A dynamic global hedging procedure making use of futures contracts is developed for a retailer of the electricity market facing price, load and basis risk. Statistical models reproducing stylized facts are developed for the electricity load, the day-ahead spot price and futures prices in the Nord Pool market. These models serve as input to the hedging algorithm, which also accounts for transaction fees. Backtests with market data from 2007 to 2012 show that the global hedging procedure provides considerable risk reduction when compared to hedging benchmarks found in the literature.
AlizadehA. H.NomikosN. K.PouliasisP. K. (2008). “A Markov regime switching approach for hedging energy commodities.” Journal of Banking &Finance32(9): 1970-1983. http://dx.doi.org/10.1016/j.jbankfin.2007.12.020.
2.
AndresenA.KoekebakkerS.WestgaardS. (2010). “Modeling electricity forward prices using the multivariate normal inverse Gaussian distribution.” Journal of Energy Markets3(3): 3-25.
3.
Abdel-AalR. E. (2008). “Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks.” Computers &Industrial Engineering54(4): 903-917. http://dx.doi.org/10.1016/j.cie.2007.10.020.
4.
Abdel-AalR. E.Al-GarniA. Z. (1997). “Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis.” Energy Journal22(11): 1059-1069. http://dx.doi.org/10.1016/S0360-5442(97)00032-7.
5.
BarakatE. H.Al-QasemJ. M. (1998). “Methodology for weekly load forecasting.” IEEE Transactions on Power Systems13(4): 1548-1555. http://dx.doi.org/10.1109/59.736304.
6.
BessembinderH.LemmonM. L. (2002). “Equilibrium pricing and optimal hedging in electricity forward markets.” Journal of Finance57(3): 1347-1382. http://dx.doi.org/10.1111/1540-6261.00463.
7.
BenthF. E.BenthJ. S.KoekebakkerS. (2008). Stochastic Modelling of Electricity and Related Markets. Singapore: World Scientific.
8.
BertsekasD. P. (1995). Dynamic Programming and Optimal Control. Belmont: Athena Scientific.
9.
BotterudA.KristiansenT.IlicM. D. (2010). “The relationship between spot and futures prices in the Nord Pool electricity market.” Energy Economics32(5): 967-978. http://dx.doi.org/10.1016/j.eneco.2009.11.009.
10.
BretonM.de FrutosJ. (2012). “Approximation of Dynamic Programs.” In DuanJ. C., eds., Handbook of Computational Finance. Berlin: Springer. pp. 633-649. http://dx.doi.org/10.1007/978-3-642-17254-0_23.
11.
ByströmH. N. E. (2003). “The hedging performance of electricity futures on the Nordic power exchange.” Applied Economics35(1): 1-11. http://dx.doi.org/10.1080/00036840210138365.
12.
CarteaÁ. and P. Villaplana (2008). “Spot price modeling and the valuation of electricity forward contracts: the role of demand and capacity.” Journal of Banking &Finance32(12): 2502-2519. http://dx.doi.org/10.1016/j.jbankfin.2008.04.006.
13.
CoulonM.PowellW. B.SircarR. (2013). “A model for hedging load and price risk in the Texas electricity market.” Energy Economics40: 976-988. http://dx.doi.org/10.1016/j.eneco.2013.05.020.
14.
DenaultM.SimonatoJ.-G.StentoftL. (2013). “A Simulation-and-Regression Approach for Stochastic Dynamic Programs with Endogenous State Variable.” Computers &Operations Research40(11): 2760-2769. http://dx.doi.org/10.1016/j.cor.2013.04.008.
15.
DengS. J.OrenS. S. (2006). “Electricity derivatives and risk management.” Energy Journal31(6-7): 940-953.
16.
DengS. J.XuL. (2009). “Mean-risk efficient portfolio analysis of demand response and supply resources.” Energy Journal34(10): 1523-1529. http://dx.doi.org/10.1016/j.energy.2009.06.055.
17.
EderingtonL. H. (1979). “The hedging performance of the new futures markets.” Journal of Finance34(1): 157-170. http://dx.doi.org/10.1111/j.1540-6261.1979.tb02077.x.
18.
EichhornA.RömischW. (2006). “Mean-risk optimization models for electricity portfolio management.” Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems: 1-7.
19.
EscribanoA.PeñaJ. IgnacioVillaplanaP. (2011). “Modelling electricity prices: international evidence.” Oxford Bulletin of Economics and Statistics73(5): 622-650. http://dx.doi.org/10.1111/j.1468-0084.2011.00632.x.
20.
EydelandA.WolyniecK. (2003). Energy and Power Risk Management: New Developments in Modeling, Pricing, and Hedging. Hoboken: Wiley.
21.
FletenS. E.BrathenE.Nissen-MeyerS. E. (2010). “Evaluation of static hedging strategies for hydropower producers in the Nordic market.” Journal of Energy Markets3(4): 1-28.
22.
