Macro and econometrics for finance
A.A. 2025/2026
Obiettivi formativi
This course introduces to the methods and practices generally used in the analysis of economic and financial data. We will cover both univariate and multivariate models for stationary and non-stationary time series. The aim of the course is twofold: first to develop a comprehensive set of tools and techniques for analysing various forms of univariate and multivariate time series, and second to acquire knowledge of recent changes in the methodology of econometric analysis of time series.
Risultati apprendimento attesi
At the end of the course students will be able to analyse macroeconomic and financial time series and use them in econometric models. Specifically, students will be familiar with univariate statistical techniques generally used to study the dynamics of a time series, like ARMA and ARIMA models, and the dynamics of their conditional variance, like ARCH and GARCH models. They will be also able to deal with linear regression models using stationary and non-stationary time series, as in the case of cointegration. Finally, students will be familiar with recent methodologies concerning multiequational models, like VARs and Structural VARs, and with Dimension Reduction Techniques. They will be able to specify and estimate the unknown parameters of the equations and use them to investigate about the dynamic and causal impact of macroeconomic and financial shocks on the endogenous variables of the model, and in the construction of simple forecasting models.
Periodo: Primo trimestre
Corso singolo
Questo insegnamento può essere seguito come corso singolo.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Primo trimestre
Prerequisiti
La lingua di erogazione del corso e' inglese
Prerequisites:
differential calculus
basic statistical theory
Prerequisites:
differential calculus
basic statistical theory
Modalità di verifica dell’apprendimento e criteri di valutazione
La lingua di erogazione del corso e' inglese
Macrofinance and Financial Econometrics are assessed separately.
The final mark is the average of both marks.
Financial Econometrics is assessed by means of a 90 minutes written exam only. Only for the December 2025 Assessment, it is possible to complement the Financial Econometrics exam with a Mini-Project.
Macrofinance and Financial Econometrics are assessed separately.
The final mark is the average of both marks.
Financial Econometrics is assessed by means of a 90 minutes written exam only. Only for the December 2025 Assessment, it is possible to complement the Financial Econometrics exam with a Mini-Project.
Module II, Financial Econometrics
Programma
Topics will include:
Definition of univariate time series.
Ergodicity and stationarity / mixing as generalisation of the iid framework.
Inference for stationary and ergodic / mixing processes.
Forecasting for stationary processes and forecast evaluation.
Parametric modelling of weakly dependent univariate time series (ARMA modelling). Impulse response functions for ARMA. Inference and forecasting in ARMA modelling.
Model selection: parsimonious modelling.
Inference for unit root series. Forecasting with unit roots.
Unit root testing.
Multivariate modelling: VARMA and VAR modelling for weakly autocorrelated time series. Impulse response function for VARs.
Identification and estimation in VAR models.
Regression models for weakly dependent and unit root multivariate time series
Cointegration.
Error Correction Models VECM
Forecasting for multivariate time series.
Large dimensional weakly dependent time series: estimation and forecasting of factor models.
Autocorrelation Conditional Heteroskedasticity
Value at Risk
Applications of time series in macroeconomics: The Efficient Market Hypothesis, The Expectation model for a vector of interest rates.
Definition of univariate time series.
Ergodicity and stationarity / mixing as generalisation of the iid framework.
Inference for stationary and ergodic / mixing processes.
Forecasting for stationary processes and forecast evaluation.
Parametric modelling of weakly dependent univariate time series (ARMA modelling). Impulse response functions for ARMA. Inference and forecasting in ARMA modelling.
Model selection: parsimonious modelling.
Inference for unit root series. Forecasting with unit roots.
Unit root testing.
Multivariate modelling: VARMA and VAR modelling for weakly autocorrelated time series. Impulse response function for VARs.
Identification and estimation in VAR models.
Regression models for weakly dependent and unit root multivariate time series
Cointegration.
Error Correction Models VECM
Forecasting for multivariate time series.
Large dimensional weakly dependent time series: estimation and forecasting of factor models.
Autocorrelation Conditional Heteroskedasticity
Value at Risk
Applications of time series in macroeconomics: The Efficient Market Hypothesis, The Expectation model for a vector of interest rates.
Metodi didattici
20 Two-hour lectures
Materiale di riferimento
The main reference textbook for the course is:
Time Series Analysis, by J. D. Hamilton, 1994, Princeton University Press. Other main references may be mentioned as the course progresses.
Time Series Analysis, by J. D. Hamilton, 1994, Princeton University Press. Other main references may be mentioned as the course progresses.
Moduli o unità didattiche
Module I, Macrofinance
SECS-P/01 - ECONOMIA POLITICA - CFU: 6
Lezioni: 40 ore
Docente:
Cinquemani Giuseppe
Module II, Financial Econometrics
SECS-P/05 - ECONOMETRIA - CFU: 6
Lezioni: 40 ore
Docente:
Iacone Fabrizio
Docente/i
Ricevimento:
Wednesday, 11AM to 1PM. Please email me to arrange an appointment
Stanza 4 (DEMM Secondo piano)