variable; discrete and continuous random variables; probability density function and cumulative distribution function; expectation of a random variable: mean and variance; bivariate random variables; conditional expectations, law of iterated expectations.

Price Analysis: random walk processes; stochastic trends, unit root tests.

Return analysis: stationarity, white noise, ARMA processes, the autocorrelation function, the partial autocorrelation function, model selection, estimation, and forecasting.

Volatility analysis: Stylized facts, ARCH, GARCH, GJR-GARCH, EGARCH models, estimation and forecasting.

Regime-switching models: Markov chains, Maximum Likelihood Estimates.

Multivariate Volatility Models: Models for Covariances and Correlations, VEC, BEKK, DCC, estimation and forecasting.

Textbook Information

Giampiero M. Gallo, Barbara Pacini "Metodi quantitativi per i mercati finanziari", Carocci Editore (2002).

Ruey S. Tsay, "Analysis of Financial Time Series", Wiley & Sons Inc, (2010).

James D. Hamilton, "Time Series Analysis", Princeton University Press (1994).

Course Planning

	Subjects	Text References
1	Basic of Statistics	Textbook 1) ch. 3
2	Price analysis	Textbook 1) ch. 5
3	Return analysis	Textbook 1) ch. 6
4	Volatility analysis	Textbook 1) ch. 7
5	Regime-switching models	Textbook 3) ch. 22
6	Multivariate volatility models	Textbook 2) ch. 10

Learning Assessment

Learning Assessment Procedures

Written and oral exam. The latter includes a discussion of a project work concerning a real case analysis, using the statistical models covered during the course and the R statistical software.

Examples of frequently asked questions and / or exercises

Describe the stylized facts of financial returns

Describe the ACF of an AR(1) process

Describe the News Impact Curve (NIC) of a GJR-GARCH

VERSIONE IN ITALIANO

Degree Course in

Data Science