Fabrizio Durante, Angelica Gianfreda, Francesco Ravazzolo, Luca Rossini
arXiv 4 Jan 2022 · Statistics — Applications · publishedInformation Sciences (2022) · 30 citations (OpenAlex)
arXiv:2201.01132 · PDF · DOI · OpenAlex · Extracted main text
This paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model {while studying Germany, the widest studied market in Europe}. The inter-dependencies are investigated in-depth and monitored over time, with particular emphasis on the tail behavior. To this end, suitable tail dependence measures are introduced to take into account a multivariate extreme scenario appropriately identified {through the} Kendall's distribution function. The empirical evidence demonstrates a strong association between electricity prices, renewable energy sources, and demand within a day and over the studied years. Hence, this analysis provides guidance for further and different incentives for promoting green energy generation while considering the time-varying dependencies of the involved variables
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Patton, A. J (2012) A review of copula models for economic time series | 0.644 | 4 | 1 | 100% |
| 2 | Bernardi, M., F. Durante, P. Jaworski, L. Petrella, and G. Salvadori (2018) Conditional risk based on multivariate hazard scenarios | 0.644 | 2 | 2 | 100% |
| 3 | Nappo, G. and F. Spizzichino (2009) Kendall distributions and level sets in bivariate exchangeable survival models | 0.644 | 2 | 2 | 100% |
| 4 | Pircalabu, A., T. Hvolby, J. Jung, and E. Hg (2017) Joint price and volumetric risk in wind power trading: A copula approach | 0.644 | 2 | 2 | 100% |
| 5 | Salvadori, G., C. De Michele, and F. Durante (2011) On the return period and design in a multivariate framework | 0.644 | 2 | 2 | 100% |
| 6 | Czado, C (2019) Analyzing dependent data with vine copulas. A practical guide with R, Volume 222 | 0.585 | 3 | 1 | 100% |
| 7 | Durante, F. and C. Sempi (2016) Principles of Copula Theory self | 0.585 | 3 | 1 | 100% |
| 8 | Durante, F., J. Fernández-Sánchez, and R. Pappadà (2015) Copulas, diagonals and tail dependence self | 0.511 | 2 | 1 | 100% |
| 9 | Hofert, M., W. Oldford, A. Prasad, and M. Zhu (2019) A framework for measuring association of random vectors via collapsed random variables | 0.511 | 2 | 1 | 100% |
| 10 | Aas, K (2016) Pair-copula constructions for financial applications: A review | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Modeling European Electricity Market Integration during turbulent times | 0.405 | 1 | 1 |