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Brenda López-Cabrera
(Brenda Lopez Cabrera)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Benschop, Thijs & López Cabrera, Brenda, 2017. "Realized volatility of CO₂ futures," SFB 649 Discussion Papers 2017-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Simone Serafini & Giacomo Bormetti, 2025. "Pricing Carbon Allowance Options on Futures: Insights from High-Frequency Data," Papers 2501.17490, arXiv.org, revised May 2025.

  2. Benschop, Thijs & López Cabrera, Brenda, 2017. "Realized volatility of CO₂ futures," SFB 649 Discussion Papers 2017-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Simone Serafini & Giacomo Bormetti, 2025. "Pricing Carbon Allowance Options on Futures: Insights from High-Frequency Data," Papers 2501.17490, arXiv.org, revised May 2025.

  3. López Cabrera, Brenda & Schulz, Franziska, 2014. "Forecasting generalized quantiles of electricity demand: A functional data approach," SFB 649 Discussion Papers 2014-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," Applied Energy, Elsevier, vol. 301(C).
    2. Petra Burdejová & Wolfgang K. Härdle, 2019. "Dynamic semi-parametric factor model for functional expectiles," Computational Statistics, Springer, vol. 34(2), pages 489-502, June.
    3. Qinyu Wu & Fan Yang & Ping Zhang, 2023. "Conditional generalized quantiles based on expected utility model and equivalent characterization of properties," Papers 2301.12420, arXiv.org.
    4. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    5. Luke Durell & J. Thad Scott & Douglas Nychka & Amanda S. Hering, 2023. "Functional forecasting of dissolved oxygen in high‐frequency vertical lake profiles," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.
    6. Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020. "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers 2010.08102, arXiv.org.
    7. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Shirun Shen & Huiya Zhou & Kejun He & Lan Zhou, 2024. "Principal Component Analysis of Two-dimensional Functional Data with Serial Correlation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 601-620, September.
    9. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University.
    10. Nickelsen, Daniel & Müller, Gernot, 2025. "Bayesian hierarchical probabilistic forecasting of intraday electricity prices," Applied Energy, Elsevier, vol. 380(C).
    11. Yu, Miao & Sibbertsen, Philipp, 2026. "Mapping price dynamics across electricity market designs: A functional data approach with STL decomposition," Applied Energy, Elsevier, vol. 407(C).
    12. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," LSE Research Online Documents on Economics 120774, London School of Economics and Political Science, LSE Library.
    13. Torben G. Andersen & Yingwen Tan & Viktor Todorov & Zhiyuan Zhang, 2025. "Testing mean stationarity of intraday volatility curves," Quantitative Economics, Econometric Society, vol. 16(3), pages 1059-1091, July.

  4. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2014. "A consistent two-factor model for pricing temperature derivatives," SFB 649 Discussion Papers 2014-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Yeny E. Rodríguez & Miguel A. Pérez-Uribe & Javier Contreras, 2021. "Wind Put Barrier Options Pricing Based on the Nordix Index," Energies, MDPI, vol. 14(4), pages 1-14, February.
    2. Peng Li, 2021. "The Valuation of Weather Derivatives Using One Sided Crank–Nicolson Schemes," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 825-847, October.
    3. Cui, Hairong & Zhou, Ying & Dzandu, Michael D. & Tang, Yinshan & Lu, Xunfa, 2019. "Is temperature-index derivative suitable for China?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Alessio Giorgini & Rogemar S. Mamon & Marianito R. Rodrigo, 2021. "A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives," Mathematics, MDPI, vol. 9(22), pages 1-15, November.
    5. Matsumoto, Takuji & Yamada, Yuji, 2025. "Advancing financial instruments and market trading framework for local solar power hedging with principal component derivatives," Energy Economics, Elsevier, vol. 149(C).
    6. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.
    7. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    8. Melzer, Awdesch & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers 2017-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Yuji Yamada & Takuji Matsumoto, 2021. "Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on Power Markets," Energies, MDPI, vol. 14(21), pages 1-28, November.

