Very good knowledge of statistics (probability, distribution theory, inference, point and interval estimation), linear algebra, and introductory/intermediate econometrics.
The main aim of the course is to provide students with a solid and rigours knowledge of econometric techniques needed to conduct quantitative analyses involving economic and financial time series. Students will be well equipped to undertake research careers in national (e.g. Bank of Italy,..) and international (e.g. European Central Bank, International Monetary Fund, World Bank, ....) institutions, in the financial industry and in the academia.
-Review of Multiple Linear Regression Model: OLS, GLS, IV;
-Maximum Likelihood (MLE) and Generalised Method of Moments (GMM) Estimation Methods
-Stationary and Non-Stationary Time Series: Unit Root Tests, Cointegration, Two-Step Engle & Granger Approach, Unrestricted Equilibrium Correction Mechanism.
-Univariate and Multivariate Time Series Modelling and Forecasting (ARMA, VAR), and Granger Causality
-Modelling Long-Run Relationship in Economics and Finance: Multivariate Cointegration Analysis (Johansen) and Factor Models;
-Alternative Volatility Measures and Correlation: An Introduction;
-Univariate and Multivariate Volatility Models and Forecasting (GARCH, MGARCH);
-Modelling and Testing for Contagion, Systemic Risk, and (Macro) Announcements in Asset Returns;
- High-Frequency Financial Econometrics: An Introduction (Microstructure Noise, Jumps, Co-jumps, ..);
-Choosing a Numerical Programming Language for Economic and Financial Research: Julia, MATLAB, Python or R (A Gentle Introduction);
-Switching and State-Space Models (A Gentle Introduction) -TBC.
-Long Memory Models and Applications to Credit Risk Models (A Gentle Introduction)- TBC.
Lectures and practical classes will be conducted using OxMetrics8 (PcGive15 and G@RCH8) and Stata17.
The assessment consists of (PRELIMINARY, TO BE CONFIRMED AT THE BEGINNING OF THE COURSE):
a) 30%: Coursework (open-book take-home exercise) with empirical applications using financial time series.
b) 70%: Final written exam (open-book take-home exercise) based on the material in the syllabus and in the coursework.
The course is set up to be delivered in presence. If lectures and classes will be delivered either hybrid or online, the syllabus and the assessment process will be modified accordingly.
Ait-Sahalia, Y. and Jacob, J. (2014), HIGH-FREQUENCY FIANANCIAL ECONOMETRICS, Princeton University Press.
Boffelli, S., and Urga, G. (2016). FINANCIAL ECONOMETRICS USING STATA. Stata Corporation.
Campbell, J.Y., Lo, A.W., and MacKinlay A.C. (1997). THE ECONOMETRICS OF FINANCIAL MARKETS, Princeton University Press.
Fan, J. and Yai, Q. (2017). THE ELEMENTS OF FINANCIAL ECONOMETRICS. Cambridge University Press.
Greene, W. (2020). ECONOMETRIC ANALYSIS. 8th Pearson.
Gourieroux, C. and Jasiak, J. (2001). FINANCIAL ECONOMETRICS, Princeton University Press
Hansen, B. (2022). ECONOMETRICS. Princeton University Press.
Hurn, S., Martin, V.L., Phillips, P.C.B., and Yu, J. (2021). FINANCIAL ECONOMETRIC MODELING. Oxford University Press.
Linton, O. (2019). FINANCIAL ECONOMETRICS. MODELS AND METHODS, Cambridge University Press
Martin, V.L., Hurn, S. and Harris, D. (2013). ECONOMETRIC MODELLING WITH TIME SERIES. Cambridge University Press
Tsay, S. R. (2010). ANALYSIS OF FINANCIAL TIME SERIES, 3rd Edition, Wiley.
Verbeek, M. (2017). A GUIDE TO MODERN ECONOMETRICS. 3rd Wiley