CODICE CORSO: I-SS12-OL LINGUA:

IV Summer School | Modelling and Forecasting Energy Markets

In the last two decades, the deregulation of energy markets and the increasing adoption of renewable energy have resulted in significant volatility of both energy price and demand worldwide. The modelling and forecasting of energy demand and price has therefore 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 prices and demand 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 demand and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility and risk management in energy markets.

 

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 Stata. 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 Summer School opens with a full-immersion module on energy data analysis with the statistical software Stata, which aims at developing the necessary practical skills to actively participate in the applied sessions during the course of the week.

 

The 2020 edition also includes an extended Case Study Group session during which participants will either work in small groups on a short applied case study analysis or on a presentation of their own research work using the techniques illustrated during the course of the week. Course leaders will discuss with participants the appropriateness of the methodologies adopted in their case study, the interpretation of the results obtained and also to indicate potential problems to be aware of given the characteristics of the underlying data, as well as providing 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, with the aid of the Stata routines developed specifically for the Summer School. 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.

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 econometrics techniques illustrated in Wooldridg, J. M (2019) and or Brooks. C (2019). More specifically, participants must be familiar with linear regression analysis, inference, regression misspecification issues and time series concepts of autocorrelation, stationarity and volatility.

Participants are not however, required to be familiar with the statistical software Stata.

 

SUGGESTED PRE SCHOOL READING

Financial Econometrics Using Stata, Boffelli, S. e Urga, G. (2016) Stata Press

Introductory Econometrics: A Modern Approach, 7th Edition, Wooldridge, J.M. (2019) Cengage Learning and/or

Introductory Econometrics for Finance, 4th Edition, C.Brooks, (2019) Cambridge University Press

 

 

MODULE 1 | ENERGY DATA ANALYSIS WITH STATA

 

SESSION I: AN INTRODUCTION TO STATA | 31st August 2020

Using Stata interactively and understanding the basics of Stata’s language syntax
Fundamental data management tasks in Stata: importing datasets, renaming and relabelling variables, creating new variables, dealing with string variables, data aggregation
Date and time functions for working with time series in Stata
Saving your work: log files and do files

 

SESSION II: ENERGY DATA ANALYSIS | 1st September 2020

Graphical analysis of energy time series: creating line plots, histograms, correlograms, scatter plots with Stata
Descriptive Statistics in Stata
Test for autocorrelation and heteroscedasticity
Normality test
Non-stationarity and unit root tests

 

MODULE 2 | MODELLING AND FORECASTING ENERGY PRICE AND DEMAND

 

SESSION I: UNIVARIATE TIME SERIES MODELS FOR ENERGY PRICES AND DEMAND (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 2nd September 2020

Univariate time series models for modelling and forecasting energy data (ARMA, ARIMA, ARFIMA, SARIMA)
Markov switching models for capturing stable and spiky regimes in energy prices
Practical applications: estimating and forecasting energy price and demand with univariate models in Stata

 

SESSION II: MULTIVARIATE TIME SERIES MODELS FOR ENERGY PRICES AND DEMAND (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 3rd September 2020

Vector autoregressive (VAR) models for forecasting energy prices and for understanding interdependences between energy markets
Granger predictability of energy prices
Practical applications: fitting VAR models with Stata

 

MODULE 3 | COINTEGRATION AND UNOBSERVED COMPONENT MODELS

 

SESSION I: COINTEGRATION MODELS OF ENERGY DEMAND (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 4th September 2020

An introduction to the theory of cointegration
Cointegration models for energy data: autoregressive distributed lag models and error correction models. The Engle & Granger procedure and the Johansen’s approach
Practical applications: Estimating energy demand models with Stata

 

SESSION II: UNOBSERVED COMPONENT ENERGY MODELS (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 7th September 2020

Unobserved component models to decompose energy demand time series into trend, seasonal, cyclical, and idiosyncratic components
Practical applications: estimating the underlying energy demand trend

 

MODULE 4 | ENERGY MARKETS VOLATILITY 

 

SESSION I: UNIVARIATE GARCH MODELS FOR ESTIMATING AND FORECASTING ENERGY PRICES VOLATILITY (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 8th September 2020

ARCH, GARCH, GARCH-in-mean and IGARCH models for energy prices
Inverse leverage effect in energy markets. Estimating asymmetric GARCH models (SAARCH, EGARCH, GJR, TGARCH, APARCH)
Practical applications: testing for inverse leverage effect in energy markets and fitting symmetric and asymmetric GARCH models with Stata

 

SESSION II: APPLIED CASE STUDY GROUP ANALYSIS | 9th September 2020

During the informal Study Case study session, participants will be encouraged to present their own research agenda. Course leaders will be on hand to discuss the appropriateness of specific methodologies, interpretation and discussion of the results obtained, as well as providing feedback and guidance on possible future developments of individual research agendas.

 

MODULE 5 | RISK MANAGEMENT TOOLS FOR ENERGY MARKETS

 

SESSION I: MULTIVARIATE GARCH MODELS FOR ENERGY PRICES VOLATILITY (ELECTRICITY, CRUDE OIL, NATURAL GAS…) | 10th September 2020

VECH and Diagonal VECH model, Constant Conditional Correlation (CCC) model, Dynamic Conditional Correlation Model (DCC) by Engle (2002) and Dynamic Conditional Correlation Model (DCC) by Tse and Tsui (2002)
Practical applications: testing for interdependencies between energy markets volatility using CCC and DCC models

 

SESSION II: RISK MANAGEMENT TOOLS | 11th September 2020

Value-at-Risk (VaR) to measure market risk of energy markets: Parametric VaR, historical simulation VaR, Monte Carlo VaR
Backtesting procedures: unconditional coverage, independence, conditional coverage, and duration based tests of independence
Practical applications: Value-at-Risk estimation of oil market with Stata

Due to the current COVID-19 situation, the 2020 edition of this Summer school will now be offered ONLINE, on a part-time basis from the 31st August to the 11th September 2020. To this end, this year’s programme has been transformed into a series of module based daily sessions scheduled Monday to Friday from 5.00 pm to 8.00 pm Central European Summer Time (CEST).

 

The informal Case Study Evening Group meeting will take place in the second week of the Summer School on Wednesday the 9th September.

 

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

Professor Giovanni URGA, Centre for Econometric Analysis, Cass Business School, London (UK) and Bergamo University (Italy)

The Summer School fee amounts to:

Students*: € 945.00
Academic: € 1795.00
Commercial: € 2845.00

 

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Residential costs for full time students are completely sponsored by TStat Training through our Investing in Young Researchers Programme. Participation is however restricted to a maximum of 3 students.

 

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 15. Places will be allocated on a first come, first serve basis. The Summer school will only be confirmed when at least 8 people have enrolled.

 

Course fee covers: teaching materials (handouts, databases and the Stata routines developed specifically for the program; a temporary licence of Stata valid for 30 days from the beginning of the school.

 

Individuals interested in attending this Summer School must return their completed registration forms by email (training@tstat.eu) to TStat by the 1st August 2020.


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The objective of TStat’s “Modelling and Forecasting Energy Markets” Summer School is to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of both prices and demand 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 demand and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility and risk management in energy markets.