Online, Italy

Modelling and Forecasting Energy Markets

online course
when 30 August 2021 - 10 September 2021
language English
duration 2 weeks
credits 10 EC
fee EUR 1250

In the last two decades, energy markets operators have witnessed major structural changes that have had a profund impact on how prices are determined on the market. Events like market liberalization, adoption of energy efficiency regulation, increased production from renewable energy sources, and climate change have contributed in making the demand and supply less predictable and the prices more volatile. The accurate modelling and forecasting of energy demand and prices has become of utmost importance, not only to energy producers themselves, but also to commodity traders and financial analysts focusing on the energy sector. The statistical features of energy data, which tends to follow periodic patterns and exhibit spikes, non-constant means and non-constant variances, renders the task of forecasting and modelling of energy data somewhat challenging.

The objective of TStat’s “Modelling and Forecasting Energy Markets” Summer School is therefore to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of both demand and prices in international energy markets.

The programme covers a wide range of econometric methods currently available to researchers and practitioners, such as: i) univariate and multivariate time series models for forecasting prices and demand; ii) univariate and multivariate GARCH models for forecasting price volatility and iii) cointegration models and panel data models for assessing the sensitivity of energy demand to price, income and climate variables and for constructing long-run policy scenarios.

Following TStat’s training philosophy, the teaching style features both theoretical sessions, where participants are given the intuition behind the choice of a specific technique, and several practical sessions using econometric software. In this manner, the 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.

The 2021 edition also includes an extended Case Study Group session during which participants will either work in small groups on a short applied case study or on a presentation of their own research work. Course leaders will discuss with participants the appropriateness of the methods adopted in their case study and the interpretation of the results obtained and will also provide feedback and guidance on possible future developments of individual research agendas.

At the end of the School participants are expected to be in a position to autonomously conduct energy markets analysis. In particular, participants will be able to evaluate which econometric method is more appropriate to the analysis in hand and will be able to test the appropriateness of their estimated model and the robustness of the results obtained.

Target group

Researchers and professionals working either: i) in the energy and related sectors, needing to model energy price and demand, and ii) on trading desks in financial institutions. Economists based in research policy institutions. Students and researchers in engineering, econometrics and finance needing to learn the econometrics methods and tools applied in this field.

Course aim

In the last two decades, energy markets operators have witnessed major structural changes that have had a profund impact on how prices are determined on the market. Events like market liberalization, adoption of energy efficiency regulation, increased production from renewable energy sources, and climate change have contributed in making the demand and supply less predictable and the prices more volatile. The accurate modelling and forecasting of energy demand and prices has become of utmost importance, not only to energy producers themselves, but also to commodity traders and financial analysts focusing on the energy sector. The statistical features of energy data, which tends to follow periodic patterns and exhibit spikes, non-constant means and non-constant variances, renders the task of forecasting and modelling of energy data somewhat challenging.

The objective of TStat’s “Modelling and Forecasting Energy Markets” Summer School is therefore to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of both demand and prices in international energy markets.

Fee info

EUR 1250: The online Summer School fee amounts to:

Full-time Students*: € 1250.00
Full-time PhD Students: € 1490.00
Academic: € 1775.00
Commercial: € 2825.00

*To be eligible for full-time student prices, participants must provide proof of their full-time student status for the current academic year.