FletenS. E.WallaceS. W.ZiembaW. T. (2002). “Hedging Electricity Portfolios via Stochastic Programming.” In GreengardC.RuszczynskiA., eds., Decision Making Under Uncertainty. New York: Springer. pp. 71-93. http://dx.doi.org/10.1007/978-1-4684-9256-9_4.
23.
FöllmerH.LeukertP. (1999). “Quantile hedging.” Finance and Stochastics3(3): 251-273. http://dx.doi.org/10.1007/s007800050062.
24.
FöllmerH.LeukertP. (2000). “Efficient hedging: cost versus shortfall risk.” Finance and Stochastics4(2): 117-146. http://dx.doi.org/10.1007/s007800050008.
25.
FrancoisP.GauthierG.GodinF. (2014). “Optimal hedging when the underlying asset follows a regime-switching Markov process.” European Journal of Operational Research237(1): 312-322. http://dx.doi.org/10.1016/j.ejor.2014.01.034.
26.
FrestadD.BenthF. E.KoekebakkerS. (2010). “Modeling the term structure dynamics in the nordic electricity swap market.” Energy Journal31(2): 53-86. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol31-No2-3.
27.
FuglerudM.VedahlK. E.FletenS. E. (2012). “Equilibrium simulation of the Nordic electricity spot price.” Proceedings of the 9th International Conference on the European Energy Market (EEM): 1-10.
28.
GodinF.2013. “Minimizing CVaR in global dynamic hedging with transaction costs.” HEC Montreal, Working paper.
29.
González-RomeraE.Jaramillo-MoránM. A.Carmona-FernándezD. (2008). “Monthly electric energy demand forecasting with neural networks and Fourier series.” Energy Conversion and Management49(11): 3135-3142. http://dx.doi.org/10.1016/j.enconman.2008.06.004.
30.
GoutteS.OudjaneN.RussoF. (2013). “Variance optimal hedging for discrete time processes with independent increments: application to electricity markets.” Journal of Computational Finance17(2): 71-111.
31.
HahnH.Meyer-NiebergS.PicklS. (2009). “Electric load forecasting methods: Tools for decision making.” European Journal of Operational Research199(3): 902-907. http://dx.doi.org/10.1016/j.ejor.2009.01.062.
32.
HatamiA. R.SeifiH.Sheikh-El-EslamiM. K. (2009). “Optimal selling price and energy procurement strategies for a retailer in an electricity market.” Electric Power Systems Research79(1): 246-254. http://dx.doi.org/10.1016/j.epsr.2008.06.003.
33.
HuovilaS. (2003). “Short-term forecasting of power demand in the Nord Pool market.” Master thesis, Lappeenranta University of Technology, Lappeenranta, Finland.
34.
JohnsenT. A. (2001). “Demand, generation and price in the Norwegian market for electric power.” Energy Economics23(3): 227-251. http://dx.doi.org/10.1016/S0140-9883(00)00052-9.
35.
JohnsenT. A.OlsenO. J.. (2011). “Regulated and unregulated Nordic retail prices.” Energy Policy39(6): 3337-3345. http://dx.doi.org/10.1016/j.enpol.2011.03.027.
36.
KanamuraT.OhashiK. (2008). “On transition probabilities of regime switching in electricity prices.” Energy Economics30(3): 1158-1172. http://dx.doi.org/10.1016/j.eneco.2007.07.011.
37.
KettunenJ.SaloA.BunnD. W. (2010). “Optimization of Electricity Retailers Contract Portfolio Subject to Risk Preferences.” IEEE Transactions on Power Systems25(1): 117-128. http://dx.doi.org/10.1109/TPWRS.2009.2032233.
38.
KoekebakkerS.OllmarF. (2005). “Forward curve dynamics in the Nordic electricity market.” Managerial Finance31(6): 73-94. http://dx.doi.org/10.1108/03074350510769703.
39.
KleindorferP. R.LiL. (2005). “Multi-period VaR-constrained portfolio optimization with applications to the electric power sector.” Energy Journal26(1): 1-26. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol26-No1-1.
40.
KnittelC. R.RobertsM. R. (2005). “An empirical examination of restructured electricity prices.” Energy Economics27(5): 791-817. http://dx.doi.org/10.1016/j.eneco.2004.11.005.
41.
LiuS. D.JianJ. B.WangY. Y. (2010). “Optimal dynamic hedging of electricity futures based on copula-GARCH models.” Proceedings of the Industrial Engineering and Engineering Management (IEEM) Conference: 2498-2502.
42.
LuciaJ. J.SchwartzE. S. (2002). “Electricity Prices and Power Derivatives: Evidence from the Nordic Power Exchange.” Review of Derivatives Research.5(1): 5-50. http://dx.doi.org/10.1023/A:1013846631785.
43.
LuciaJ. J.TorroH. (2011). “On the risk premium in Nordic electricity futures prices.” International Review of Economics &Finance20(4): 750-763. http://dx.doi.org/10.1016/j.iref.2011.02.005.
