Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs (original) (raw)

Further advances in forecasting day-ahead electricity prices using time series models

KIEE International Transactions on …, 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

A GARCH forecasting model to predict day-ahead electricity prices

IEEE Transactions on Power Systems, 2005

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed.

Modeling the daily electricity price volatility with realized measures

Energy Economics, 2014

We propose using the Realized GARCH model to estimate the daily price volatility in the EPEX power markets. The model specification extracts the volatility-related information from realized measures, which substantially improves the in-sample fit of the data compared to the standard EGARCH model. More importantly, evidence on the out-of-sample forecasts reinforces the value of the specifications as the forecast quality is improved over the benchmark model under eight conventional criteria. The increased forecast accuracy is robust under both the rolling-window and recursive estimation scheme. Finally, we show that intraday range is an effective volatility indicator in the power market as the benefit of including intraday range is substantial as compared to realized variance.

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...

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.

Some New Approaches to Forecasting the Price of Electricity: A Study of Californian Market

2008

In this paper we consider the forecasting performance of a range of semi-and non-parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time-and price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financial econometrics literature, where volatility clustering is common. However, electricity time series tend to exhibit higher volatility on a daily basis, but within a mean reverting framework, albeit with occasional large 'spikes'.

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...

Forecasting Time-Varying Covariance Matrices in Intradaily Electricity Spot Prices

Working Papers Serie Ad, 2002

This paper deals with analysing and forecasting intradaily volatility in electricity spot prices. We analyse the hourly spot prices from the Argentine Electricity Market by grouping prices in three daily series (block bids). We estimate the VAR model for the conditional mean structure and several multivariate analysis based on the multivariate GARCH models, specifically the orthogonal GARCH by Alexander (2000) and the constrained multivariate GARCH by Engle and Mezrich (1996). We also measure the forecasting performance of the daily block bid volatilities and covariances under both approaches obtaining similar results. This methodology could be used for managing risk of block bid portfolios and also for the valuation of derivatives on intradaily time-blocks of electricity spot prices.

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

International Journal of Forecasting, 2008

This empirical paper compares the accuracy of 12 time series methods for short-term (dayahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models, their extensions -spike preprocessed, threshold and semiparametric autoregressions (i.e. AR models with nonparametric innovations), as well as, mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.