Advanced Time Series Econometrics #

Topics #

I teach the second part of the course, which covers the formulation, estimation and testing of multivariate and high-dimensional volatility models. We also discuss the use of high-frequency data in realized volatility measurement, and its use in volatility forecasting.

Lecture 1 Slides Lecture 2 Slides

Literature #

Bauwens, L., S. Laurent, and J.V.K. Rombouts (2006), ``Multivariate GARCH models: A survey,’’ Journal of Applied Econometrics 21, 79–109.
Engle, R. F. (2002), ``Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models,’’ Journal of Business & Economic Statistics 20, 339–350.
Ledoit, O. and M. Wolf (2012), ``Nonlinear shrinkage estimation of large-dimensional covariance matrices,’’ The Annals of Statistics, 40, 1024–1060.
Engle, R.F., O. Ledoit, and M. Wolf (2019), ``Large Dynamic Covariance Matrices,’’ Journal of Business & Economic Statistics, 37, 363–375.
Andersen, T.G. and L. Benzoni (2009), ``Realized volatility,’’ pp. 555–570 in T.G. Andersen, R.A. Davis, J.P. Kreiss and T. Mikosch (eds.), Handbook of Financial Time Series. Springer-Verlag.
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,’’ The Review of Economics and Statistics, 89, 701–720.
Hansen, P.R., Z. Huang and H.H. Shek (2012), ``Realized GARCH: A joint model for returns and realized measures of volatility,’’ Journal of Applied Econometrics, 27, 877–906.
Paye, B. S. (2012). ``Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables,’’ Journal of Financial Economics, 106, 527–546.
Bucci, A. (2020). ``Realized volatility forecasting with neural networks,’’ Journal of Financial Econometrics, 18, 502–531.
Other materials on Canvas.