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GARCH-based put option valuation to maximize benefit of wind investors

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  • Contreras, Javier
  • Rodríguez, Yeny E.

Abstract

A method based on Empirical Martingale Simulation (EMS) is presented to evaluate investments in wind energy. Risk-neutral prices are calculated, where electricity market prices are modeled using an ARIMA–GARCH method which shows conditional heteroskedasticity. The values of the put options are calculated a week ahead and it is observed that wind producers that invest in the options market can hedge against price risk and can also maximize their benefits. The use of Monte Carlo simulation with the EMS method in periods of high volatility is especially useful for investors facing price volatilities in order to improve their returns. The model is applied to the Colombian electricity market.

Suggested Citation

  • Contreras, Javier & Rodríguez, Yeny E., 2014. "GARCH-based put option valuation to maximize benefit of wind investors," Applied Energy, Elsevier, vol. 136(C), pages 259-268.
    • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:259-268
    DOI: 10.1016/j.apenergy.2014.08.085
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    as
    1. Leloux, Jonathan & Lorenzo, Eduardo & García-Domingo, Beatriz & Aguilera, Jorge & Gueymard, Christian A., 2014. "A bankable method of assessing the performance of a CPV plant," Applied Energy, Elsevier, vol. 118(C), pages 1-11.
    2. Wang, Chou-Wen & Wu, Chin-Wen & Tzang, Shyh-Weir, 2012. "Implementing option pricing models when asset returns follow an autoregressive moving average process," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 8-25.
    3. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    4. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Study on offshore wind power potential and wind farm optimization in Hong Kong," Applied Energy, Elsevier, vol. 130(C), pages 519-531.
    5. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3459-3476.
    6. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    7. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    8. Shcherbakova, Anastasia & Kleit, Andrew & Cho, Joohyun, 2014. "The value of energy storage in South Korea’s electricity market: A Hotelling approach," Applied Energy, Elsevier, vol. 125(C), pages 93-102.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    11. YongChern Su & MingDa Chen & HanChing Huang, 2010. "An application of closed-form GARCH option-pricing model on FTSE 100 option and volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 20(11), pages 899-910.
    12. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    13. Coll-Mayor, Debora & Paget, Mia & Lightner, Eric, 2007. "Future intelligent power grids: Analysis of the vision in the European Union and the United States," Energy Policy, Elsevier, vol. 35(4), pages 2453-2465, April.
    14. Jin-Chuan Duan & Jean-Guy Simonato, 1998. "Empirical Martingale Simulation for Asset Prices," Management Science, INFORMS, vol. 44(9), pages 1218-1233, September.
    15. Chung-Li Tseng & Kyle Y. Lin, 2007. "A Framework Using Two-Factor Price Lattices for Generation Asset Valuation," Operations Research, INFORMS, vol. 55(2), pages 234-251, April.
    16. Baringo, L. & Conejo, A.J., 2011. "Wind power investment within a market environment," Applied Energy, Elsevier, vol. 88(9), pages 3239-3247.
    17. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    18. Fleten, S.-E. & Maribu, K.M. & Wangensteen, I., 2007. "Optimal investment strategies in decentralized renewable power generation under uncertainty," Energy, Elsevier, vol. 32(5), pages 803-815.
    19. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    20. Kucuksari, Sadik & Khaleghi, Amirreza M. & Hamidi, Maryam & Zhang, Ye & Szidarovszky, Ferenc & Bayraksan, Guzin & Son, Young-Jun, 2014. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments," Applied Energy, Elsevier, vol. 113(C), pages 1601-1613.
    21. Katsaprakakis, Dimitris Al. & Christakis, Dimitris G. & Pavlopoylos, Kosmas & Stamataki, Sofia & Dimitrelou, Irene & Stefanakis, Ioannis & Spanos, Petros, 2012. "Introduction of a wind powered pumped storage system in the isolated insular power system of Karpathos–Kasos," Applied Energy, Elsevier, vol. 97(C), pages 38-48.
    22. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 585-625.
    23. Haldrup Niels & Nielsen Morten Ø., 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-24, September.
    24. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    25. J. Muñoz & J. Contreras & J. Caamaño & P. Correia, 2011. "A decision-making tool for project investments based on real options: the case of wind power generation," Annals of Operations Research, Springer, vol. 186(1), pages 465-490, June.
    26. Lars Stentoft, 2008. "Option Pricing using Realized Volatility," CREATES Research Papers 2008-13, Department of Economics and Business Economics, Aarhus University.
    27. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    28. Jin‐Chuan Duan, 1995. "The Garch Option Pricing Model," Mathematical Finance, Wiley Blackwell, vol. 5(1), pages 13-32, January.
    29. repec:dau:papers:123456789/1433 is not listed on IDEAS
    30. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    31. 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.
    32. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
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    8. Kitzing, Lena & Juul, Nina & Drud, Michael & Boomsma, Trine Krogh, 2017. "A real options approach to analyse wind energy investments under different support schemes," Applied Energy, Elsevier, vol. 188(C), pages 83-96.

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