Construction of Mixed Derivatives Strategy for Wind Power Producers

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

Deng, S.J.; Oren, S.S. Electricity derivatives and risk management. Energy 2006, 31, 940–953. [CrossRef]

Brockett, P.L.; Wang, M.; Yang, C. Weather derivatives and weather risk management. Risk Manag. Insur. Rev. 2005,

8, 127–140. [CrossRef]

Halkos, G.E.; Tsirivis, A.S. Energy commodities: A review of optimal hedging strategies. Energies 2019, 12, 3979. [CrossRef]

Eydeland, A.; Wolyniec, K. Energy and Power risk Management: New Developments in Modeling, Pricing, and Hedging;

John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2002; Volume 97.

Clewlow, L.; Strickland, C. Energy Derivatives: Pricing and Risk Management; Lacima: Sydney, NS, Australia, 2000.

Wieczorek-Kosmala, M. Weather risk management in energy sector: The Polish case. Energies 2020, 13, 945. [CrossRef]

Alexandridis, A.K.; Zapranis, A.D. Weather Derivatives, Modeling and Pricing Weather-Related Risk; Springer: New York, NY, USA,

2013; ISBN 978-1-4614-6070-1.

Burger, M.; Klar, B.; Muller, A.; Schindlmayr, G.A. spot market model for pricing derivatives in electricity markets. Quant. Financ.

2004, 4, 109–122. [CrossRef]

Jewson, S.; Brix, A. Weather Derivative Valuation: The Meteorological, Statistical, Financial and Mathematical Foundations; Cambridge

University Press: Cambridge, UK, 2005; p. 392.

Roncoroni, A.; Fusai, G.; Cummins, M. Handbook of Multi-Commodity Markets and Products: Structuring, Trading and Risk Management;

John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2015; pp. 255–277.

Berhane, T.; Shibabaw, A.; Awgichew, G.; Walelgn, A. Pricing of weather derivatives based on temperature by obtaining market

risk factor from historical data. Model. Earth Syst. Environ. 2021, 7, 871–884. [CrossRef]

Elias, R.S.; Wahab, M.I.M.; Fang, L. A comparison of regime-switching temperature modeling approaches for applications in

weather derivatives. Eur. J. Oper. Res. 2014, 232, 549–560. [CrossRef]

Zapranis, A.; Alexandridis, A. Modelling the temperature time-dependent speed of mean reversion in the context of weather

derivatives pricing. Appl. Math. Financ. 2008, 15, 355–386. [CrossRef]

Benth, F.E.; Benth, J.Š. The volatility of temperature and pricing of weather derivatives. Quant. Financ. 2007, 7, 553–561. [CrossRef]

Gyamerah, S.A.; Ngare, P.; Ikpe, D. Hedging Crop Yields Against Weather Uncertainties—A Weather Derivative Perspective.

Math. Comput. Appl. 2019, 24, 71. [CrossRef]

Alaton, P.; Djehiche, B.; Stillberger, D. On modelling and pricing weather derivatives. Appl. Math. Financ. 2002, 9, 1–20. [CrossRef]

Boyle, C.F.; Haas, J.; Kern, J.D. Development of an irradiance-based weather derivative to hedge cloud risk for solar energy

systems. Renew. Energy 2021, 164, 1230–1243. [CrossRef]

Mosquera-López, S.; Uribe, J.M. Pricing the risk due to weather conditions in small variable renewable energy projects. Appl.

Energy 2022, 322, 119476. [CrossRef]

Leobacher, G.; Ngare, P. On modelling and pricing rainfall derivatives with seasonality. Appl. Math. Financ. 2011,

18, 71–91. [CrossRef]

Benth, F.E.; Di Persio, L.; Lavagnini, S. Stochastic modeling of wind derivatives in energy markets. Risks 2018, 6, 56. [CrossRef]

Xiao, Y.; Wang, X.; Wang, X.; Wu, Z. Trading wind power with barrier option. Appl. Energy 2016, 182, 232–242. [CrossRef]

Rodríguez, Y.E.; Pérez-Uribe, M.A.; Contreras, J. Wind put barrier options pricing based on the Nordix index. Energies 2021,

14, 1177. [CrossRef]

Masala, G.; Micocci, M.; Rizk, A. Hedging Wind Power Risk Exposure through Weather Derivatives. Energies 2022, 15, 1343. [CrossRef]

Kanamura, T.; Homann, L.; Prokopczuk, M. Pricing analysis of wind power derivatives for renewable energy risk management.

Appl. Energy 2021, 304, 117827. [CrossRef]

Christensen, T.S.; Pircalabu, A. On the spatial hedging effectiveness of German wind power futures for wind power generators.

J. Energy Mark. 2018, 11, 71–96. [CrossRef]

Benth, F.E.; Pircalabu, A. A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures. Appl. Math. Financ. 2018,

25, 36–65. [CrossRef]

Gersema, G.; Wozabal, D. An equilibrium pricing model for wind power futures. Energy Econ. 2017, 65, 64–74. [CrossRef]

Thomaidis, N.S.; Christodoulou, T.; Santos-Alamillos, F.J. Handling the risk dimensions of wind energy generation. Appl. Energy

2023, 339, 120925. [CrossRef]

Oum, Y.; Oren, S.; Deng, S. Hedging quantity risks with standard power options in a competitive wholesale electricity market.

