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Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

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  • Erdogdu, Erkan

Abstract

This paper uses high frequency spot price data from fourteen wholesale electricity markets in Europe to analyze asymmetric volatility in European day-ahead power markets with Exponential GARCH (E-GARCH) and TARCH models. Our data set ranges from 1992 to 2015 and consists of approximately 926 thousand observations. As such, this paper constitutes the most extensive and comprehensive work conducted so far on European power markets, to the best of our knowledge. Unlike most of the literature that treats price as a continuous variable and attempts to model its trajectory, this paper adopts a unique approach and regards each hour in a day a separate market. The results show, in post-2008 period, the most expensive electricity is consumed in Turkey, Ireland and UK while the cheapest power is in Russia, Nordic countries and Czech Republic. Russia, Poland and Czech Republic have the least volatile markets while France, Ireland and Portugal have the most volatile ones. Volatility has decreased in many European countries in post-2008 period. Besides, we find magnitude effect is usually larger than the leverage effect, meaning that the absolute value of price change is relatively more important than the sign of the change (whether it is an increase or a decrease) to explain volatility in European day-ahead power markets. Moreover, the results imply there isn’t a uniform inverse leverage effect in electricity prices; that is, price increases are more destabilizing in some European markets (e.g. Poland, Slovenia, Ireland, Netherlands) than comparable price decreases but vice versa also holds true in some other countries (e.g. Portugal and France). Leverage (or inverse leverage) effect in post-2008 period is relatively stronger in Portugal, France and Ireland; but its impact is quite limited in Turkey and Germany. Furthermore, although the impact of seasonality on prices is obvious, a specific pattern cannot be identified. Finally, large changes in the volatility will affect future volatilities for a relatively longer period of time in Nordic countries, Ireland and the UK while changes in current volatility will have less effect on future volatilities in Czech Republic, Russia and Turkey.

Suggested Citation

  • Erdogdu, Erkan, 2015. "Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis," MPRA Paper 70986, University Library of Munich, Germany, revised 09 Dec 2015.
    • Handle: RePEc:pra:mprapa:70986
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    1. Haugom, Erik & Ullrich, Carl J., 2012. "Forecasting spot price volatility using the short-term forward curve," Energy Economics, Elsevier, vol. 34(6), pages 1826-1833.
    2. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    3. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    4. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    5. Erdogdu, Erkan, 2011. "The impact of power market reforms on electricity price-cost margins and cross-subsidy levels: A cross country panel data analysis," Energy Policy, Elsevier, vol. 39(3), pages 1080-1092, March.
    6. Schlueter, Stephan, 2010. "A long-term/short-term model for daily electricity prices with dynamic volatility," Energy Economics, Elsevier, vol. 32(5), pages 1074-1081, September.
    7. Le Pen, Yannick & Sévi, Benoît, 2010. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Energy Economics, Elsevier, vol. 32(4), pages 758-770, July.
    8. Erkan Erdogdu, 2014. "The Political Economy of Electricity Market Liberalization: A Cross-country Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    9. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    10. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    11. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    12. Deb, Rajat & Albert, Richard & Hsue, Lie-Long & Brown, Nicholas, 2000. "How to Incorporate Volatility and Risk in Electricity Price Forecasting," The Electricity Journal, Elsevier, vol. 13(4), pages 65-75, May.
    13. Erdogdu, Erkan, 2013. "A cross-country analysis of electricity market reforms: Potential contribution of New Institutional Economics," Energy Economics, Elsevier, vol. 39(C), pages 239-251.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Lester Hadsell & Hany A. Shawky, 2006. "Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market, 2001-2004," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 157-180.
    16. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    17. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
    18. Higgs, Helen & Worthington, Andrew, 2008. "Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market," Energy Economics, Elsevier, vol. 30(6), pages 3172-3185, November.
    19. Higgs, Helen, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 748-756, September.
    20. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    21. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
    22. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
    23. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    24. Boogert, Alexander & Dupont, Dominique, 2005. "On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands," Energy Economics, Elsevier, vol. 27(5), pages 752-770, September.
    25. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
    26. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    27. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    28. Bierbrauer, Michael & Menn, Christian & Rachev, Svetlozar T. & Truck, Stefan, 2007. "Spot and derivative pricing in the EEX power market," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3462-3485, November.
    29. repec:dau:papers:123456789/5450 is not listed on IDEAS
    30. Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
    31. Stein-Erik, Fleten & Paraschiv, Florentina & Schürle, Michel, 2013. "Spot-forward Model for Electricity Prices," Working Papers on Finance 1311, University of St. Gallen, School of Finance.
    32. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
    33. Jan Seifert & Marliese Uhrig-Homburg, 2007. "Modelling jumps in electricity prices: theory and empirical evidence," Review of Derivatives Research, Springer, vol. 10(1), pages 59-85, January.
    34. Hellström, Jörgen & Lundgren, Jens & Yu, Haishan, 2012. "Why do electricity prices jump? Empirical evidence from the Nordic electricity market," Energy Economics, Elsevier, vol. 34(6), pages 1774-1781.
    35. Helen Higgs, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Discussion Papers in Economics economics:200904, Griffith University, Department of Accounting, Finance and Economics.
    36. Lester Hadsell, Achla Marathe and Hany A. Shawky, 2004. "Estimating the Volatility of Wholesale Electricity Spot Prices in the US," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-40.
    37. Helen Higgs & Andrew C. Worthington, 2005. "Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-42.
    38. Dias, José G. & Ramos, Sofia B., 2014. "Heterogeneous price dynamics in U.S. regional electricity markets," Energy Economics, Elsevier, vol. 46(C), pages 453-463.
    39. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    40. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
    41. Ralf Becker & Stan Hurn & Vlad Pavlov, 2007. "Modelling Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 371-382, December.
    42. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
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    More about this item

    Keywords

    asymmetric volatility; price modeling; European power markets; E-GARCH; TARCH;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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