Training and Research
PhD Programme Courses/classes - 2019/2020
Lezioni Dottorandi
Credits: 50
Language: Italian
Teacher: Valeria Franceschi, Catia Scricciolo
Behavioral and Experimental Economics
Credits: 5
Language: Italian
Teacher: Maria Vittoria Levati, Chiara Nardi, Luca Zarri
Corporate governance
Credits: 4
Language: Italian
Teacher: Alessandro Lai
Development Economics
Credits: 4
Language: Italian
Teacher: Federico Perali
Econometrics for management
Credits: 4
Language: Italian
Teacher: Francesca Rossi, Laura Magazzini
Energy Economics
Credits: 2,5
Language: Italian
Teacher: Luigi Grossi
Game Theory
Credits: 4
Language: Italian
Teacher: Francesco De Sinopoli
Inequality
Credits: 5
Language: Italian
Teacher: Francesco Andreoli, Claudio Zoli
Macro economics
Credits: 2,5
Language: Italian
Teacher: Alessia Campolmi
Macroeconomics I
Credits: 10
Language: Italian
Teacher: Claudio Zoli, Angelo Zago, Martina Menon
Mathematics
Credits: 7,5
Language: Italian
Teacher: Alberto Peretti, Athena Picarelli, Letizia Pellegrini
Organization Theory
Credits: 4
Language: Italian
Teacher: Cecilia Rossignoli, Alessandro Zardini, Lapo Mola
Political economy
Credits: 5
Language: Italian
Teacher: Emanuele Bracco, Roberto Ricciuti, Marcella Veronesi
Probability
Credits: 7,5
Language: Italian
Teacher: Marco Minozzo
Metodi quantitativi per la gestione aziendale
Credits: 5
Language: Italian
Teacher: Riccardo Scarpa
Statistica
Credits: 7,5
Language: Italian
Supply Chain Management
Credits: 4
Language: Italian
Teacher: Barbara Gaudenzi
Energy Economics (2019/2020)
Teacher
Referent
Credits
2.5
Language
Italian
Class attendance
Free Choice
Location
VERONA
Learning outcomes
Energy Markets analysis could be carried out from different perspectives. The main idea behind this course would be to focus on the economics of energy markets and on related quantitative models based on linear and nonlinear processes for measuring and forecasting volumes and prices. The focus of the course will be on electricity markets, although reference will also be made to natural gas markets.
Some recent developments about the introduction of renewable sources on the electricity grid and to the economic feasibility of electricity storage will conclude the course.
The main goal of the course will be to illustrate methods and approaches with detailed examples using real data and to provide PhD students with a set of economic models and econometric-statistical tools to perform reliable and original analyses.
Prerequisites
PhD students should be familiar with basic notions of time series analysis and stochastic processes in discrete time and with elementary notions of industrial economics.
Basic knowledge from statistics and econometrics plus rudimentary experiences with data and numerical calculations will be helpful. Quantitative analysis will be performed by the freeware software R (http://cran.r-project.org/).
Program
1. Stylized facts of electricity prices
Price spikes: what determines spikes. Case studies.
Seasonality: determinants. Autocorrelation structure and frequency domain analysis.
Seasonal decomposition: moving average technique, spectral decomposition, rolling volatility technique.
Mean reversion: detrended fluctuation analysis, periodogram regression
Volatility clustering and leverage effect
2. Modelling electricity loads and prices
Factors affecting load patterns (demand side): time factors and weathers conditions. Analysis of weather variables.
Factors affecting prices (supply side): generation factors. The impact of renewables electricity sources.
ARIMA-type models
Regression models with exogenous regressors
GARCH models
Switching models
3. Forecasting and evaluation of forecasting performances
Forecasting loads and prices: selection of the best model
Assessing forecasting performances of alternative models: MAPE, MPE, Theil’s index, Diebold and Mariano test.
The rolling windows technique
Case studies
4. Further topics
Energy storage: the case of gas and electricity
Robust methods for energy prices and loads: implications on forecasting performances
Shift-share analysis of energy demand
Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|
Bee M., Santi F. | Finanza Quantitativa con R | Apogeo | 2013 |
Examination Methods
Written assignment
PhD school courses/classes - 2019/2020
PhD School training offer to be defined
Faculty
Magazzini Laura
laura.magazzini@univr.it 045 8028525Manzoni Elena
elena.manzoni@univr.it 8783PhD students
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