Sinha, Pankaj and Mathur, Kritika (2016): Empirical Analysis of Developments in the Day Ahead Electricity Markets in India.
Preview |
PDF
MPRA_paper_72969.pdf Download (4MB) | Preview |
Abstract
Given the thrust on the deregulation of electricity markets in India since 2003, the short term electricity market with power exchanges in particular have evolved rapidly to support the growth of the power markets in an efficient manner. Since their year of inception 2008, power exchanges are now more efficient and are able to mitigate risks arising from price volatility for the participants to a large extent. This paper throws light on the trading of day ahead electricity contracts in India. We try to assess whether day ahead electricity returns and return volatility exhibit weekday effect. The study also looks at the effect of traded volume of electricity on electricity return volatility and the impact of introduction of the fifteen minute contracts in the day ahead electricity market in India on returns and return volatility.
Item Type: | MPRA Paper |
---|---|
Original Title: | Empirical Analysis of Developments in the Day Ahead Electricity Markets in India |
English Title: | Empirical Analysis of Developments in the Day Ahead Electricity Markets in India |
Language: | English |
Keywords: | Power trading, electricity futures, Power exchange |
Subjects: | D - Microeconomics > D4 - Market Structure, Pricing, and Design > D44 - Auctions Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q48 - Government Policy |
Item ID: | 72969 |
Depositing User: | Pankaj Sinha |
Date Deposited: | 12 Aug 2016 03:47 |
Last Modified: | 20 Oct 2019 04:37 |
References: | 1. Aggarwal, S.K., Saini, L.M., & Kumar, A. (2009). Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13-22. 2. Bowden, N., & Payne, J.E. (2008). Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models. Energy Economics, 30(6), 3186-3197. 3. Catalão, J.P.S., Mariano, S.J.P.S., Mendes, V.M.F., & Ferreira, L.A.F.M. (2007). Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research, 77(10), 1297-1304. 4. Cifter, A. (2013). Forecasting electricity price volatility with the Markov-switching GARCH model: Evidence from the Nordic electric power market. Electric Power Systems Research, 102, 61-67. 5. Conejo, A.J., Contreras, J., Espínola, R., & Plazas, M.A. (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. International Journal of Forecasting, 21(3), 435-462. 6. Contreras, J., Espínola, R., Nogales, F.J., & Conejo, A.J. (2003). ARIMA Models to Predict Next-Day Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. 7. Cuaresma, J.C., Hlouskova, J., Kossmeier, S., & Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87–106. 8. Fan, S., & Hyndman, R. J. (2011). Short-term load forecasting using semi-parametric additive models. In Power and Energy Society General Meeting, IEEE, 1-7. 9. Fontana, F., Gianfreda, A., & Renò, R. (2007). Does it take volume to move European electricity spot prices? Anales de Estudios Económicos y Empresariales, 17, 59-85. 10. Garcia, R.C., & Contreras, J. (2005). A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices. IEEE Transactions on Power Systems, 20(2), 867-874. 11. Guirguis, H.S., & Felder, F.A. (2004). Further advances in forecasting day-ahead electricity prices using time series models. KIEE International Transactions on PE, 4-A(3), 159-166. 12. Hadsell, L. (2006). A TARCH examination of the return volatility–volume relationship in electricity futures. Applied Financial Economics, 16(12), 893-901. 13. Hadsell, L., & Shawky, H.A. (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, 27(2), 157-179. 14. Hadsell, L., & Shawky, H.A. (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, 27(2), 157-179. 15. Higgs, H., & Worthington, A.C. (2005). Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects. The Energy Journal, 26(4), 23-41. 16. Huisman, R., Huurman, C., & Mahieu, R. (2007). Hourly electricity prices in day-ahead markets. Energy Economics, 29(2), 240-248. 17. Jakasa T., Androcec, I., & Sprcic P. (2011). Electricity price forecasting — ARIMA model approach, Conference Proceedings of 8th International Conference on the European Energy Market (EEM), 222–225. 18. Kalantzis, F. G., & Milonas, N. T. (2013). Analyzing the impact of futures trading on spot price volatility: Evidence from the spot electricity market in France and Germany. Energy Economics, 36, 454-463. 19. Kristiansen, T. (2012). Forecasting Nord Pool day-ahead prices with an autoregressive model. Energy Policy, 49, 328-332. 20. Li, G., Liu, C.C., Mattson C., & Lawarrée, J. (2007). Day-Ahead Electricity Price Forecasting in a Grid Environment. IEEE Transactions on Power Systems, 22(1), 266-274. 21. Liu, H., & Shi, J. (2013). Applying ARMA-GARCH approaches to forecasting short-term electricity prices. Energy Economics, 37, 152-166. 22. Longstaff, F. A., & Wang, A. W. (2004). Electricity forward prices: a high‐frequency empirical analysis. Journal of Finance, 59(4), 1877-1900. 23. Nogales, F.J., Contreras J., & Conejo A.J. (2002). Forecasting Next-Day Electricity Prices by Time Series Models. IEEE Transactions on Power Systems, 17(2), 342-348. 24. Raviv, E., Bouwman, K.E., & Dijk, D. V. (2013). Forecasting day-ahead electricity prices: utilizing hourly price. Tinebergen Institute, Working Paper Series. 25. Singhal, D., & Swarup, K.S. (2011). Electricity price forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 550-555. 26. Thomas, S., Ramiah, V., Mitchell, H., & Heaney, R. (2011). Seasonal factors and outlier effects in rate of return on electricity spot prices in Australia's National Electricity Market. Applied Economics, 43(3), 355-369. 27. Zhang, J., & Tan, J. (2013). Day-ahead electricity price forecasting using WT, CLSSVM and EGARCH model. International Journal of Electricity Power and Energy Systems, 45(1), 362–368. 28. Zhou, M., Yan, Z., Ni, Y.X., Li, G., & Nie, Y. (2006). Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach. IEEE Proceedings-Generation, Transmission, Distribution, 153(2), 187-195. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/72969 |