Training and Research

PhD Programme Courses/classes

Mathematical Statistics

Credits: 5

Language: English

Teacher:  Catia Scricciolo

Microeconomics 1

Credits: 7.5

Language: English

Teacher:  Simona Fiore, Claudio Zoli, Martina Menon

Continuous Time Econometrics

Credits: 5

Language: English

Teacher:  Cecilia Mancini

Probability

Credits: 7.5

Language: English

Teacher:  Marco Minozzo

Macroeconomics I

Credits: 7.5

Language: English

Teacher:  Tamara Fioroni, Alessia Campolmi

Game Theory

Credits: 5

Language: English

Teacher:  Francesco De Sinopoli

Mathematics

Credits: 4.5

Language: English

Teacher:  Andrea Mazzon, Jonathan Yick Yeung Tam

Advice to Young Economists

Credits: 4

Language: English

Teacher:  Marco Piovesan

Stochastic Optimization and Control

Credits: 5

Language: English

Teacher:  Athena Picarelli

Financial Time Series

Credits: 5

Language: English

Teacher:  Giuseppe Buccheri, Francesca Rossi

Mean Field Games (part I)

Credits: 2.5

Language: English

Teacher:  Luciano Campi

Job Market Orientation

Credits: 1

Language: English

Teacher:  Joan Madia, Simone Quercia

Discretization of Processes

Credits: 4.5

Language: English

Teacher:  Jean Jacod

Topics in applied economics with administrative data

Credits: 1

Language: English

Teacher:  Edoardo Di Porto

Multivariate Analysis with Latent Variables: The SEM Approach

Credits: 3

Language: English

Teacher:  Albert Satorra

Finanza Matematica

Credits: 5

Language: English

Teacher:  Alessandro Gnoatto

Political Economy

Credits: 4

Language: English

Teacher:  Emanuele Bracco, Roberto Ricciuti

Finite Mixture Models in Health Economics: Theory and Applications

Credits: 1

Language: English

Teacher:  Paolo Li Donni

Inequality

Credits: 4

Language: English

Teacher:  Francesco Andreoli, Claudio Zoli

Behavioral and Experimental Economics

Credits: 4

Language: English

Teacher:  Simone Quercia, Maria Vittoria Levati, Marco Piovesan

Health Economics

Credits: 4

Language: English

Teacher:  Paolo Pertile

Development economics

Credits: 4

Language: English

Teacher:  Federico Perali

Finance

Credits: 4

Language: English

Teacher:  Giorgio Vocalelli

Mean Field Games (part II)

Credits: 2.5

Language: English

Teacher:  Giulia Liveri

Stochastic Processes in Finance

Credits: 5

Language: English

Teacher:  Sara Svaluto-Ferro, Christa Cuchiero

Dynamic Corporate Finance

Credits: 2

Language: Englìsh

Academic staff

Giuseppe Buccheri,

Credits

5

Language

English

Class attendance

Free Choice

Location

VERONA

Learning objectives

This is a graduate course on recent topics in Financial Time Series.\ In the first part of the course students will familiarize with the fundamental notions of time series analysis, with a particular emphasis on ARMA models.\ In the second part, some recent advances in financial time series models and volatility forecasting will be presented.

Prerequisites and basic notions

Students are supposed to posses a basic knowledge of calculus and linear algebra. A basic knowledge of a scientific computing software (Matlab, Python) is also required.

Program

Part 1
Lecturer: Francesca Rossi (10 hours)
- Introduction to time series; review of univariate statistics; tests for serial correlation; review of multivariate statistics; conditional distributions; the Markov property.
- Weak and strong stationarity; examples of autocorrelation structures; AR(1) and AR(2) models; MA(1) and MA(2) models; ARMA(1,1) models; ARMA(p,q) models; Wold decomposition.
- Law of Large Numbers and Central Limit Theorem for dependent data; estimation via Yule-Walker equations; OLS estimation; Maximum Likelihood estimation; Conditional Maximum Likelihood principle; Information Criteria.
Part 2
Lecturer: Giuseppe Buccheri (10 hours)
- Introductory topics. GARCH-type models, stochastic volatility models, Kalman filter. Cox classification of parameter-driven versus observation-driven models.
- Score-driven models as observation-driven models. Univariate score-driven volatility models based on Student-t and GED distributions. Scaling factors and link functions. Stationarity and ergodicity.
- DCC and dynamic correlation models based on the Student-t distribution. ``DRD" decomposition of the covariance matrix, (un)identifiability of static parameters, hyperspherical coordinates. Comparison with DCC.
- Realized measures. Univariate and multivariate score-driven models for realized measures. Estimation errors and curse-of-dimensionality. Two-step approaches and comparison with HAR-DRD.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Lectures

Learning assessment procedures

The final exam consists of a written examination covering the topics of Part 1, and the analysis of a scientific article related to Part 2 of the course. The analysis entails the elaboration and modeling of time-series data and writing a report.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

The final evaluation is based on the exam grade.

Criteria for the composition of the final grade

The final grade is the average of the two grades achieved in Part 1 and Part 2.

Scheduled Lessons

When Classroom Teacher topics
Tuesday 30 January 2024
10:00 - 12:00
Duration: 2:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Francesca Rossi Financial Time Series
Thursday 01 February 2024
14:00 - 17:00
Duration: 3:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Francesca Rossi Financial Time Series
Tuesday 06 February 2024
10:00 - 12:00
Duration: 2:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Francesca Rossi Financial Time Series
Thursday 08 February 2024
14:00 - 17:00
Duration: 3:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Francesca Rossi Financial Time Series
Monday 12 February 2024
11:00 - 13:00
Duration: 2:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Giuseppe Buccheri Financial Time Series
Wednesday 14 February 2024
14:00 - 17:00
Duration: 3:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Giuseppe Buccheri Financial Time Series
Monday 19 February 2024
11:00 - 13:00
Duration: 2:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Giuseppe Buccheri Financial Time Series
Wednesday 21 February 2024
14:00 - 17:00
Duration: 3:00 AM
Polo Santa Marta - SMT.07 [SMT.7 - terra] Giuseppe Buccheri Financial Time Series