Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Energy Systems
Erschienen in: Energy Systems 2/2020

04.01.2019 | Original Paper

Research on optimization of hedging ratio of thermal coal futures in thermal power enterprises based on Delphi method

verfasst von: Yunna Wu, Lingyun Liu, Jianli Zhou, Chenghao Wu, Chuanbo Xu

Erschienen in: Energy Systems | Ausgabe 2/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The enthusiasm of coal-fired thermal power enterprises to participate in coal futures hedging to avoid the risk of coal price fluctuations is increasing. The key to hedge operation is to determine the total coal inventory required by the power generation company according to the amount of electricity generated. Participating in the hedging of coal futures not only plays a role in avoiding the risk of price fluctuation, but also reduces the occupation of funds and improves the utilization ratio of enterprises’ funds. When the total stock of the power generation enterprise has been determined, the optimal ratio of coal futures hedging is calculated scientifically, and then the virtual stock position of coal is calculated, and the actual stock and virtual stock of the coal are reasonably determined. This paper constructs the optimization model of coal hedging ratio of coal-fired thermal power generation enterprises, and innovatively puts forward the Delphi method to determine the three coefficients in the hedging ratio model based on the practical experience.
Vorheriger Artikel Demand response program using incentive and dis-incentive based scheme
Nächster Artikel Effect of the approximation of voltage angle difference on the OPF algorithms in the power network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Lien, D., Kwak, S.: Provisional liquidation of futures hedge programs. Energy Econ. 28(2), 266–273 (2006)CrossRef
2.
Fine, J., Busch, C., Garderet, R.: The upside hedge value of California’s global warming policy given uncertain future oil prices. Energy Policy 44, 46–51 (2012)CrossRef
3.
Chen, S.G., Gu, X.: Research on overconfidence influence of stock index futures hedge. In: International Conference on Management Science & Engineering 2014, pp. 1295–1300
4.
Hsu, C.M.: Forecasting stock/futures prices by using neural networks with feature selection. In: Information Technology and Artificial Intelligence Conference 2011, pp. 1–7
5.
Rohlfs, W., Madlener, R.: Valuation of CCS-ready coal-fired power plants: a multi-dimensional real options approach. Energy Syst. 2(3–4), 243–261 (2011)CrossRef
6.
Lingyun, L., Xiaohu, Z.: Mutation risk of coal-fired double factors and futures and spot hedge strategy in coal-fired power enterprises. Coal Econ. Res. 35(3), 57–61 (2015)
7.
Shang, Y.: US Coal Futures’ References to Chinese Coal Financial Innovation. In: International Conference on Information Management, Innovation Management and Industrial Engineering 2009, pp. 172–175
8.
Shi, J., Xu, T.: Price and volatility dynamics between securitized real estate spot and futures markets. Econ. Model. 35(5), 582–592 (2013)CrossRef
9.
10.
Lee, C.L., Stevenson, S., Lee, M.L.: Futures trading, spot price volatility and market efficiency: evidence from european real estate securities futures. J. Real Estate Financ. Econ. 48(2), 299–322 (2014)CrossRef
11.
Martínez, B., Torró, H.: Hedging spark spread risk with futures. Energy Policy 113, 731–746 (2018)CrossRef
12.
Rosyid, F.A., Adachi, T.: Optimization on long term supply allocation of Indonesian coal to domestic market. Energy Syst. 9(2), 1–30 (2017)
13.
Song, M.L., Song, H., Zhao, J.J., Wang, J.L.: Power supply, coal price, and economic growth in China. Energy Syst. 8(4), 1–20 (2017)CrossRef
14.
Guo, X., Wei, Y., Yuan, J.: Will the steam coal price rebound under the new economy normalcy in China? Energies 9(9), 751 (2016)CrossRef
15.
Burrows, M.: Decentralised energy futures: the changing emissions reduction landscape. Procedia Cirp 29, 138–143 (2015)CrossRef
16.
Connor, L.H.: Energy futures, state planning policies and coal mine contests in rural New South Wales. Energy Policy 99, 233–241 (2016)CrossRef
17.
Shang, Y., Lu, X.: Feasibility Analysis to Establish Coking Coal Futures in China from US Experience. In: International Conference on Industrial Engineering and Engineering Management, 2009. IE&EM 2009, pp. 1978–1982
18.
