ERASMUS+ Course on “Applied Time Series Analysis for Volatility Modeling” (27-30 Apr 2026)
Course Description
You are using statistics at an advanced level, are familiar with R, and now have a dataset containing time series such as climate data, growth data, or stock market prices. If so, this course will help you to develop a more professional understanding of time series and the methods applicable to their analysis.
This course provides an introduction to time series analysis, focusing on Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models. These models are used to model and forecast time-varying volatility in time series where uncertainty evolves over time. This course is not only relevant for general data analysis, but also for evaluating energy markets and climate policy because both of these fields exhibit strong volatility clustering and shock persistence. The course includes estimation and forecasting, combining theoretical sessions with practical applications using real data and academic papers. Students will examine volatility clustering, shock persistence, volatility spillovers and volatility forecasting in order to analyse the impact of geopolitical shocks, supply chain disruptions, changes in demand, regulatory changes and weather-related events.
Target Group
This course is suitable for PhD students who are working (or wish to work) with statistical methods, including those involving financial, energy and climate data. Your statistical skills would be at an advanced level and you have at least basic knowledge on time series analysis and R. There are no admission requirements, but a basic knowledge of statistics is recommended.
Course Dates and Schedule
The course will be held from Monday, 27 April 2026, to Thursday, 30 April 2026, with daily sessions from 9:00 to 13:00h.
More information about the course content, schedule, tools, evaluation & credits etc. as well as how to register for the course can be found here.