This course provides a presentation of state-of-the-art methodologies for the analysis of volatility, correlations, networks and transmission of financial and macro time series, with applications to systemic risk measurement.
The course begins by introducing GARCH models for the analysis of time-varying volatility and DCC models for time-varying correlations. These time series techniques are then used to construct popular measures of systemic risk, recently proposed in the literature: CoVaR and SRISK. In the last day, instructors will focus on the advanced details of networks, connectedness and transmission. During the sessions, instructors will also introduce the algorithms to deal with large data sets.
The course is divided in theory and practice sessions. Theory classes will introduce the methodology whereas practice session will illustrate the techniques introduced in the course on real datasets.
By participating in this course, you will learn:
State-of-the-art models for forecasting volatility and correlation.
Systemic Risk Measurement.
How to use volatility and correlation models in practice.
Apply time series techniques to the study of connectedness, spillovers, risk and contagion.