Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market

Stevenson, Max; Amaral, Luiz Felipe; Peat, Maurice

doi:10.2202/1558-3708.1383

Cited by 12 publications

(17 citation statements)

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“…To extend the signal beyond the calibration window, Yousefi et al (2005) used a spline fit, Wong et al (2003) applied polynomial extrapolation, while Stevenson (2001) and Stevenson et al (2006) utilized predictions of threshold autoregressive models (TAR) fitted to smoothed (via wavelet shrinkage) spot prices from the Australian electricity market. To our best knowledge, the latter two papers are the only ones where wavelets have been used to forecast electricity prices for horizons of more than a few days ahead.…”

Section: 23mentioning

confidence: 99%

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Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

2013

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When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less important for valuation of typical power derivatives, the long-term seasonal components (LTSC; seasonal, annual) are much more difficult to tackle. Surprisingly, in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered. With this paper we want to fill the gap and present a thorough study on estimation and forecasting of the LTSC of electricity spot prices. We consider a battery of models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that all considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long-term seasonal components.

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Section: 23mentioning

confidence: 99%

“…• wavelets (Conejo et al, 2005;Weron, 2010, 2012;Stevenson, 2001;Schlueter, 2010;Stevenson et al, 2006;Weron, 2006Weron, , 2009Weron et al, 2004a,b) or other nonparametric smoothing techniques (Bordignon et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

2013

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“…Leaving out the highest one or two detail levels leads to price series with eliminated price spikes, at least for half-hourly data (Stevenson, 2001;Stevenson et al, 2006). Alternatively, more detail levels can be removed leading to a much smoother signal, which then can be subtracted from the original prices to yield deseasonalized and detrended data.…”

Section: Identification Of Spikesmentioning

confidence: 99%

“…The third approach is to use wavelet decomposition and smoothing as more robust to outliers and a less periodic alternative to Fourier analysis (Janczura and Weron, 2010;Stevenson et al, 2006;. Recall, that wavelets belong to families and come in pairs of a father and a mother wavelet for a given order, like the Daubechies wavelets of order 24 used here.…”

Section: The Wavelet Long-term Seasonal Componentmentioning

confidence: 99%

“…Other model the seasonal pattern by sinusoidal functions or sums of sinusoidal functions of different frequencies (Bierbrauer et al, 2007;Borovkova and Permana, 2006;Cartea and Figueroa, 2005;Erlwein et al, 2010;Geman and Roncoroni, 2006;Keles et al, 2012;Lucia and Schwartz, 2002;Pilipovic, 1998;Seifert and Uhrig-Homburg, 2007;Weron, 2008), sometimes coupled with an exponentially weighted moving average (EWMA) (De Jong, 2006). As a more robust to outliers and less strictly periodic alternative to Fourier analysis, wavelet decomposition and smoothing have been applied by Weron (2010, 2012), Stevenson (2001), Stevenson et al (2006), Weron ( , 2009 and Weron et al (2004a,b).…”

Section: Introductionmentioning

confidence: 99%

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Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

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A note on using the Hodrick–Prescott filter in electricity markets

Weron

Zator

2015

Energy Economics

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Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine-based models. However, a clear disadvantage of the wavelet-based approach is the increased complexity of the technique as compared to the other two classes of LTSC models, which may render it too complicated for practitioners. To facilitate this problem, we propose here a much simpler, yet equally powerful method for identifying the LTSC in electricity spot price series. It makes use of the Hodrick-Prescott (HP) filter, a widely-recognized tool in macroeconomics.

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Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market

Cited by 12 publications

References 0 publications

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

A note on using the Hodrick–Prescott filter in electricity markets

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