ECONOMETRICS FOR FINANCIAL MARKETS

Course objectives

The course aims at introducing students to the theory, the techniques and the advanced practice of econometric analysis in economics and in finance, with special regard to: measuring non observable economic variables, verifying economic theories, forecasting future pattern of real economic and financial variables, evaluating the effects of macro and micro-economic policies. Furthermore, the course will also address some situations where the assumptions of the Ordinary Least Squares estimator do not hold and thus classical regression analysis is not applicable. Particular attention will be paid to models for limited dependent variables and to Vector Autoregressive (VAR) models. Concepts will be presented by means of an extensive use of examples with real economic and financial data, in order to achieve a better understanding of the theoretical concepts and to introduce the students to the practice of computationally-based estimation and validation techniques. Students who pass the exam will know and understand the basic instruments of econometrics and their use to solve empirical economic and financial problems. The discussion of case-studies will make students able to apply econometric tools to answer economic and financial questions of interest. Students will be able to autonomously conduct and comment empirical analyses, also evaluating possible violations of the basic assumptions. Finally, students will be able to interpret and present the results coming from the estimated models, both in oral and in written form.

Channel 1
STEFANO SIMONE GALIANI Lecturers' profile

Program - Frequency - Exams

Course program
- Panoramica degli strumenti di analisi dei dati con Python e R e relativi concetti di programmazione. - Introduzione ai moduli Numpy, Pandas series e Scipy per l'analisi di serie e indicatori di dati finanziari. - Introduzione ai modelli classici di regressione lineare, ipotesi e test diagnostici. - Modellazione e previsione di serie temporali univariate: Processi a media mobile, Processi autoregressivi, funzione di autocorrelazione parziale, processi ARMA; Esempi di modelli di serie temporali in finanza. - Modellazione delle relazioni in finanza: stazionarietà e unit root testing; Test per le radici unitarie; Cointegrazione. - Modellazione della volatilità: modelli di volatilità storica e volatilità implicita. Tecniche di estrazione della distribuzione implicita dei rendimenti attesi. - Modellazione della covarianza in finanza tramite le funzioni Copula. Misure multivariate di gestione del rischio ed interpretazione. - Introduzione al Machine Learning applicato ai mercati finanziari: Regressione lineare, Classificazione, Random Forests, Support Vector Machines. Applicazione in Python tramite la libreria SKLearn applicata ai prezzi degli immobili, ai dati dei prestiti bancari e pool di transazioni con carta di credito.
Prerequisites
Statistics course covering probability theory and statistical inference. Financial Mathematics concepts pertinent to contingent claims. Basic programming in any language, although the first part of the course will provide students with a solid foundations of relevant Python and R concepts.
Books
Several papers and programming notebooks will be provided directly by the lecturer. Brooks, C. (2019). Introductory Econometrics for Finance (4th ed.), Cambridge University Press. Kelliher, C. (2022) Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering, Routledge Cherubini, U. et al. Copula Methods in Finance (2004), Wiley Finance Hilpisch, Y., Listed Volatility and Variance Derivatives: A Python-based Guide (2016), Wiley Finance
Frequency
- Lectures - Individual assigments - Group Assignments - programming tutorials
Exam mode
The exam consists in two parts: - the first one includes review questions to test theoretical knowledge and critical understanding; - the second one consists in a series of assignments covering empirical aspects and it is aimed at testing applied skills. Each assignment is carried out in groups during the semester and foresees a periodic formal (group) presentations about methods adopted and models developed.
Lesson mode
Frontal lectures, practical exercises in lab and group project
  • Lesson code10592800
  • Academic year2025/2026
  • CourseFinance and insurance
  • CurriculumFinancial risk and data analysis - in lingua inglese
  • Year2nd year
  • Semester1st semester
  • SSDSECS-P/05
  • CFU12