Prof. Dr. Florentina Paraschiv

Interessensgebiete
Energie:

• Modellierung von Strom-, Öl- und Gas-Märkten
• Risikomodellierung für Strom-Märkte
• Kraftwerksoptimierung
• Erneuerbare Energien

Banken und Finanzen:

• Finanzmarkt-Regulierung
• Liquiditätsrisiko
• Stresstest

Lehre

• Price Dynamics in Energy Markets and their Interdependencies
• Quantitative Aspects of Financial Regulation

Publikationen

Für eine aktuelle Publikationsliste siehe Lebenslauf in den Downloads

• Webinar Florentina Paraschiv, University of Cambridge, Isaac Newton Institute, MES programme: "Econometrics of Intraday Electricity Prices". (video)

• Spada, Matteo; Paraschiv, Florentina; Burgherr, Peter (2018): A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies. In: Energy 154, S. 277–288.

• Kiesel, Ruediger; Paraschiv, Florentina; Sætherø, Audun. (2018) On the Construction of Hourly Price Forward Curves for Electricity Prices. Computational Management Science.
• Spada, M., Paraschiv, F., Burgherr, P. (2017). A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies, under review in Energy.
• Benth, F.E. & Paraschiv, F. (2017). A structural model for electricity forward prices, Journal of Banking and Finance, forthcoming, Best Paper Award, Energy and Commodity Finance Conference, Paris, 2016.
• Kiesel, R. & Paraschiv, F. (2017). Econometric analysis of 15-minute intraday electricity prices, under review in Energy Economics, 64, 77—90.
• Paraschiv, F., Bunn, D. & Westgaard, S. (2017). A fully parametric approach for quantile regressions with time-varying coefficients, under review available at: http://bit.ly/1sxA9Sf
• Aepli, M.D., Frauendorfer, K., Füss, R., Paraschiv, F., (2016). The Predictive Power of Multivariate Dynamic Copula Models, under review, available at: http://bit.ly/1sMJsyo
• Hagfors, L.I., Paraschiv, F., Prokopczuk, M. & Westgaard, S. (2016) Prediction of extreme price occurrences in the German day-ahead electricity market, Quantitative Finance, http://dx.doi.org/10.1080/14697688.2016.1211794
• Hagfors, L.I., Molnar, P., Paraschiv, F. & Westgaard, S. (2016). Using quantile regression to analyze the effect of renewables on EEX price formation, Renewable Energy and Environmental Sustainability, 32(1), DOI: 10.1051/rees/2016036 .
• Keles, D., Scelle, J., Paraschiv, F. & Fichtner, W. (2015). Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks (ANN), Applied Energy, 162, 218—230.
• Paraschiv, F., Hadzi-Mishev, R., & Keles, D., (2015). Extreme Value Theory for heavy-tails in electricity prices. Journal of Energy Markets, forthcoming.
• Paraschiv, F., Mudry, P.-A. & Andries, A., (2015). Stress testing techniques for portfolios of commodity futures, using extreme-value theory and copulas, Economic Modelling, 50, 9—18.
• Paraschiv, F., Fleten, S.-E. & Schürle, M. (2015). A spot-forward model for electricity prices with regime shifts. Energy Economics, 47, 142—153, doi:10.1016/j.eneco.2014.11.003 .
• Paraschiv, F., Erni, D. & Pietsch, R. (2014). The impact of renewable energies on EEX day-ahead electricity prices. Energy Policy, 73, 196—210, http://dx.doi.org/10.1016/j.enpol.2014.05.004 . 
• Kovacevic, R., & Paraschiv, F. (2014). Medium-term planning for thermal electricity production. OR Spectrum, 36(3), 723–759. (Best Paper Award, Conference Energy Finance, Essen 2013). 
• Daviou, A. & Paraschiv, F. (2014). Investors’ behavior under changing market volatility. Journal of Investing, 23(2), 96–113. 
• Celik, G., Frauendorfer, K. & Paraschiv, F. (2014). Joint dynamics of European and American oil prices. In M. Prokopczuk (ed.): Energy Pricing Models: Recent Advances, Methods, and Tools, published by Palgrave Macmillan, NY (forthcoming). 
• Mudry, P.-A. & Paraschiv, F. (2014). Stress testing techniques for portfolios of commodity futures, using extreme-value theory and copulas. In R.J. Fonseca et al. (eds.): Computational Management Science. Lecture Notes in Economics and Mathematical Systems, 682, DOI 10.1007/978-3-319-20430-7_3