TÜRKİYE ELEKTRİK FİYATLARINDAKİ ANİ SIÇRAMALARIN MARKOV REJİM DEĞİŞİM MODELLERİYLE ANALİZİ
Yıl 2018,
, 217 - 237, 27.04.2018
Osman Tüzün
,
Ramazan Ekinci
,
Fatih Ceylan
Hakan Kahyaoğlu
Öz
Bu çalışmanın amacı, Türkiye elektrik piyasasında gerçekleşen sistem
marjinal fiyatlarındaki (spot elektrik fiyatları) ani fiyat artış (spike)
etkilerini analiz etmektir. Ele alınan zaman aralığında piyasa fiyatlarını
temsil eden zaman serisinde söz konusu etkilerin varlığı Markov-Değişim
Genelleştirilmiş Kendisiyle Bağlaşımlı Koşullu Değişen Varyans (MS-GARCH)
tekniği kullanılarak test edilmiştir. Söz konusu model düşük ve yüksek oynaklık
dönemlerini temsil eden iki farklı rejimle tanımlanmıştır.
Elde
edilen sonuçlara göre ani fiyat artışlarının (spike), ortalama fiyat düzeyinden
sapma yaratan tesadüfi (stokastik) bir etkiye sahip olduğu sonucuna
ulaşılmıştır. Bununla birlikte elektrik piyasasında genellikle normal fiyat
rejimleri geçerli olmakla birlikte, normal fiyat rejimlerinden ani fiyat
yükseliş rejimine geçiş olasılığının yüksek olduğu da görülmektedir. Ayrıca
elektrik fiyatları yüksek bir oynaklıkla birlikte güçlü bir rejim bağımlılığı
da göstermektedir.
Kaynakça
- Adam, C., Joanne, F., ve Stan, H. (2013), “Semi-parametric Forecasting of Spikes in Electricity Prices.” The Economic Record, (287), 508.
- Amjady, N., ve Keynia, F. (2011), “A new prediction strategy for price spike forecasting of day-ahead electricity markets.” Applied Soft Computing Journal, 114246-4256. doi:10.1016/j.asoc.2011.03.024.
- Becker, R., Hurn, S., ve Pavlov, V. (2007), “Modelling Spikes in Electricity Prices.” Economic Record, (263), 371.
- Cai, J. (1994), “A Markov Model of Unconditional Variance in ARCH”, Journal of Business and Economics Statistics 12 (3) ,309–316.
- Christensen, T., Hurn, A., ve Lindsay, K. (2012), “Forecasting Spikes in Electricity Prices.” International Journal of Forecasting, 28400-411. doi:10.1016/j.ijforecast.2011.02.019
- Cifter, A. (2013), “Forecasting Electricity Price Volatility with The Markov-Switching GARCH Model: Evidence From The Nordic Electric Power Market.” Electric Power Systems Research, 10261-67. doi:10.1016/j.epsr.2013.04.007.
- Davies, N., & Joseph D. P. (1986), “Detecting Non-Linearity in Time Series,” The Statistician, C. XXXV, No:2, s. 274.
- De Jong, C. (2006), “The Nature of Power Spikes: A Regime-Switch Approach.” Studies in Nonlinear Dynamics and Econometrics, 10(3).
- De Jong, C. ve Huisman, R. (2002),”Option Formulas for Mean-Reverting Power Prices with Spikes”, Energy Global Research Paper; and ERIM Report Series Reference No. ERS 2002 96 F&A. http://ssrn.com/abstract=324520 or http://dx.doi.org/10.2139/ssrn.324520
- De Sanctis, A., ve Mari, C. (2007), “Modelling Spikes in Electricity Markets Using Excitable Dynamics. Physica A: Statistical Mechanics and Its Applications,” 384457-467. doi:10.1016/j.physa.2007.05.015.
- Doğan, E. (2016), “Are Shocks to Electricity Consumption Transitory or Permanent? Sub-National Evidence From Turkey.” Utilities Policy, doi:10.1016/j.jup.2016.06.007.
- Eichler, M., Grothe, O.,Manner, H. ve Türk, D. (2013), “Models for Short-Term Forecasting of Spike Occurrences in Australian Electricity Markets: A Comparative Study.” Journal of Energy Markets 7(1).
- Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimate of The Variance of United Kingdom Inflation”, Econometrica 50 (1982) 987–1007.
- Granger, C. W. J. & Teräsvırta, T. (1993), “Modelling Nonlinear Economic Relationships.” Oxford University Pres.
- Gray, S.F. (1996), “Modeling the Conditional Distribution of Interest Rates As A Regime Switching Process”, Journal of Financial Economics 42 (1) ,27–62.
- Haldrup, N. ve Nielsen, M. Ø. (2006a), “A Regime Switching Long Memory Model for Electricity Prices.” Journal Of Econometrics,135349-376.doi:10.1016/j.jeconom.2005.07.021.
