CODICE CORSO: I-SS12 LINGUA: RESIDENZIALE

Modelling Energy Markets using Stata

The trend in deregulation in energy markets worldwide has resulted in significant volatility, both in terms of price and demand, in international energy markets. The modelling and forecasting of both demand and pricing has therefore become of utmost importance, not only to energy producers themselves, but to commodity traders and financial analysts focusing on the energy sector. Moreover, the specific nature of energy data itself, which tends to follow periodic patterns and exhibit non-constant means and variances, has resulted in the task of forecasting and modelling of energy data becoming somewhat challenging.

 

The objective of our “Modelling Energy Markets in Stata” Summer School is therefore, to provide participants with the requisite toolset, both theoretical and applied, to enable them to correctly implement the appropriate statistical tools required for the modelling of both demand and prices in international energy markets. As such, the program has been developed to illustrate the range of available statistical tools currently available to researchers and practitioners, encompassing both: i) the more traditional univariate and multivariate time series regression approach to the modelling of price and demand in energy markets, focusing on the distributional properties, stationarity, seasonality and autocorrelated characteristics of energy time series data; and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility and risk management in energy markets.

 

Throughout the course of the week, theoretical sessions are reinforced by case study examples, in which the course tutors discuss 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.

 

The summer school opens with an optional one-day introduction to Stata course to enable participants unfamiliar with the statistical software Stata to acquire the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata. The course covers everything from the very basics, in order to get one up and running in Stata, to an overview of the available Stata commands for preliminary data analysis, data management, importing and exporting data formats.

Researchers and professionals working either: i) on trading desks in financial institutions or ii) in the energy and related sectors, needing to model energy pricing. Economists based in financial institutions. Students and researchers in engineering, econometrics and finance needing to learn the statistical tools and methodologies applied in this field.

Introductory knowledge of econometrics and/or statistics.

MODULE A: AN INTRODUCTION TO STATA

 

SESSION I: INTRODUCTION – GETTING STARTED

Stata’s GUI
File types in Stata
Working interactively in Stata

Organizing one’s work in Stata
Help
Web resources in Stata: dowloading updates and new commands via internet

Saving output: the log file
Interrupting Stata
Loading Stata databases
The Log Output File
Saving databases in Stata
Exiting the software

 

SESSION II: PRELIMINARY DATA ANALYSIS

A preliminary look at the data: describe, summarize commands
Abbreviations in Stata
Stata’s syntax

Constrained command

Summary statistics
Statistical Tables: table, tabstat and tabulate commands

 

SESSION III: DATA MANAGEMENT

Renaming variables
Selecting or eliminating variables
The count command
sort command
Creating sub-groups: the prefix by
Creating new variables: generate
Operators in Stata
The command assert
Missing values in Stata
Modifying variables: replace, recode
Creating Labels: variable labels and value labels
Creating dummy variables

 

SESSION IV: IMPORTING DATA FROM SPREADSHEETS

Import Excel and Export Excel commands
The insheet and outsheet commands
Reading in Text Data Files
Issues to watch out for when importing data

Missing values
String variables
Date variables

Redefining missing values
destring command
tostring command
dealing wih “messy” strings

 

MODULE B: THE ECONOMETRICS OF ENERGY MARKETS USING STATA

 

SESSION I: UNIVARIATE TIME SERIES MODELS FOR ENERGY PRICES AND DEMANDS (ELECTRICITY, CRUDE OIL, NATURAL GAS…)

Analysis the features of energy time series: seasonality, normality, stationarity and unit root tests, autocorrelation, heteroscedasticity, spikes.

Application

Data analysis of energy time series in Stata

Univariate time series models for energy data (AR, MA, ARMA, ARIMA, ARFIMA, SARIMA)
Markov switching models for capturing stable and turbulent regimes in energy prices

Application

Estimating and forecasting energy prices and demands with univariate models in Stata

 

SESSION II: MULTIVARIATE MODELS FOR ENERGY PRICES AND DEMANDS (ELECTRICITY, CRUDE OIL, NATURAL GAS…)

Vector Autoregressive models to model interdependencies between stationary energy prices.

