Forecasting Energy Prices and Volatility with Stata

The modelling and forecasting of energy prices and volatility has become of utmost importance in the current turbulent times. 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 energy prices somewhat challenging.

The objective of TStat’s “Forecasting Energy Prices and Volatility with Stata” course is to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of 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 to estimate and forecast prices and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility.

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.

This course is of particular interest to 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.

Participants should have a knowledge of the inferential statistics and introductory econometric methods illustrated in Brooks (2019).

This module aims to introduce Stata, so participants do not need to possess any previous knowledge of the software.

SESSION I: MODELS FOR ENERGY PRICES AND RETURNS

1. Analysis of the features of energy prices and returns:
   • Stationarity
   • Autocorrelation
   • Conditional heteroscedasticity
   • Fat tails
2. Univariate time series models for forecasting energy prices and returns (ARMA, ARIMA, SARIMA);
3. Vector autoregressive (VAR) models for forecasting energy prices/returns and for understanding interdependences between energy markets.

We are currently adding the finishing touches to our 2024 training calendar. We therefore ask you to check our website regularly or contact us at training@tstat.eu should the dates for the course you are interested in not be published yet. You will then be contacted via email as soon as the dates are available.

Dr Elisabetta PELLINI, Centre for Econometric Analysis, Bayes Business School (formerly Cass), London (UK).