Coordinator

Juri Marcucci
Bank of Italy
Via Nazionale 91, 00184 Rome, Italy
Email: juri.marcucci@bancaditalia.itjuri.marcucci@gmail.com

 

Lecturers

  • Andrew Patton (Duke University),
  • Kevin Sheppard (Oxford University)

Application Deadline  is   June 24

Deadline for FEE PAYMENT is  July 5

 

Basic Requirements

Intermediate knowledge of econometrics

Course outline

1.      Univariate volatility models
2.      Multivariate volatility models
3.      MV GARCH models, estimation and testing
4.      High frequency data and volatility forecasting
5.      Realized covariance and kernels, vast kernels
6.      Recent developments in forecasting volatility with high frequency data
7.      Composite likelihood and other high dimensional approaches
8.      Semivariances and semicovariances
 
Schedule
 
Session 1A (Patton): Univariate volatility models
Session 1B (Sheppard): Multivariate volatility models
 
Session 2A (Sheppard): More sophisticated MV GARCH models, estimation options
Session 2B (Patton): High frequency data and volatility forecasting
 
Session 3A (Sheppard): Realized covariance and kernels, vast kernels
Session 3B: Group computer assignment session
 
Session 4A (Patton): Recent developments in forecasting volatility with high frequency data
Session 4B (Sheppard): Composite likelihood and other high dimensional approaches
 
Session 5A (Patton): Semivariances and semicovariances
 

References

  • Andersen, T.G., and T. Bollerslev, 1998, Answering the skeptics: yes, standard volatility models do provide accurate forecasts, International Economic Review, 39, 885-905.
  • Andersen, T.G., T. Bollerslev, P.F. Christoffersen, and F.X. Diebold, 2006, Volatility and correlation forecasting. In: G. Elliott, C.W.J. Granger, and A. Timmermann, (Eds.), Handbook of Economic Forecasting. North Holland Press, Amsterdam.
  • Andersen, T.G., T. Bollerslev, and F.X.Diebold, 2007, Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility, Review of Economics and Statistics, 89, 701-720.
  • Andersen, T.G., T. Bollerslev, and F.X. Diebold, 2010, Parametric and nonparametric volatility measurement. In: L.P. Hansen and Y. Aït-Sahalia (Eds.), Handbook of Financial Econometrics. North-Holland Press, Amsterdam.
  • Bollerslev, T., A.J. Patton, and R. Quaedvlieg, 2016, Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting, Journal of Econometrics, 192, 1-18.
  • Bollerslev, T., A.J. Patton, and R. Quaedvlieg, 2017, Realized SemiCovariances: Looking for Signs of Direction Inside the Covariance Matrix, working paper.
  • Bollerslev, T., A.J. Patton, and R. Quaedvlieg, 2017, Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions, working paper.
  • Hansen, P.R., and A. Lunde, 2006, Realized variance and market microstructure noise, Journal of Business and Economic Statistics, 24, 127-161.
  • Patton, A.J., 2011, Volatility Forecast Comparison using Imperfect Volatility Proxies, Journal of Econometrics, 160(1), 246-256.
  • Patton, A.J., and K. Sheppard, 2015, Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility, Review of Economics and Statistics, 97(3), 683-697.

Software

Participants will use their laptops with R, Matlab, or Pyton already installed on them.

Venue

The Module will be held in the Bank of Italy's Scuola di Automazione per Dirigenti Bancari (SADiBa), via San Marco n.54, Perugia. Participants will be accommodated at SADiBa.

Fees and Enrollment

  •  Students:850€ 
  • University staff: 1000€ 
  •  Others: 2500€

Fee includes: Fee includes full board accommodation  starting from Sunday.

Contacts