Talinli, I., Topuz, E., Akbay, M.U.: Comparative analysis for energy production processes (EPPs): sustainable energy futures for Turkey. Energy Policy 38(8), 4479–4488 (2010)CrossRef
19.
Tse, Y.: Price discovery and volatility spillovers in the DJIA index and futures markets. J. Futures Mark. 19(8), 911–930 (1999)CrossRef
20.
Johnson, L.L.: The theory of hedging and speculation in commodity futures. Rev. Econ. Stud. 27(3), 139–151 (1960)CrossRef
21.
Howard, C.T., D'Antonio, L.J.: A risk-return measure of hedging effectiveness. J. Financ Quant. Anal. 19(1), 101–112 (1984)CrossRef
22.
Cecchetti, S.G., Cumby, R.E., Figlewski, S.: Estimation of the optimal futures hedge. Rev. Econ. Stat. 70, 623–630 (1988)CrossRef
23.
Chou, W., Denis, K.F., Lee, C.F.: Hedging with the Nikkei index futures: The convential model versus the error correction model. Q. Rev. Econ. Financ 36(4), 495–505 (1996)CrossRef
24.
Ghosh, A.: Hedging with stock index futures: Estimation and forecasting with error correction model. J. Futures Mark. 13(7), 743–752 (1993)CrossRef
25.
Park, H.Y., Bera, A.K.: Interest‐rate volatility, basis risk and heteroscedasticity in hedging mortgages. Real Estate Econ. 15(2), 79–97 (1987)CrossRef
26.
Hull, R.M.: Leverage ratios, industry norms, and stock price reaction: An empirical investigation of stock-for-debt transactions. Financ Manage. 28(2), 32–45 (1999)CrossRef
27.
28.
29.
Shuran, Z., Dandan, D.: Futures dynamic VaR hedging based on ECM model. Stat. Decis. 13, 150–154 (2013)
30.
Jun, S.: Pricing, Behavior Patterns and Institutions of the Futures Markets: Pricing, Behavior Patterns and Institutions of the Futures Markets. Fudan University Press, Shanghai (2012)
31.
Wang, G.J., Xie, C., He, L.Y., Chen, S.: Detrended minimum-variance hedge ratio: a new method for hedge ratio at different time scales. Phys. A Stat. Mech. Appl. 405(C), 70–79 (2014)CrossRef
32.
Dolatabadi, S., Narayan, P.K., Nielsen, M.Ø., Xu, K.: Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. J. Futures Mark. 38(2), 219–242 (2018)CrossRef
33.
Steurer, J.: The Delphi method: an efficient procedure to generate knowledge. Skelet. Radiol. 40(8), 959–961 (2011)CrossRef
34.
Orsi, F., Geneletti, D., Newton, A.C.: Towards a common set of criteria and indicators to identify forest restoration priorities: an expert panel-based approach. Ecol. Ind. 11(2), 337–347 (2011)CrossRef
35.
Rowe, G., Wright, G.: The Delphi technique: past, present, and future prospects—introduction to the special issue. Technol. Forecast. Soc. Change 78(9), 1487–1490 (2011)CrossRef
36.
Diamond, I.R., Grant, R.C., Feldman, B.M., Pencharz, P.B., Ling, S.C., Moore, A.M., Wales, P.W.: Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies. J. Clin. Epidemiol. 67(4), 401–409 (2014)CrossRef
37.
Gracht, H.A.V.D.: Consensus measurement in Delphi studies: review and implications for future quality assurance. Technol. Forecast. Soc. Change 79(8), 1525–1536 (2012)CrossRef
38.
Linde, E.V.D., Duin, P.V.D.: The Delphi method as early warning: linking global societal trends to future radicalization and terrorism in The Netherlands. Technol. Forecast. Soc. Change 78(9), 1557–1564 (2011)CrossRef
39.
Ray, P.K., Sahu, S.: Productivity management in India: a Delphi study. Int. J. Oper. Prod. Manag. 10(10), 25–51 (1990)CrossRef
Metadaten
Titel
Research on optimization of hedging ratio of thermal coal futures in thermal power enterprises based on Delphi method
verfasst von
Yunna Wu
Lingyun Liu
Jianli Zhou
Chenghao Wu
Chuanbo Xu
Publikationsdatum
04.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 2/2020
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-018-00322-y

Weitere Artikel der Ausgabe 2/2020

Energy Systems 2/2020 Zur Ausgabe

Original Paper

Effect of the approximation of voltage angle difference on the OPF algorithms in the power network

Original Paper

Simulation methods for stochastic storage problems: a statistical learning perspective

Original Paper

Do unit commitment constraints affect generation expansion planning? A scalable stochastic model

OriginalPaper

Detection of power swing and fault occurring simultaneously with power swing using instantaneous frequency

Original Paper

An integrated optimisation model of refinery short-term planning: a case study

Original Paper

Demand response program using incentive and dis-incentive based scheme