  5. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    2. Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    3. Tajeddin, Alireza & Fazelpour, Farivar, 2016. "Towards realistic design of wind dams: An innovative approach to enhance wind potential," Applied Energy, Elsevier, vol. 182(C), pages 282-298.
    4. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    6. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    7. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    8. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    10. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    11. Mohamed Elnaggar & Ezzaldeen Edwan & Matthias Ritter, 2017. "Wind Energy Potential of Gaza Using Small Wind Turbines: A Feasibility Study," Energies, MDPI, vol. 10(8), pages 1-13, August.
    12. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    13. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    14. Alina Wilke & Paul J.J. Welfens, 2020. "Urban Wind Energy Production Potential: New Opportunities," EIIW Discussion paper disbei287, Universitätsbibliothek Wuppertal, University Library.
    15. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    17. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    18. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

  6. Benschopa, Thijs & López Cabreraa, Brenda, 2014. "Volatility modelling of CO₂ emission allowance spot prices with regime-switching GARCH models," SFB 649 Discussion Papers 2014-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    2. Cong, Ren & Lo, Alex Y., 2017. "Emission trading and carbon market performance in Shenzhen, China," Applied Energy, Elsevier, vol. 193(C), pages 414-425.
    3. Bingzi Jin & Xiaojie Xu, 2026. "Employing Gaussian process regression with Bayesian inference to predict the living-materials producer price index in China," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(2), pages 4091-4129, April.
    4. Hartvig, Áron Dénes & Pap, Áron & Pálos, Péter, 2023. "EU Climate Change News Index: Forecasting EU ETS prices with online news," Finance Research Letters, Elsevier, vol. 54(C).
    5. Muğaloğlu, Erhan & Kılıç, Edanur & Keskin, Hazar & Buğra Selçuklu, Saltuk, 2026. "Dynamics of electricity price volatility and its impacts on energy investments," Renewable Energy, Elsevier, vol. 256(PF).
    6. Benschop, Thijs & López Cabrera, Brenda, 2017. "Realized volatility of CO₂ futures," SFB 649 Discussion Papers 2017-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    8. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.

  7. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2014. "A consistent two-factor model for pricing temperature derivatives," SFB 649 Discussion Papers 2014-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Yeny E. Rodríguez & Miguel A. Pérez-Uribe & Javier Contreras, 2021. "Wind Put Barrier Options Pricing Based on the Nordix Index," Energies, MDPI, vol. 14(4), pages 1-14, February.
    2. Peng Li, 2021. "The Valuation of Weather Derivatives Using One Sided Crank–Nicolson Schemes," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 825-847, October.
    3. Cui, Hairong & Zhou, Ying & Dzandu, Michael D. & Tang, Yinshan & Lu, Xunfa, 2019. "Is temperature-index derivative suitable for China?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Alessio Giorgini & Rogemar S. Mamon & Marianito R. Rodrigo, 2021. "A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives," Mathematics, MDPI, vol. 9(22), pages 1-15, November.
    5. Matsumoto, Takuji & Yamada, Yuji, 2025. "Advancing financial instruments and market trading framework for local solar power hedging with principal component derivatives," Energy Economics, Elsevier, vol. 149(C).
    6. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.
    7. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    8. Melzer, Awdesch & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers 2017-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Yuji Yamada & Takuji Matsumoto, 2021. "Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on Power Markets," Energies, MDPI, vol. 14(21), pages 1-28, November.

