COURSE LEADER: ELISABETTA PELLINI, GIOVANNI URGA COURSE ID: D-EF42-OL LANGUAGE:

2022 | 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.

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:

Stationarity

Autocorrelation

Conditional heteroscedasticity

Fat tails

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.

 

COURSE REFERENCES 

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.

Due to the ongoing Public Health situation, the 2022 edition of this training course will be offered ONLINE on a part-time basis. The course program has therefore been restructured into two modules which will be offered on the 10th-11th of March from 10:00 am to 1:30 pm Central European Time (CET).

Students*: € 355.00
Ph.D Students: € 455.00
University: € 505.00
Commercial: € 675.00

*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.

Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.

The number of participants is limited to 8. Places, will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled.

Course fees cover: course materials (handouts and datasets to be used during the course), a temporary licence of Stata valid for 30 days from the beginning of the course.

Individuals interested in attending this training course should contact TStat Training to ask for a registration form. The completed application should then be returned to TStat by the 1st of March 2022.

 


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ONLINE FORMAT

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.

Due to the ongoing Public Health situation, the 2022 edition of this training course will be offered ONLINE on a part-time basis, on the 10th-11th of March.