Online, Italy

Multivariate Garch (Volatility) Models For Risk Management

online course
when 6 October 2022 - 14 October 2022
language English
duration 2 weeks
fee EUR 710

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Multivariate Garch Models for Risk Management course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets. To this end, the course focuses on the modelling and forecasting of financial time series and in particular modelling returns and volatility in asset returns; the modelling of cross market correlations, volatility spillovers and contagion in financial asset markets; and the implementation of both factor models and principal components analysis for the identification of specific asset, country and global factors. The course concludes with an analysis of the available risk management tools/measures widely adopted in academia and the financial sector. During the course, a number of alternative GARCH models, models of conditional correlations, and Value at Risk models will be reviewed.

In common with TStat’s training philosophy, throughout the course the theoretical sessions are reinforced by case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, course leaders are able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed in the course.

Target group

The course is of particular interest to: i) Master and Ph.D. Students and researchers in public and private research centres, and ii) professionals employed in risk management in the following sectors: asset management, exchange rate and market risk analysis, front office and research in investment banking and insurance, needing to acquire the necessary econometric/statistical toolset to independently conduct an empirical analysis of financial risk.

Course aim

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Multivariate Garch Models for Risk Management course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets. To this end, the course focuses on the modelling and forecasting of financial time series and in particular modelling returns and volatility in asset returns; the modelling of cross market correlations, volatility spillovers and contagion in financial asset markets; and the implementation of both factor models and principal components analysis for the identification of specific asset, country and global factors. The course concludes with an analysis of the available risk management tools/measures widely adopted in academia and the financial sector. During the course, a number of alternative GARCH models, models of conditional correlations, and Value at Risk models will be reviewed.

Fee info

EUR 710: Full-time Students*: € 710.00
Ph.D. Students: € 910.00
Academic: € 1010.00
Commercial: € 1350.00



*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status.