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.
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
Analysis of the features of energy prices and returns:
Univariate time series models for forecasting energy prices and returns (ARMA, ARIMA, SARIMA);
Vector autoregressive (VAR) models for forecasting energy prices/returns and for understanding interdependences between energy markets.
SESSION II: MODELS FOR ENERGY PRICES VOLATILITY
Univariate GARCH model for forecasting energy markets volatility. Modelling leverage effect and inverse leverage effect with asymmetric GARCH models (EGARCH, TGARCH, GJR-GARCH, APARCH).
Modelling cross-markets correlations and testing for volatility spillovers with MGARCH models: Diagonal VECH (DVECH), Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC) models.
SUGGESTED READING (PRE – AND POST-COURSE)
Introductory Econometrics for Finance. Brooks, C., (2019). Cambridge University Press, 4th edition.
Boffelli, S., and Urga, G.,(2016). Financial Econometrics Using Stata. Stata Press Publication, StataCorp LP, College Station, Texas.
We are currently putting the finishing touches to our 2023 training calendar. We therefore ask that you re-visit our website periodically or contact us at firstname.lastname@example.org should the dates for the course which you are interested in following not yet be published. You will then be contacted via email as soon as the dates are available.
Dr Elisabetta Pellini, Centre for Econometric Analysis, Bayes Business School, City, University of London (UK). Professor Giovanni Urga, Centre for Econometric Analysis, Bayes Business School, City, University of London (UK).
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.