  8. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    2. Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    3. Tajeddin, Alireza & Fazelpour, Farivar, 2016. "Towards realistic design of wind dams: An innovative approach to enhance wind potential," Applied Energy, Elsevier, vol. 182(C), pages 282-298.
    4. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    6. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    7. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    8. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    10. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    11. Mohamed Elnaggar & Ezzaldeen Edwan & Matthias Ritter, 2017. "Wind Energy Potential of Gaza Using Small Wind Turbines: A Feasibility Study," Energies, MDPI, vol. 10(8), pages 1-13, August.
    12. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    13. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    14. Alina Wilke & Paul J.J. Welfens, 2020. "Urban Wind Energy Production Potential: New Opportunities," EIIW Discussion paper disbei287, Universitätsbibliothek Wuppertal, University Library.
    15. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    17. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    18. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

  9. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall derivatives at the CME," SFB 649 Discussion Papers 2013-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Evarest Emmanuel & Berntsson Fredrik & Singull Martin & Yang Xiangfeng, 2018. "Weather derivatives pricing using regime switching model," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 13-27, March.
    2. Andrea Martínez Salgueiro & Maria-Antonia Tarrazon-Rodon, 2021. "Weather derivatives to mitigate meteorological risks in tourism management: An empirical application to celebrations of Comunidad Valenciana (Spain)," Tourism Economics, , vol. 27(4), pages 591-613, June.
    3. Nelson Christopher Dzupire & Philip Ngare & Leo Odongo, 2019. "Pricing Basket Weather Derivatives on Rainfall and Temperature Processes," IJFS, MDPI, vol. 7(3), pages 1-14, June.
    4. Andrea Martínez Salgueiro & Maria-Antonia Tarrazon-Rodon, 2020. "Approaching rainfall-based weather derivatives pricing and operational challenges," Review of Derivatives Research, Springer, vol. 23(2), pages 163-190, July.
    5. Bressan, Giacomo Maria & Romagnoli, Silvia, 2021. "Climate risks and weather derivatives: A copula-based pricing model," Journal of Financial Stability, Elsevier, vol. 54(C).
    6. Friedrich Poeschel, 2013. "Assortative matching through signals," 2013 Papers ppo178, Job Market Papers.

  10. Härdle, Wolfgang Karl & López-Cabrera, Brenda & Teng, Huei-wen, 2013. "State Price Densities implied from weather derivatives," SFB 649 Discussion Papers 2013-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.
    2. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    3. Xixuan Han & Boyu Wei & Hailiang Yang, 2018. "Index Options And Volatility Derivatives In A Gaussian Random Field Risk-Neutral Density Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-41, June.
    4. Friedrich Poeschel, 2013. "Assortative matching through signals," 2013 Papers ppo178, Job Market Papers.