Application

Fitting a VAR model with Stata

Cointegration theory. Autoregressive distributed lag models and error correction models. The Engle&Granger procedure and the Johansen’s approach

Application

Cointegration techniques to model energy demand with Stata

 

MODULE C: FORECASTING ENERGY MARKETS VOLATILY USING STATA

 

SESSION I: UNIVARIATE GARCH MODELS FOR ESTIMATING AND FORECASTING ENERGY PRICES VOLATILY (ELECTRICITY, CRUDE OIL, NATURAL GAS)

Volatility definition and features
ARCH, GARCH, GARCH-in-mean and IGARCH models
Application

Analysing energy prices volatility and fitting ARCH and GARCH models with Stata

Inverse leverage effect in energy markets. Estimating asymmetric GARCH models (SAARCH, EGARCH, GJR, TGARCH, APARCH). News impact curve
Alternative GARCH specifications: Power ARCH, Non-linear GARCH models

Application

Testing for inverse leverage effect in energy markets and fitting asymmetric GARCH models with Stata

 

SESSION II: MULTIVARIATE GARCH MODELS FOR ENERGY PRICES VOLATILITY AND RISK. MANAGEMENT TECHNICQUES

Diagonal VECH, Constant Conditional Correlation, Dynamic Conditional Correlation models

Application

Testing for interdependencies between energy markets using Stata

Value-at-Risk to measure market risk of energy markets. Parametric model, historical simulation, Monte Carlo simulation

Application

Value at Risk estimation of oil markets with Stata

The residential Summer School will be held in Florence from the 18th to 22nd of September 2017.

 

LOCATION: Villa La Stella ♦ Via Jacopone da Todi, 12 ♦ 50133 Florence

 

Una-Louise BELL, TStat S.r.l.

Elisabetta PELLINI, Cass Business School

Giovanni URGA, Cass Business School

 

PARTICIPATION FOR THE ENTIRE WEEK (Modules A, B and C – 5 days)

Students*: € 1030.00
Academic: € 1608.00
Government / Nonprofit: € 1814.00
Commercial: € 2020.00

 

MODULES A and B (3 days)
Students*: € 729.00
Academic: € 1126.00
Government / Nonprofit: € 1272.00
Commercial: € 1418.00

 

MODULE B or C (2 days)
Students*: € 484.00
Academic: € 766.00
Government / Nonprofit: € 862.00
Commercial: € 958.00

 

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.

 

All fees are subject to VAT (applied at the current Italian rate of 22%).

 

Please note that a non-refundable deposit of €100.00 for students and €200.00 for academic, government/nonprofit and commercial participants, is required to secure a place and is payable upon registration. The number of participants is limited to 20. Places will be allocated on a first come, first serve basis.

 

Course fees cover: i) teaching materials (copies of lecture slides, databases and Stata routines used during the workshop); ii) a temporary licence of Stata valid for 30 days from the beginning of the workshop; iii) half board accommodation (breakfast, lunch and coffee breaks) in a single room at Villa La Stella (4 nights for entire week, 2 nights for Modules A and B, 1 night for Module B or Module C). Participants requiring accommodation the day before the course beginning or the night of the final day of the school, are requested to contact us as soon as possible.

In order to maximize the usefulness of this workshop, we recommend that participants bring their own laptops with them, to be able to actively participate in the empirical sessions.

 

Individuals interested in attending this summer school must return their completed registration forms either by email (training@tstat.eu) or by fax (+39 0864 206014) to TStat by the 3rd of September 2017.


L’iscrizione al corso dovrà avvenire tramite lo specifico modulo di registrazione e pervenire a TStat S.r.l. almeno 15 giorni prima dell’inizio del corso stesso. E’ possibile richiedere il modulo di registrazione compilando il seguente form oppure inviando una mail a formazione@tstat.it


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Autorizzo il trattamento dei dati personali ai sensi dell'articolo 13 del D.lgs. n.196/2003 - Testo completo



The trend in deregulation in energy markets worldwide has resulted in significant volatility, both in terms of price and demand, in international energy markets. The modelling and forecasting of both demand and pricing has therefore become of utmost importance, not only to energy producers themselves, but to commodity traders and financial analysts focusing on the energy sector. Moreover, the specific nature of energy data itself, which tends to follow periodic patterns and exhibit  non-constant means and variances, has resulted in the task of forecasting and modelling of energy data becoming somewhat challenging.