Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data

Haugom, Erik; Westgaard, Sjur; Solibakke, Per Bjarte; Lien, Gudbrand

doi:10.1016/j.eneco.2011.01.013

Cited by 38 publications

(15 citation statements)

References 36 publications

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“…The HAR-RV model is extended by adding additional regressors, similarly in principle to what was done by Haugom et al (2011) for the electricity market. Two main extensions are made; one using the IV measure, and one using the exogenous variables discussed in section 2.…”

Section: Methodsmentioning

confidence: 99%

Forecasting Volatility of the U.S. Oil Market

et al. 2014

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We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contains predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts, while including other market variables significantly improves daily, weekly and monthly volatility forecasts.

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Section: Methodsmentioning

confidence: 99%

Forecasting Volatility of the U.S. Oil Market

et al. 2014

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“…Furthermore, the estimation dataset is updated as new observations become available, while the oldest observations are discarded. Then, a new estimate of the regression parameters is obtained by solving Equation (18). Estimating the parameters on relatively recent values only has the drawback that deviations from the average behavior are quickly discarded.…”

Section: Time Adaptive Quantile Regressionmentioning

confidence: 99%

“…The most popular approach in the existing literature on the matter is to describe conditional price densities by some variant of the generalist autoregressive conditional heteroskedasticity model (GARCH), uni-or multi-variate ( [14][15][16] and the references therein), often with the aid of a jump-diffusion model [17,18]. Disappointingly, though, rigorous measures of the forecasting skill for such conditional density models are, in most cases, not reported.…”

Section: Introductionmentioning

confidence: 99%

Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression

2014

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A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%-95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over the GARCH model lies in the former's ability to generate reliable quantile estimates.

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“…During the last decade, wind power generation has experienced a powerful breakthrough due to incentives, preferential price, Kyoto Protocol commitments and technical achievement, as well as other renewable energy resources. In Europe, Nordic consumers benefit financially from the presence of Danish wind on the power market [15]. In recent years, Germany also has a high penetration of wind power ( Figure 1) [16].…”

Section: Price Forecasting Frameworkmentioning

confidence: 99%

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Matijaš

Delimar

2014

Energies

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Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

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Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data

Cited by 38 publications

References 36 publications

Forecasting Volatility of the U.S. Oil Market

Forecasting Volatility of the U.S. Oil Market

Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

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