  11. López Cabrera, Brenda & Schulz, Franziska, 2013. "Volatility linkages between energy and agricultural commodity prices," SFB 649 Discussion Papers 2013-042, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    2. Vo, Long Hai & Le, Thai-Ha, 2021. "Eatery, energy, environment and economic system, 1970–2017: Understanding volatility spillover patterns in a global sample," Energy Economics, Elsevier, vol. 100(C).
    3. Yousaf, Imran & Abrar, Afsheen & Ali, Shoaib & Goodell, John W., 2024. "Connectedness between energy cryptocurrencies and US equity markets: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. Anthony N. Rezitis & Panagiotis Andrikopoulos & Theodoros Daglis, 2024. "Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 451-483, March.
    5. Hokey Min, 2022. "Examining the Impact of Energy Price Volatility on Commodity Prices from Energy Supply Chain Perspectives," Energies, MDPI, vol. 15(21), pages 1-16, October.
    6. Harun Uçak & Irfan Ullah & Yakup Ari, 2024. "The volatility connectedness between fertilizers and rice price: evidences from the global major rice-producing countries," Asia-Pacific Journal of Regional Science, Springer, vol. 8(1), pages 239-263, March.
    7. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2021. "The realized volatility of commodity futures: Interconnectedness and determinants#," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 139-151.
    8. Ding, Shusheng & Cui, Tianxiang & Zheng, Dandan & Du, Min, 2021. "The effects of commodity financialization on commodity market volatility," Resources Policy, Elsevier, vol. 73(C).
    9. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    10. M. Thenmozhi & Shipra Maurya, 2020. "Crude Oil Volatility Transmission Across Food Commodity Markets: A Multivariate BEKK-GARCH Approach," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 20(2), pages 131-164, August.
    11. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    12. Tule, Moses K. & Salisu, Afees A. & Chiemeke, Charles C., 2019. "Can agricultural commodity prices predict Nigeria's inflation?," Journal of Commodity Markets, Elsevier, vol. 16(C).
    13. Han, Liyan & Jin, Jiayu & Wu, Lei & Zeng, Hongchao, 2020. "The volatility linkage between energy and agricultural futures markets with external shocks," International Review of Financial Analysis, Elsevier, vol. 68(C).
    14. Yahya, Muhammad & Oglend, Atle & Dahl, Roy Endré, 2019. "Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach," Energy Economics, Elsevier, vol. 80(C), pages 277-296.
    15. Miroslava Ivanova & Lilko Dospatliev, 2023. "Effects of Diesel Price on Changes in Agricultural Commodity Prices in Bulgaria," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    16. Zhang, Wei & Wu, Jiayi & Wang, Shun & Zhang, Yong, 2025. "Examining dynamics: Unraveling the impact of oil price fluctuations on forecasting agricultural futures prices," International Review of Financial Analysis, Elsevier, vol. 97(C).
    17. Faruk Urak, 2025. "Unraveling Turkish agricultural market challenges: Consequences of COVID‐19, Russia–Ukraine conflict, and energy market dynamics," Agribusiness, John Wiley & Sons, Ltd., vol. 41(2), pages 307-341, April.
    18. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    19. Yoon, Seong-Min, 2022. "On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests," Renewable Energy, Elsevier, vol. 199(C), pages 536-545.
    20. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
    21. Rangga Handika & Rangga Handika & Sigit Triandaru, 2016. "Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 814-821.
    22. Yahya, Muhammad & Dutta, Anupam & Bouri, Elie & Wadström, Christoffer & Uddin, Gazi Salah, 2022. "Dependence structure between the international crude oil market and the European markets of biodiesel and rapeseed oil," Renewable Energy, Elsevier, vol. 197(C), pages 594-605.
    23. Capitani, Daniel Henrique Dario & Gaio, Luiz Eduardo, . "Volatility Transmissionin Agricultural Markets: Evidence from the Russia-Ukraine Conflict," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 11(2).
    24. Zhu, Yanli & Yang, Xian & Zhang, Chuanhai & Liu, Sihan & Li, Jiayi, 2024. "Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty," Journal of Commodity Markets, Elsevier, vol. 36(C).
    25. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
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    Cited by:

    1. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    2. Xixuan Han & Boyu Wei & Hailiang Yang, 2018. "Index Options And Volatility Derivatives In A Gaussian Random Field Risk-Neutral Density Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-41, June.

  13. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall derivatives at the CME," SFB 649 Discussion Papers 2013-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Bressan, Giacomo Maria & Romagnoli, Silvia, 2021. "Climate risks and weather derivatives: A copula-based pricing model," Journal of Financial Stability, Elsevier, vol. 54(C).