44.
MadalenoM.PinhoC. (2008). “The hedging effectiveness of electricity futures.” Working paper.
NiJ.ChuL. K.WuF.SculliD.ShiY. (2012). “A multi-stage financial hedging approach for the procurement of manufacturing materials.” European Journal of Operational Research221(2): 424-431. http://dx.doi.org/10.1016/j.ejor.2012.03.031.
47.
Nordic Energy Regulators (NordREG). 2010. The Nordic financial electricity market. Report 8/2010, November 2010.
48.
OumY.OrenS. S.DengS. J. (2006). “Hedging quantity risks with standard power options in a competitive wholesale electricity market.” Naval Research Logistics53(7): 697-712. http://dx.doi.org/10.1002/nav.20184.
49.
OumY.OrenS. S. (2009). “VaR constrained hedging of fixed price load-following obligations in competitive electricity markets.” Risk and Decision Analysis1(1): 43-56.
50.
OumY.OrenS. S. (2010). “Optimal static hedging of volumetric risk in a competitive wholesale electricity market.” Decision Analysis7(1): 107-122. http://dx.doi.org/10.1287/deca.1090.0167.
51.
RedlC.HaasR.HuberC.BhmerB. (2009). “Price formation in electricity forward markets and the relevance of systematic forecast errors.” Energy Economics31(3): 356-364. http://dx.doi.org/10.1016/j.eneco.2008.12.001.
RémillardB.LangloisH.HocquardA.PapageorgiouN. (2010). “Optimal hedging of American options in discrete time.” In CarmonaR. A., eds., Numerical Methods in Finance. Berlin: Springer. pp. 145-170.
54.
RochaP.KuhnD. (2012). “Multistage stochastic portfolio optimisation in deregulated electricity markets using linear decision rules.” European Journal of Operational Research216(2): 397-408. http://dx.doi.org/10.1016/j.ejor.2011.08.001.
55.
RosenblattM. (1952). “Remarks on a multivariate transformation.” Annals of Mathematical Statistics23(3): 470-472. http://dx.doi.org/10.1214/aoms/1177729394.
56.
SandaG. E.OlsenE. T.FletenS. E. (2013). “Selective hedging in hydro-based electricity companies.” Energy Economics40(1): 326-338. http://dx.doi.org/10.1016/j.eneco.2013.06.018.
57.
SchweizerM.1995. “Variance-optimal hedging in discrete time.” Mathematics of Operation Research20(1): 1-32. http://dx.doi.org/10.1287/moor.20.1.1.
58.
State of California (2004). Order Instituting Rulemaking to Establish Policies and Cost Recovery Mechanisms for Generation Procurement and Renewable Resource Development Interim Opinion. Public Utility Commission Decision No. 04-01050. http://docs.cpuc.ca.gov/PUBLISHED/FINAL_DECISION/33625.htm
59.
StoftS.BeldenT.GoldmanC.PickleS. (1998). “A primer on electricity futures and other derivatives.” Laurence Berkeley National Laboratory, Technical Report.
60.
TanlapcoE.LawarreJ.LiuC. C. (2002). “Hedging with futures contracts in a deregulated electricity industry.” IEEE Transactions on Power Systems17(3): 577-582. http://dx.doi.org/10.1109/TPWRS.2002.800897.
61.
TorroH. (2011). “Assessing the influence of spot price predictability on electricity futures hedging.” Journal of Risk13(4): 31-61
62.
VehviläinenI.KeppoJ. (2003). “Managing electricity market price risk.” European Journal of Operational Research145(1): 136-147. http://dx.doi.org/10.1016/S0377-2217(01)00399-X.
63.
VehviläainenI.PyykköanenT. (2005). “Stochastic factor model for electricity spot price - the case of the Nordic market.” Energy Economics27(2): 351-367. http://dx.doi.org/10.1016/j.eneco.2005.01.002.
64.
Von der FehrN. H. M.HansenP. V. (2010). “Electricity retailing in Norway.” The Energy Journal31(1): 25-45. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol31-No1-2.
65.
WagnerM.SkantzeP.IlicM. (2003). “Hedging optimization algorithms for deregulated electricity markets.” Proceedings of the 12th conference on intelligent systems application to power systems.
66.
WeronR. (2006). Modeling and Forecasting Electricity Loads and Prices. Chichester: Wiley. http://dx.doi.org/10.1002/9781118673362.
67.
WooC.K.KarimovR.HorowitzI. (2004). “Managing electricity procurement cost and risk by a local distribution company.” Energy Policy32(5): 635-645. http://dx.doi.org/10.1016/S0301-4215(02)00317-8.
68.
ZanottiG.GabbiG.GeranioM. (2010). “Hedging with futures: Efficacy of GARCH correlation models to European electricity markets.” Journal of International Financial Markets, Institutions and Money20(2): 135-148. http://dx.doi.org/10.1016/j.intfin.2009.12.001.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
