COURSE OVERVIEW
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 indepth 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.
TARGET AUDIENCE
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.
PREREQUISITE
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.
PROGRAM
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.
SESSIONS 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.
SUGGESTED READING (PRE – AND POST-COURSE)
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.