  14. López Cabrera, Brenda & Schulz, Franziska, 2013. "Volatility linkages between energy and agricultural commodity prices," SFB 649 Discussion Papers 2013-042, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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    1. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    2. Chang, C-L. & McAleer, M.J. & Wang, Y-A., 2016. "Modelling Volatility Spillovers for Bio-ethanol, Sugarcane and Corn Spot and Futures Prices," Econometric Institute Research Papers EI2016-45, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Vo, Long Hai & Le, Thai-Ha, 2021. "Eatery, energy, environment and economic system, 1970–2017: Understanding volatility spillover patterns in a global sample," Energy Economics, Elsevier, vol. 100(C).
    4. Yousaf, Imran & Abrar, Afsheen & Ali, Shoaib & Goodell, John W., 2024. "Connectedness between energy cryptocurrencies and US equity markets: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    5. Anthony N. Rezitis & Panagiotis Andrikopoulos & Theodoros Daglis, 2024. "Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 451-483, March.
    6. Hokey Min, 2022. "Examining the Impact of Energy Price Volatility on Commodity Prices from Energy Supply Chain Perspectives," Energies, MDPI, vol. 15(21), pages 1-16, October.
    7. Harun Uçak & Irfan Ullah & Yakup Ari, 2024. "The volatility connectedness between fertilizers and rice price: evidences from the global major rice-producing countries," Asia-Pacific Journal of Regional Science, Springer, vol. 8(1), pages 239-263, March.
    8. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2021. "The realized volatility of commodity futures: Interconnectedness and determinants#," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 139-151.
    9. Ding, Shusheng & Cui, Tianxiang & Zheng, Dandan & Du, Min, 2021. "The effects of commodity financialization on commodity market volatility," Resources Policy, Elsevier, vol. 73(C).
    10. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    11. M. Thenmozhi & Shipra Maurya, 2020. "Crude Oil Volatility Transmission Across Food Commodity Markets: A Multivariate BEKK-GARCH Approach," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 20(2), pages 131-164, August.
    12. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    13. Tule, Moses K. & Salisu, Afees A. & Chiemeke, Charles C., 2019. "Can agricultural commodity prices predict Nigeria's inflation?," Journal of Commodity Markets, Elsevier, vol. 16(C).
    14. Han, Liyan & Jin, Jiayu & Wu, Lei & Zeng, Hongchao, 2020. "The volatility linkage between energy and agricultural futures markets with external shocks," International Review of Financial Analysis, Elsevier, vol. 68(C).
    15. Yahya, Muhammad & Oglend, Atle & Dahl, Roy Endré, 2019. "Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach," Energy Economics, Elsevier, vol. 80(C), pages 277-296.
    16. Miroslava Ivanova & Lilko Dospatliev, 2023. "Effects of Diesel Price on Changes in Agricultural Commodity Prices in Bulgaria," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    17. Zhang, Wei & Wu, Jiayi & Wang, Shun & Zhang, Yong, 2025. "Examining dynamics: Unraveling the impact of oil price fluctuations on forecasting agricultural futures prices," International Review of Financial Analysis, Elsevier, vol. 97(C).
    18. Faruk Urak, 2025. "Unraveling Turkish agricultural market challenges: Consequences of COVID‐19, Russia–Ukraine conflict, and energy market dynamics," Agribusiness, John Wiley & Sons, Ltd., vol. 41(2), pages 307-341, April.
    19. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    20. Yoon, Seong-Min, 2022. "On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests," Renewable Energy, Elsevier, vol. 199(C), pages 536-545.
    21. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
    22. Rangga Handika & Rangga Handika & Sigit Triandaru, 2016. "Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 814-821.
    23. Yahya, Muhammad & Dutta, Anupam & Bouri, Elie & Wadström, Christoffer & Uddin, Gazi Salah, 2022. "Dependence structure between the international crude oil market and the European markets of biodiesel and rapeseed oil," Renewable Energy, Elsevier, vol. 197(C), pages 594-605.
    24. Capitani, Daniel Henrique Dario & Gaio, Luiz Eduardo, . "Volatility Transmissionin Agricultural Markets: Evidence from the Russia-Ukraine Conflict," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 11(2).
    25. Zhu, Yanli & Yang, Xian & Zhang, Chuanhai & Liu, Sihan & Li, Jiayi, 2024. "Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty," Journal of Commodity Markets, Elsevier, vol. 36(C).
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  15. Härdle, Wolfgang Karl & López Cabrera, Brenda & Okhrin, Ostap & Wang, Weining, 2010. "Localising temperature risk," SFB 649 Discussion Papers 2011-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gillmann, Niels & Okhrin, Ostap, 2025. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Economic Modelling, Elsevier, vol. 148(C).
    2. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    3. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    4. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    5. Grochowicz, Aleksander & Benth, Fred Espen & Zeyringer, Marianne, 2024. "Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway," Applied Energy, Elsevier, vol. 356(C).
    6. Shen, Zhiwei, "undated". "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
    7. Xiaofeng Cao & Ostap Okhrin & Martin Odening & Matthias Ritter, 2015. "Modelling spatio-temporal variability of temperature," Computational Statistics, Springer, vol. 30(3), pages 745-766, September.

