Reliability of ARMA and GARCH models of electricity spot market prices (original) (raw)

Modelling Price Dynamics in Turkish Electricity Market: Lessons from Garch Estimates

M U Iktisadi ve Idari Bilimler Dergisi

In this paper, we estimate electricity market volatility in Turkey using various GARCH-class models. Spot price in Turkish electricity market exhibits significant variation and therefore, conditional modelling of the volatility can make us better understand the price dynamics of this important market. We estimate volatilities of weekly prices over the period of January 2010 to April 2017 and compare the performance of various GARCH models that take into account the asymmetric effects, possible mean effects of the volatility, fat-tails of the distribution and persistence of the volatility series. We found time varying volatility is an important feature of the price dynamics in Turkish electricity market and additionally, in modelling volatility, paying attention to the extreme price changes via heavy tailed distributions improves the model fit substantially.

Modelling Energy Market Volatility Using Garch Models and Estimating Value-At-Risk

2019

Purpose: The study focused on modelling the volatility of energy markets spot prices using GARCH models and estimating Value-at-Risk. Methodology: The conditional heteroscedasticity models are used to model the volatility of gasoline and crude oil energy commodities. In estimating Value at Risk; GARCH-EVT model is utilized in comparison with other conventional approaches. The accuracy of the VaR forecasts is assessed by using standard statistical back testing procedures. Results: The empirical results suggests that the gasoline and crude oil prices exhibit highly stylized features such as extreme price spikes, price dependency between markets, correlation asymmetry and non-linear dependency. We also conclude that the EGARCH-EVT model is more robust, provides the best t and outperforms the other conventional models in terms of forecasting accuracy and VaR prediction. Generally, the GARCH-EVT model can be used to plays an integral role as a risk management tool in the energy industry....

Analysis of Forecasting Models in an Electricity Market under Volatility

2021

Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks a concrete consensus on the most suitable forecasting approach. We conduct an extensive empirical analysis to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we performed out-of-sample forecasting and back-testing, and we evaluated the performance of these models. Our empirical analysis indicates that an ARMA-GARCH framework with the student’s t-distribution significantly outperforms other frameworks. We only performed wavelet-based forecasting based on the MAPE. The results of the robust forecasting methods are capable of displaying the importance of proper forecasting process design, policy implications for market efficiency, and predic...

International Journal of Energy Economics and Policy Application of GARCH Model to Forecast Data and Volatility of Share Price of Energy (Study on Adaro Energy Tbk, LQ45)

2018

Most of the times, Economic and Financial data not only become highly volatile but also show heterogeneous variances (heteroscedasticity). The common method of the Box Jenkins cannot be used for data modeling as the method has an effect of heteroscedasticity (autoregressive conditional heteroscedastic ARCH effects). One of the usable methods to overcome the effect of heteroscedasticity is GARCH model. The aim of this study is to find the best model to estimate the parameters, to predict the share price, and to forecast the volatility of data share price of Adaro energy Tbk, Indonesia, from January 2014 to December 2016. The study also discuss the Window Dressing. The best model which fits the data is identified as AR(1)-GARCH (1,1). The application of this best model for forecasting the share price of Adaro energy Tbk, Indonesia, for the next 30 days showed very promising results and the mean absolute percentage error was determined as 2.16%.

Statistical Modeling of Electricity Prices using Time Series Model

2014

Forecasting electric power prices of a competitive market is important to providing estimates of electricity prices for future days. Forecasting results can be used by generation companies for bidding in the market strategically. The forecast can also be used by the transmission companies can plan a head for scheduling short-term generator outages and design load response programs. The aim of this study is to determine the best model for forecasting the prices of electricity in a competitive market. Thus, we will compare the AR, MA, GARCH, and ARCH model. The study also aims at providing the estimates of electricity prices based on the best model. Other variables that provide energy in the industries will be used to test on the validity of the model. The ARMAX model indicated to be the better than the GARCH model in modeling the electricity prices.

A study of electricity price volatility for the Brazilian energy market

2008 5th International Conference on the European Electricity Market, 2008

In the recent months, the price of the electricity in Brazil has presented a high level of volatility. As an example, the verified highest electricity price return in March 2007 was almost 260%. The volatility of a commodity plays an important role in the study of the risk management. It also improves the efficiency in parameter estimation and the accuracy in interval forecast. In this work, the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model is used to study the price volatility in the Brazilian market in four geographical regions. The results have shown that the model is able to estimate the behavior of the volatility.

Garch Modelling of High-Frequency Volatility in Australia's National Electricity Market

SSRN Electronic Journal, 2000

This paper considers the underlying volatility process in Australian electricity prices and examines the applicability of a range of GARCH specifications to modelling volatility in 5 regional pool markets in the NEM. The GARCH variants considered include the basic GARCH, TARCH, EGARCH and PARCH specifications. The approach used in this study differs from the previous Australian ARCH-based studies in that discrete half-hourly returns are used over a six-year sample period, across each of five regional pools in the NEM. Seasonal effects and outliers (price spikes) are filtered prior to fitting the various GARCH models in order to investigate the underlying volatility process without the noise contributed by these effects. Results show that the PARCH specification is favoured in the NSW, QLD and SNOWY regions but in QLD and SA, the EGARCH specification is preferred as it more reliably describes the volatility processes in those two regions.

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Studies in Nonlinear Dynamics & Econometrics, 2000

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable -system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-ofsample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting. * The authors are grateful to Dick van Dijk and two anonymous referees for insightful comments and suggestions.

Electricity Spot Price Modeling and Forecasting in European Markets

Energies

In many competitive electricity markets around the world, the dynamic behavior of hourly electricity prices is subject to significant uncertainty and volatility due to electricity demand, availability of generation sources, fuel costs, and power plant availability. This work is devoted to describing and comparing the dynamics of electricity prices for some markets in Europe, selecting the five countries representing the largest economies in Western Europe (France, Germany, Italy, Spain, and the United Kingdom). Additionally, Denmark is included in the study to assess whether the size of the country is a determinant of price behavior. The six datasets of hourly price series, which exhibits a strong daily seasonality, are modelled using the most relevant well-known statistical models for time series analysis: ARIMA models and different versions of GARCH models. The comparison of the estimated models’ parameters, the analysis of outliers’ rate of appearance and the evaluation of out-of...