- Haldrup, N. ve Nielsen, M. Ø. (2006b), “Directional congestion and regime switching in a long memory model for electricity prices.” Studies in Nonlinear Dynamics & Econometrics, 10:1–24.
- Haldrup, N., Nielsen, F. S., ve Nielsen, M. Ø. (2010), “A Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching.” Energy Economics, 321044-1058. doi:10.1016/j.eneco.2010.02.012.
- Hamilton, J.D. (1989), “A New Approach to The Economic Analysis of Nonstationary Time Series and The Business Cycle”, Econometrica 57 (2), 357–384.
- Hamilton, J.D. (1990), “Analysis of Time Series Subject to Changes in Regimes”, Journal of Econometrics 45, 39–70.
- Hamilton, J.D. (1994), “Time Series Analysis,” 1st ed., Princeton University Press, Princeton
- Hamilton,J.D. ve Susmel, R. (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime”, Journal of Econometrics 64 (1/2) ,307–333.
- Higgs, H., ve Worthington, A. (2008), “Full Length Article: Stochastic Price Modeling of High Volatility, Mean-Reverting, Spike-Prone Commodities: The Australian Wholesale Spot Electricity Market.” Energy Economics, 30 (Technological Change and the Environment), 3172-3185. doi:10.1016/j.eneco.2008.04.006.
- Huisman, R. (2008), “The influence of temperature on spike probability in day-ahead power prices.” Energy Economics, 30:2697–2704.
- Huisman, R., Mahieu, R. J., ve Schlichter, F. (2009), “Electricity Portfolio Management: Optimal Peak/Off-Peak Allocations”, Energy Economics, Vol:31, Issue 1,pp169–174.
- Huisman, R., ve Mahieu, R. (2003), “Regime Jumps in Electricity Prices.” Energy Economics, 25425-434. doi:10.1016/S0140-9883(03)00041-0.
- Janczura ve R. Weron. (2010), “An empirical comparison of alternate regime-switching models for electricity spot prices.” Energy Economics, 32:1059–1073.
- Janczura, J., Trück, S., Weron, R., ve Wolff, R. C. (2012), “Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide To Robust Modeling” , MPRA Paper No. 39277, posted 6. June 2012, http://mpra.ub.uni-muenchen.de/39277/.
- Kanamura, T. ve Ohashi, K. (2008), “On transition probabilities of regime switching in electricity prices.” Energy Economics, 30:1158–1172.
- Karakatsani, N. ve Bunn, D. (2008), “Intra-day and regime-switching dynamics in electricity price formation.” Energy Economics, 30:1776–1797.
- Keenan, D. M. (1985), “A Tukey Nonadditivity-Type Test for Time Series Nonlinearity.” Biometrika, C. LXXII, No:1, s. 39–44.
- Klaassen, F. (202),” Improving GARCH Volatility Forecasts with Regime-Switching GARCH”, Empirical Economics 27 (2) ,363–394.
- Lu, X., Dong, Z. Y., ve Li, X. (2005), “Electricity Market Price Spike Forecast with Data Mining Techniques.” Electric Power Systems Research, 7319-29. doi:10.1016/j.epsr.2004.06.002.
- Marwan, M., Ledwich, G., ve Ghosh, A. (2014), “Demand-Side Response Model to Avoid Spike of Electricity Price.” Journal Of Process Control, 24(Energy Efficient Buildings Special Issue), 782-789. doi:10.1016/j.jprocont.2014.01.009.
- Masorry, Z. (2010), “Sources of Heteroscedasticity in The Spot Electricity Price Time Series” Energy Market (EEM), 7th International Conference on the European, doi: 10.1109/EEM.2010.5558781.
- Mauritzen, J. (2015), “How Price Spikes Can Help Overcome The Energy Efficiency Gap.” Economics Letters, 114. doi:10.1016/j.econlet.2015.07.008.
- Mount, T. D., Ning, Y., ve Cai, X. (2006), “Predicting Price Spikes in Electricity Markets Using A Regime-Switching Model with Time-Varying Parameters.” Energy Economics, 2862-80. doi:10.1016/j.eneco.2005.09.008.
- Nelson, D. (1991), “Conditional Heteroscedasticity in Asset Returns: A New Approach”, Econometrica. 59, 347–370.
- Paraschiv, F., Fleten, S., ve Schürle, M. (2015), “A Spot-Forward Model for Electricity Prices with Regime Shifts. Energy Economics,” 47142-153. doi:10.1016/j.eneco.2014.11.003.
- R.J. Garcia, J. Contreras, M.V. Akkeren, J.B.C. Garcia. (2005). “A GARCH forecasting model to predict day-ahead electricity prices,” IEEE Transactions on Power Systems, 20 (2) 867–874.
- Schmidt, T. (2008), “Modelling Energy Markets with Extreme Spikes.” Mathematical Control Theory & Finance, 359. doi:10.1007/978-3-540-69532-5_20.