Over the past two decades several countries around the world have liberalized their energy markets, with the result that the risk for utilities, energy producers and commodity market operators has increased substantially.
In this new environment, accurate modelling and forecasting of energy demand and prices has become of key importance for all market players to plan their short- and long-term operations.
This course provides a review of and a practical guide to several major econometric methodologies to modelling the stylised facts of the energy prices and demand time series, via regression and cointegration analysis, univariate and multivariate GARCH models. Practical demonstrations will be conducted using Stata.
Day 1: Modelling the Conditional Mean of Energy Demands and Prices Series
Session 1 & 2:
- Introduction to energy time series features: distributional properties, stationarity, seasonality, autocorrelation, heteroscedasticity.
- Univariate models of conditional mean (MA, AR, ARMA, ARIMA, ARMAX). Analysis of the properties and practical applications of identification and diagnostic checking of ARMA models.
- Forecasting energy prices with ARMA models.
Session 3 & 4:
- Vector Autoregressive (VAR) models. Analysis of the properties and practical applications of identification and diagnostic checking of VAR models.
- Long-run relationships in energy: applying cointegration analysis to model fuels demand.
Day 2: Modelling the Volatility of Energy Prices Series
Sessions 1 & 2:
- Characteristics of energy prices volatility.
- ARCH and GARCH models, Integrated GARCH model, GARCH in mean. Asymmetric GARCH models: SAARCH, EGARCH, GJR, TGARCH, APARCH. Estimating the news impact curve.
- Forecasting energy prices volatility with GARCH models
Sessions 3 & 4:
- Multivariate GARCH models: Diagonal VECH, Constant Conditional Correlation, Dynamic Conditional Correlation models. Analysis of the properties and practical applications of identification and diagnostic checking of MGARCH models.
- Forecasting energy markets correlations with MGARCH models.
40% Theory, 30% Demonstration and 30% Practical
- S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata. Stata Press Publication.
- R.S. Tsay (2010), Analysis of Financial Time Series. Wiley & Sons.
Pre-course Suggested Reading
- Several academic papers will be suggested during the course to complement the syllabus.
09:00 - 09:20 Registration
09:30 - 11:00 Session 1
11:00 - 11:15 Tea / coffee break
11:15 - 12:45 Session 2
12:45 - 13:45 Lunch
13:45 - 15:15 Session 3
15:15 - 15:30 Tea / coffee break (Feedback Session)
15:30 - 17:00 Session 4
Basic knowledge of statistics and econometrics is assumed as well as a working interest in modelling energy prices.
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Cost includes course materials, lunch and refreshments.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, we can also provide laptops for a small daily charge).
- If you need assistance in locating hotel accommodation, please notify us at the time of booking.
- Payment of course fees required prior to the course start date.
- Registration closes 5-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.