  16. Härdle, Wolfgang Karl & López Cabrera, Brenda & Okhrin, Ostap & Wang, Weining, 2010. "Localising temperature risk," SFB 649 Discussion Papers 2011-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gillmann, Niels & Okhrin, Ostap, 2025. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Economic Modelling, Elsevier, vol. 148(C).
    2. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    3. Grochowicz, Aleksander & Benth, Fred Espen & Zeyringer, Marianne, 2024. "Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway," Applied Energy, Elsevier, vol. 356(C).
    4. Shen, Zhiwei, "undated". "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.

  17. Härdle, Wolfgang Karl & López Cabrera, Brenda, 2009. "Implied market price of weather risk," SFB 649 Discussion Papers 2009-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Fred Espen Benth & Paul Kruhner, 2014. "Representation of infinite dimensional forward price models in commodity markets," Papers 1403.4111, arXiv.org.
    2. Fred Espen Benth & Anca Pircalabu, 2018. "A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 36-65, January.
    3. Benth, Fred Espen & Taib, Che Mohd Imran Che, 2013. "On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets," Energy Economics, Elsevier, vol. 40(C), pages 259-268.
    4. Ragnhild Noven & Almut Veraart & Axel Gandy, 2015. "A Lévy-driven rainfall model with applications to futures pricing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 403-432, October.
    5. Evarest Emmanuel & Berntsson Fredrik & Singull Martin & Yang Xiangfeng, 2018. "Weather derivatives pricing using regime switching model," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 13-27, March.
    6. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    7. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457.
    8. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.
    9. Jr‐Wei Huang & Sharon S. Yang & Chuang‐Chang Chang, 2018. "Modeling temperature behaviors: Application to weather derivative valuation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1152-1175, September.
    10. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    11. L. Kermiche & N. Vuillermet, 2016. "Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 165-177, January.
    12. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    13. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    14. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    15. Ole E. Barndorff-Nielsen & Fred Espen Benth & Almut E. D. Veraart, 2013. "Modelling energy spot prices by volatility modulated L\'{e}vy-driven Volterra processes," Papers 1307.6332, arXiv.org.
    16. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    17. Grochowicz, Aleksander & Benth, Fred Espen & Zeyringer, Marianne, 2024. "Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway," Applied Energy, Elsevier, vol. 356(C).
    18. Makkonen, Adam & Vallström, Daniel & Uddin, Gazi Salah & Rahman, Md Lutfur & Haddad, Michel Ferreira Cardia, 2021. "The effect of temperature anomaly and macroeconomic fundamentals on agricultural commodity futures returns," Energy Economics, Elsevier, vol. 100(C).
    19. Fernando Alves Silveira & Silvio Parodi de Oliveira Camilo, 2024. "A blockchain-based platform for trading weather derivatives," Digital Finance, Springer, vol. 6(1), pages 3-22, March.
    20. Fred Espen Benth, 2021. "Pricing of Commodity and Energy Derivatives for Polynomial Processes," Mathematics, MDPI, vol. 9(2), pages 1-30, January.
    21. Larsson, Karl & Green, Rikard & Benth, Fred Espen, 2023. "A stochastic time-series model for solar irradiation," Energy Economics, Elsevier, vol. 117(C).
    22. Benth, Fred Espen & Koekebakker, Steen, 2015. "Pricing of forwards and other derivatives in cointegrated commodity markets," Energy Economics, Elsevier, vol. 52(PA), pages 104-117.
    23. Joanna Janczura, 2014. "Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 79(1), pages 1-30, February.
    24. Grith, Maria & Härdle, Wolfgang Karl & Kneip, Alois & Wagner, Heiko, 2016. "Functional principal component analysis for derivatives of multivariate curves," SFB 649 Discussion Papers 2016-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    25. Kanamura, Takashi, 2019. "Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power," MPRA Paper 92009, University Library of Munich, Germany.
    26. Alfeus, Mesias & Mwampashi, Muthe M. & Nikitopoulos, Christina S. & Overbeck, Ludger, 2025. "Stochastic modelling and forecasting of wind capacity utilization with applications to risk management: The Australian case," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).

