2018/2019

2019/2020

DM270

SECS-P/05 (ECONOMETRIA)

DIPARTIMENTO DI SCIENZE ECONOMICHE E AZIENDALI

ECONOMICS, FINANCE AND INTERNATIONAL INTEGRATION - ECONOMIA, FINANZA E INTEGRAZIONE INTERNAZIONALE

Finance

2°

Primo Semestre (23/09/2019 - 21/12/2019)

9

66 ore di attività frontale

English

SCRITTO

ROSSI EDUARDO (titolare) - 7 CFU
SANTUCCI DE MAGISTRIS PAOLO - 2 CFU

The course is meant to deepen the technical knowledge of the econometric mehods used in the analysis of financial markets. Neccessary prerequisites are econometrics, statistics and mathematical finance.

The objective of this course is to provide a comprehensive and systematic account of financial econometric models and their applications to modeling and prediction of financial time series data, focusing on asset returns and volatilities. The students will learn the analytical tools needed for the specification and estimation of econometric models with financial data. Students at the end of the course will have a working knowledge of financial time series data and gain expertise in the software to conduct the analyses.

Introduction to MATLAB

1.Finite difference equations. Solutions and stability. Stationarity and
ergodicity
2. ARMA models: Stationarity, invertibility, forecasting
3. Maximum likelihood estimation of ARMA models
4. VAR: representation and estimation
5. Stochastic trends and deterministic trends. Unit root testing

2. Empirical asset pricing models: Generalized method of moments (GMM)

3. Volatility of financial returns: models, estimation, forecasting
(a) Introduction
(b) Univariate GARCH models (T, 8,9,10)
(c) Multivariate GARCH models
(d) Stochastic volatility models
(e) Nonparametric estimation of volatility with high-frequency data

Frontal lessons

Hamilton J. (1994), Time Series Analysis, Princenton University Press.
Taylor S.J. (2005) Asset Prices Dynamics, volatility, and prediction, Princenton University Press.
Singleton K. (2006) Empirical Dynamic Asset Pricing, Princenton University Press.

Written exam

Written exam