Nav. Res. Logist. 2006, 53, 697–712. [CrossRef]

Oum, Y.; Oren, S. VaR constrained hedging of fixed Price load-following obligations in competitive electricity markets. J. Risk

Decision Anal. 2009, 1, 43–56. [CrossRef]

Oum, Y.; Oren, S. Optimal static hedging of volumetric risk in a competitive wholesale electricity market. Decis. Anal. 2010,

7, 107–122. [CrossRef]

Yamada, Y. Valuation and hedging of weather derivatives on monthly average temperature. J. Risk 2007, 10, 101–125. [CrossRef]

Yamada, Y. Optimal Hedging of Prediction Errors Using Prediction Errors. Asia Pac. Financ. Mark. 2008, 15, 67–95. [CrossRef]

Matsumoto, T.; Yamada, Y. Simultaneous hedging strategy for price and volume risks in electricity businesses using energy and

weather derivatives. Energy Econ. 2021, 95, 105101. [CrossRef]

Matsumoto, T.; Yamada, Y. Customized yet Standardized temperature derivatives: A non-parametric approach with suitable

basis selection for ensuring robustness. Energies 2021, 14, 3351. [CrossRef]

Energies 2023, 16, 3809

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

26 of 26

Yamada, Y.; Matsumoto, T. Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on

Power Markets. Energies 2021, 14, 7311. [CrossRef]

Matsumoto, T.; Yamada, Y. Improving the Efficiency of Hedge Trading Using High-er-Order Standardized Weather Derivatives

for Wind Power. Energies 2023, 16, 3112. [CrossRef]

Coulon, M.; Powell, W.B.; Sircar, R. A model for hedging load and price risk in the Texas electricity market. Energy Econ. 2013,

40, 976–988. [CrossRef]

Bhattacharya, S.; Gupta, A.; Kar, K.; Owusu, A. Risk management of renewable power producers from co-dependencies in

cashflows. Eur. J. Oper. Res. 2020, 283, 1081–1093. [CrossRef]

Kaufmann, J.; Kienscherf, P.A.; Ketter, W. Modeling and managing joint price and volumetric risk for volatile electricity portfolios.

Energies 2020, 13, 3578. [CrossRef]

Benth, F.E.; Lange, N.; Myklebust, T.A. Pricing and hedging quanto options in energy markets. J. Energy Mark. 2015,

8, 1–35. [CrossRef]

Caporin, M.; Pre´s, J.; Torro, H. Model based Monte Carlo pricing of energy and temperature quanto options. Energy Econ. 2012,

34, 1700–1712. [CrossRef]

Ministry of Economy, Trade and Industry; METI. Present Status and Promotion Measures for the Introduction of Renewable

Energy in Japan. 2011. Available online: https://www.meti.go.jp/english/policy/energy_environment/renewable/index.html

(accessed on 17 March 2023).

Nasdaq. Wind Power Futures Based on the German Wind Index NAREX WIDE. 2023. Available online: https://www.nasdaq.

com/solutions/wind-power-futuresl (accessed on 17 March 2023).

Japan Electric Power Information Center, Inc.; JEPIC. The Electric Power Industry in Japan. 2022. Available online: https:

//en.wikipedia.org/wiki/Feed-in_premium (accessed on 17 March 2023).

Betz, A. Wind-Energie und ihre Ausnutzung durch Windmühlen; Vandenhoek and Ruprecht: Göttingen, Germany, 1926; 64p.

Villanueva, D.; Feijóo, A. Wind power distributions: A review of their applications. Renew. Sustain. Energy Rev. 2010,

14, 1490–1495. [CrossRef]

Hanifi, S.; Liu, X.; Lin, Z.; Lotfian, S. A critical review of wind power forecasting methods—Past, present and future. Energies

2020, 13, 3764. [CrossRef]

Wood, S.N. Package ‘mgcv’ v. 1.8–42. 2023. Available online: https://cran.r-project.org/web/packages/mgcv/mgcv.pdf

(accessed on 25 March 2023).

Hastie, T.; Tibshirani, R. Generalized Additive Models; Chapman & Hall: Boca Raton, FL, USA, 1990.

Wood, S.N. Generalized Additive Models: An Introduction with R, 2nd ed.; Chapman and Hall: New York, NY, USA, 2017.

Wood, S.N.; Goude, Y.; Fasiolo, M. Interpretability in Generalized Additive Models. In Interpretability for Industry 4.0: Statistical

and Machine Learning Approaches; Springer: Cham, Switzerland, 2022.

Matsumoto, T.; Yamada, Y. Comprehensive and Comparative Analysis of GAM-Based PV Power Forecasting Models Using

Multidimensional Tensor Product Splines against Machine Learning Techniques. Energies 2021, 14, 7146. [CrossRef]

Liu, H.; Chen, C. Data processing strategies in wind energy forecasting models and applications: A comprehensive review. Appl.

Energy 2019, 249, 392–408.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual

author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to

people or property resulting from any ideas, methods, instructions or products referred to in the content.

...