Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices

This paper uses high-frequency continuous intraday electricity price data from the EPEX market to estimate and forecast realized volatility. Three different jump tests are used to break down the variation into jump and continuous components using quadratic variation theory. Several heterogeneous autoregressive models are then estimated for the logarithmic and standard deviation transformations. Generalized autoregressive conditional heteroskedasticity (GARCH) structures are included in the error terms of the models when evidence of conditional heteroskedasticity is found. Model selection is based on various out-of-sample criteria. Results show that decomposition of realized volatility is important for forecasting and that the decision whether to include GARCH-type innovations might depend on the transformation selected. Finally, results are sensitive to the jump test used in the case of the standard deviation transformation.

[1]  M. Dacorogna,et al.  Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis , 1990 .

[2]  Roberto Renò,et al.  Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting , 2008 .

[3]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[4]  George Tauchen,et al.  Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance , 2012 .

[5]  Erik Haugom,et al.  Forecasting Spot Price Volatility Using the Short-Term Forward Curve , 2010 .

[6]  Carl J. Ullrich,et al.  Realized Volatility and Price Spikes in Electricity Markets: The Importance of Observation Frequency , 2009 .

[7]  Tim Bollerslev,et al.  Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility , 2007 .

[8]  P. Solibakke,et al.  Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data , 2011 .

[9]  Philip Gray,et al.  A New Approach to Characterizing and Forecasting Electricity Price Volatility , 2008 .

[10]  Michael Frömmel,et al.  Modeling the Daily Electricity Price Volatility with Realized Measures , 2013 .

[11]  N. Shephard,et al.  Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation , 2005 .

[12]  Ernst Schaumburg,et al.  Federal Reserve Bank of New York Staff Reports Jump-robust Volatility Estimation Using Nearest Neighbor Truncation Jump-robust Volatility Estimation Using Nearest Neighbor Truncation , 2010 .

[13]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[14]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[15]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[16]  S. Mittnik,et al.  The Volatility of Realized Volatility , 2005 .