Financial Time Series and High Frequency Econometrics
THE COURSE IS DELIVERED IN ONLINE MODE
6-12 September 2020
Alessandra Amendola, University of Salerno
Vincenzo Candila, Sapienza University of Rome
Massimiliano Caporin, University of Padua
Walter Distaso, Imperial College London and University of Messina
Program is conditional to the recruitment of a minimum of 15 participants
Intermediate knowledge of statistical inference and econometrics.
Models for daily returns (based on EOD information):
- Modelling and forecasting the conditional mean of returns (level): ARMA and ARIMA models;
- Modelling and forecasting conditional variance of returns (volatility): GARCH models and their variants;
- Modelling and forecasting conditional covariances and correlations: Multivariate GARCH models.
High frequency econometrics:
- Features of intra-daily data;
- Volatility estimation with Jumps and microstructure noise;
- Volatility modeling and forecasting using high frequency data;
- GARCH type models using realized information (Realized GARCH, HEAVY);
- Dynamic models for realized measures (HAR, MEM);
- Localized (high frequency) regressions;
- Realized covariances and correlations: estimation challenges and dynamic models.
Portfolio construction and optimization:
- Predicting time-varying expected returns; the problem of persistent regressors;
- Estimating and predicting covariance matrices;
- Factor models: linear observable factors specification and estimation;
- Estimating risk premia in cross-section, Fama-McBeth regressions;
- Portfolio optimization.
- Backtesting and Evaluation of volatility forecast;
- Risk measures: VaR, Expected Shortfall; parametric and semiparametric approaches; conditional quantiles and expectiles.
Reference textbooks and suggested readings:
Aït-Sahalia Y., Jacod J. (2014) High-frequency Financial Econometrics, Princeton University Press.
Bauwens L., Hafner C., Laurent S. (2012) Handbook of Volatility Models and Their Applications, Wiley.
Christoffersen P. (2016) Elements of Financial Risk Management, Academic Press.
Francq C., Zakoian J.M. (2010) GARCH Models: Structure, Statistical Inference and Financial Applications, Wiley.
Hautsch N. (2012) Econometrics of Financial High-Frequency Data, Springer.
Linton O. (2019) Financial Econometrics: Models and Methods, Cambridge University Press.
Turan G. Bali, Robert F. Engle, Scott Murray (2016) Empirical Asset Pricing: The Cross Section of Stock Returns, Wiley.
Handouts, further readings and material will be provided before the beginning of the course and during the lectures.
Theoretical lectures are associated with working sessions; during them you will receive the suggestions needed to use an econometric software and to run your own empirical analysis. Data-sets and programming files to make applied econometrics will be provided during the lectures.
For the practical tutorials and applications participants will use the softwares R and Matlab, which will have to be installed on their own laptops.
The Module will last one week.
Lectures and tutorials will be in English, with the following schedule:
Monday to Friday: lectures 9:00-13:00, 15:00-17:00; tutorials and individual hands-on sessions: 17:00-19:00.
Saturday: lectures 9:00-13:00.
Fees and Enrollment
- Students, new graduated students, PhD students and temporary university staff: 340 euro
- University staff: 450 euro
- Others: 1500 euro
In case of enrollment in two or more courses Student and Staff participants are entitled to a discount of 100 euros on each course. Other participants are entitled to a discount of 300 euros on each course.
Application Deadline: July 15th, 2020
Deadline for Fee Payment is August 10th 2020
- For more information: Antonella Mallus e-mail: email@example.com
- For administrative issues : Alessandra Picariello phone:+39 0512092637; e-mail: firstname.lastname@example.org