Time Series Methods for Risk Analysis

Learn with our expert faculty

facultyLuca Gambetti
PhD, Universitat Pompeu Fabra
UAB and BSE

Director
facultyChristian Brownlees
PhD, University of Florence
UPF and BSE

Instructor

Course overview

The Barcelona School of Economics Intensive Course on ​​Time Series Methods for Risk Analysis provides an introduction to state-of-the-art techniques for the analysis of risk in finance and macroeconomics. 

The first part of the course introduces univariate time series models used for the analysis of time-varying volatility (GARCH models); multivariate time series models for the analysis of time-varying correlations (DCC models) as well as quantile regression for the analysis of downside risk. 

The second part of the course presents empirical applications. In the first application, GARCH-DCC models are used to construct a number of systemic risk measures recently proposed in the literature (CoVaR, MES, SRISK). In the second application GARCH models and quantile regression are used for Growth-at-Risk forecasting. 

The course includes theory sessions (10 hours) and practical sessions (10 hours). During the practice sessions, students will use MATLAB to replicate the methodology as well as the empirical findings documented in the lectures.

INTENSIVE COURSE

Time Series Methods for Risk Analysis

Applications will open soon!
  ONLINE
Regular Fee 1200 €
Reduced Fee 720 €

10% early-bird discount applies to payments made on or before January 14, 2022 at 23:59 (CET)

See below for reduced fee eligibility


Early-bird payment deadline: January 14, 2022

  ONLINE
Regular Fee 1200 €
Reduced Fee 720 €

10% early-bird discount applies to payments made on or before January 14, 2022 at 23:59 (CET)

See below for reduced fee eligibility


Last day to apply: February 21, 2022

  ONLINE
Regular Fee 1200 €
Reduced Fee 720 €

10% early-bird discount applies to payments made on or before January 14, 2022 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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