Heavy-tails and regime-switching in electricity prices

In this paper we first analyze the stylized facts of electricity prices, in particular, the extreme volatility and price spikes which lead to heavy-tailed distributions of price changes. Then we calibrate Markov regime-switching (MRS) models with heavy-tailed components and show that they adequately address the aforementioned characteristics. Contrary to the common belief that electricity price models ‘should be built on log-prices’, we find evidence that modeling the prices themselves is more beneficial and methodologically sound, at least in case of MRS models.

[1]  R. Weron,et al.  Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts? , 2007 .

[2]  A. Secchi,et al.  Some Statistical Investigations on the Nature and Dynamics of Electricity Prices , 2005 .

[3]  D. Bunn Modelling prices in competitive electricity markets , 2004 .

[4]  Marc S. Paolella,et al.  An econometric analysis of emission allowance prices , 2008 .

[5]  Hugo Steinhaus,et al.  Market price of risk implied by Asian-style electricity options and futures ☆ Rafa ł Weron ⁎ , 2008 .

[6]  Vincent Kaminski Managing energy price risk : the new challenges and solutions , 2004 .

[7]  Rodney C. Wolff,et al.  Outlier Treatment and Robust Approaches for Modeling Electricity Spot Prices , 2007 .

[8]  F. Benth,et al.  Stochastic Modeling of Electricity and Related Markets , 2008 .

[9]  K. F. Chan,et al.  Using extreme value theory to measure value-at-risk for daily electricity spot prices , 2006 .

[10]  R. Huisman,et al.  Regime Jumps in Electricity Prices , 2001 .

[11]  Ingve Simonsen,et al.  Volatility of power markets , 2005 .

[12]  Agnieszka Wyłomańska,et al.  Measures of Dependence for Stable AR(1) Models with Time-Varying Coefficients , 2008 .

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  S. Rachev,et al.  Spot and Derivative Pricing in the EEX Power Market , 2007 .

[15]  R. Weron Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[16]  S. Rachev,et al.  Stable Paretian Models in Finance , 2000 .

[17]  Cyriel de Jong The Nature of Power Spikes: a regime-switch approach , 2005 .

[18]  D. Bessler,et al.  Price dynamics among U.S. electricity spot markets , 2006 .

[19]  Shi-Jie Deng,et al.  Levy process-driven mean-reverting electricity price model: the marginal distribution analysis , 2005, Decis. Support Syst..

[20]  T. Mount,et al.  ESTIMATING THE VOLATILITY OF SPOT PRICES IN RESTRUCTURED ELECTRICITY MARKETS AND THE IMPLICATIONS FOR OPTION VALUES , 1998 .

[21]  Stefan Trück,et al.  Vorsicht Hochspannung! - Risikomanagement in Energiemärkten (Teil II) Modellierung von Strompreisen , 2004 .

[22]  James D. Hamilton Analysis of time series subject to changes in regime , 1990 .

[23]  Irina Khindanova,et al.  Stable modeling in energy risk management , 2002, Math. Methods Oper. Res..

[24]  E. Benz,et al.  CO2 Emission Allowances Trading in Europe - Specifying a New Class of Assets , 2006 .

[25]  Rafal Weron,et al.  Modeling Electricity Prices with Regime Switching Models , 2004, International Conference on Computational Science.

[26]  J. Klafter,et al.  From solar flare time series to fractional dynamics , 2008 .

[27]  W. Härdle,et al.  Statistical Tools for Finance and Insurance , 2003 .

[28]  J. Wyatt Decision support systems. , 2000, Journal of the Royal Society of Medicine.

[29]  Rafał Weron,et al.  Computationally intensive Value at Risk calculations , 2004 .

[30]  Hans Byström,et al.  Extreme Value Theory and Extremely Large Electricity Price Changes , 2005 .

[31]  Svetlozar T. Rachev,et al.  Stable modeling of different European power markets , 2005 .