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All issues Volume 24 (2020) ESAIM: PS, 24 (2020) 963-1002 References
Open Access
Issue
ESAIM: PS
Volume 24, 2020
Page(s) 963 - 1002
DOI https://doi.org/10.1051/ps/2020027
Published online 15 December 2020
  1. R. Aid, L. Campi, A.N. Huu and N. Touzi, A structural risk-neutral model of electricity prices. Int. J. Theor. Appl. Finance 12 (2009) 925–947. [CrossRef] [Google Scholar]
  2. Y. Aït-Sahalia and J. Jacod, Testing for jumps in a discretely observed process. Ann. Stat. 37 (2009) 184–222. [CrossRef] [Google Scholar]
  3. Y. Aït-Sahalia and J. Jacod, High-frequency financial econometrics. Princeton University Press (2014). [Google Scholar]
  4. O.E. Barndorff-Nielsen, N. Shephard and M. Winkel, Limit theorems for multipower variation in the presence of jumps. Stochastic Process. Appl. 116 (2006) 796–806. [CrossRef] [Google Scholar]
  5. F.E. Benth, J. Kallsen and T. Meyer-Brandis, A non-Gaussian Ornstein–Uhlenbeck process for electricity spot price modeling and derivatives pricing. Appl. Math. Finance 14 (2007) 153–169. [CrossRef] [Google Scholar]
  6. F.E. Benth, R. Kiesel and A. Nazarova, A critical empirical study of three electricity spot price models. Energy Econ. 34 (2012) 1589–1616. [CrossRef] [Google Scholar]
  7. H. Biermé and A. Desolneux, A Fourier approach for the level crossings of shot noise processes with jumps. J. Appl. Probab. 49 (2012) 100–113. [CrossRef] [Google Scholar]
  8. P.J. Brockwell, R.A. Davis and Y. Yang, Estimation for nonnegative lévy-driven Ornstein-Uhlenbeck processes. J. Appl. Probab. 44 (2007) 977–989. [CrossRef] [Google Scholar]
  9. A. Cartea and M.G. Figueroa, Pricing in electricity markets: a mean reverting jump diffusion model with seasonality. Appl. Math. Finance 12 (2005) 313–335. [CrossRef] [Google Scholar]
  10. L. Clewlow and C. Strickland, Valuing energy options in a one factor model fitted to forward prices. Available at SSRN 160608 (1999). [Google Scholar]
  11. L. Clewlow, C. Strickland et al., A multi-factor model for energy derivatives. Technical report (1999). [Google Scholar]
  12. R. Cont and P. Tankov, Vol. 2 of Financial modelling with jump processes. CRC Press (2003). [Google Scholar]
  13. O. Féron and E. Daboussi, Calibration of electricity price models. In Commodities, Energy and Environmental Finance. Springer (2015) 183–207. [CrossRef] [Google Scholar]
  14. H. Föllmer and M. Schweizer, Hedging of contingent claims under incomplete information. Appl. Stochastic Anal. Stochastic Monographs 5 (1991) 389–414. [Google Scholar]
  15. H. Geman and A. Roncoroni, Understanding the fine structure of electricity prices. J. Bus. 79 (2006) 1225–1261. [CrossRef] [Google Scholar]
  16. J. Gonzalez, J. Moriarty and J. Palczewski, Bayesian calibration and number of jump components in electricity spot price models. Energy Econ. 65 (2017) 375–388. [CrossRef] [Google Scholar]
  17. B. Hambly, S. Howison and T. Kluge, Modelling spikes and pricing swing options in electricity markets. Quant. Finance 9 (2009) 937–949. [CrossRef] [Google Scholar]
  18. J. Jacod and P. Protter, Vol. 67 of Discretization of processes. Springer Science & Business Media (2011). [Google Scholar]
  19. R. Kiesel, G. Schindlmayr and R.H. Börger, A two-factor model for the electricity forward market. Quant. Finance 9 (2009) 279–287. [CrossRef] [Google Scholar]
  20. C. Klüppelberg, T. Meyer-Brandis and A. Schmidt, Electricity spot price modelling with a view towards extreme spike risk. Quant. Finance 10 (2010) 963–974. [CrossRef] [Google Scholar]
  21. S. Koekebakker and F. Ollmar, Forward curve dynamics in the nordic electricity market. Manag. Finance 31 (2005) 73–94. [Google Scholar]
  22. J.J. Lucia and E.S. Schwartz, Electricity prices and power derivatives: Evidence from the Nordic power exchange. Rev. Derivat. Res. 5 (2002 5–50. [CrossRef] [Google Scholar]
  23. C. Mancini, Estimation of the characteristics of the jumps of a general Poisson-diffusion model. Scand. Actuar. J. 2004 (2004) 42–52. [CrossRef] [Google Scholar]
  24. R.C. Merton, Option pricing when underlying stock returns are discontinuous. J. Fin. Econ. 3 (1976) 125–144. [CrossRef] [Google Scholar]
  25. T. Meyer-Brandis and M. Morgan, A dynamic lévy copula model for the spark spread. In Quantitative Energy Finance. Springer (2014) 237–257. [CrossRef] [Google Scholar]
  26. T. Meyer-Brandis and P. Tankov, Multi-factor jump-diffusion models of electricity prices. Int. J. Theor. Appl. Finance 11 (2008) 503–528. [CrossRef] [Google Scholar]
  27. M. Moreno, P. Serrano and W. Stute, Statistical properties and economic implications of jump-diffusion processes with shot-noise effects. Eur. J. Oper. Res. 214 (2011) 656–664. [CrossRef] [Google Scholar]
  28. G. Peccati and M.S. Taqqu, Central limit theorems for double Poisson integrals. Bernoulli 14 (2008) 791–821. [CrossRef] [Google Scholar]
  29. P. Protter and K. Shimbo, No arbitrage and general semimartingales. In Markov processes and related topics: a Festschrift for Thomas G. Kurtz. Institute of Mathematical Statistics (2008) 267–283. [CrossRef] [Google Scholar]
  30. T. Schmidt, Modelling energy markets with extreme spikes. In Mathematical Control Theory and Finance. Springer (2008) 359–375. [CrossRef] [Google Scholar]
  31. E. Schwartz and J.E. Smith, Short-term variations and long-term dynamics in commodity prices. Manag. Sci. 46 (2000) 893–911. [CrossRef] [Google Scholar]
  32. M. Schweizer, A guided tour through quadratic hedging approaches. In Handbooks in mathematical finance: Option pricing, interest rates and risk management. Cambridge University Press (2001) 538–574. [CrossRef] [Google Scholar]
  33. A.E.D. Veraart, Inference for the jump part of quadratic variation of Itô semimartingales. Econ. Theory 26 (2010) 331–368. [CrossRef] [Google Scholar]
  34. X. Warin, Gas storage hedging. In Numerical Methods in Finance. Springer (2012) 421–445. [CrossRef] [Google Scholar]

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