ANALYSIS OF MULTIDIMENSIONAL AND TIME SERIES DATA

Academic Year 2024/2025 - Teacher: Luca SCAFFIDI DOMIANELLO

Expected Learning Outcomes

The course presents the most common statistical models used in analysing financial and/or economic time series. Particular attention is given to their practical implementation by using the software R. The students will be able to choose a suitable statistical model for the analysis of economics and/or financial time series.

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.

Multivariate time series: stationarity, Vector autoregressions, Vector ARMA, Estimation

Principal Component Analysis and Factor Models

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	Multivariate time series	Textbook 2) ch. 8
6	Principal Component Analysis and Factor Models	Textbook 2) ch. 9
7	Multivariate Volatility Models	Textbook 2) ch. 10

Learning Assessment

Learning Assessment Procedures

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

VERSIONE IN ITALIANO

Corso di laurea magistrale in

Economia e Politiche Pubbliche