  18. Härdle, Wolfgang Karl & López Cabrera, Brenda, 2009. "Implied market price of weather risk," SFB 649 Discussion Papers 2009-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544, World Scientific Publishing Co. Pte. Ltd..
    2. Fred Espen Benth & Paul Kruhner, 2014. "Representation of infinite dimensional forward price models in commodity markets," Papers 1403.4111, arXiv.org.
    3. Fred Espen Benth & Anca Pircalabu, 2018. "A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 36-65, January.
    4. Ragnhild Noven & Almut Veraart & Axel Gandy, 2015. "A Lévy-driven rainfall model with applications to futures pricing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 403-432, October.
    5. Benth, Fred Espen & Taib, Che Mohd Imran Che, 2013. "On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets," Energy Economics, Elsevier, vol. 40(C), pages 259-268.
    6. Evarest Emmanuel & Berntsson Fredrik & Singull Martin & Yang Xiangfeng, 2018. "Weather derivatives pricing using regime switching model," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 13-27, March.
    7. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    8. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457.
    9. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.
    10. Jr‐Wei Huang & Sharon S. Yang & Chuang‐Chang Chang, 2018. "Modeling temperature behaviors: Application to weather derivative valuation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1152-1175, September.
    11. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    12. L. Kermiche & N. Vuillermet, 2016. "Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 165-177, January.
    13. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    14. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    15. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    16. Ole E. Barndorff-Nielsen & Fred Espen Benth & Almut E. D. Veraart, 2013. "Modelling energy spot prices by volatility modulated L\'{e}vy-driven Volterra processes," Papers 1307.6332, arXiv.org.
    17. Dorfleitner, Gregor & Wimmer, Maximilian, 2010. "The pricing of temperature futures at the Chicago Mercantile Exchange," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1360-1370, June.
    18. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    19. Grochowicz, Aleksander & Benth, Fred Espen & Zeyringer, Marianne, 2024. "Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway," Applied Energy, Elsevier, vol. 356(C).
    20. Makkonen, Adam & Vallström, Daniel & Uddin, Gazi Salah & Rahman, Md Lutfur & Haddad, Michel Ferreira Cardia, 2021. "The effect of temperature anomaly and macroeconomic fundamentals on agricultural commodity futures returns," Energy Economics, Elsevier, vol. 100(C).
    21. Ahčan, Aleš, 2012. "Statistical analysis of model risk concerning temperature residuals and its impact on pricing weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 131-138.
    22. Fernando Alves Silveira & Silvio Parodi de Oliveira Camilo, 2024. "A blockchain-based platform for trading weather derivatives," Digital Finance, Springer, vol. 6(1), pages 3-22, March.
    23. Fred Espen Benth, 2021. "Pricing of Commodity and Energy Derivatives for Polynomial Processes," Mathematics, MDPI, vol. 9(2), pages 1-30, January.
    24. Larsson, Karl & Green, Rikard & Benth, Fred Espen, 2023. "A stochastic time-series model for solar irradiation," Energy Economics, Elsevier, vol. 117(C).
    25. Benth, Fred Espen & Koekebakker, Steen, 2015. "Pricing of forwards and other derivatives in cointegrated commodity markets," Energy Economics, Elsevier, vol. 52(PA), pages 104-117.
    26. Joanna Janczura, 2014. "Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 79(1), pages 1-30, February.
    27. Grith, Maria & Härdle, Wolfgang Karl & Kneip, Alois & Wagner, Heiko, 2016. "Functional principal component analysis for derivatives of multivariate curves," SFB 649 Discussion Papers 2016-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    28. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
    29. Kanamura, Takashi, 2019. "Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power," MPRA Paper 92009, University Library of Munich, Germany.
    30. Alfeus, Mesias & Mwampashi, Muthe M. & Nikitopoulos, Christina S. & Overbeck, Ludger, 2025. "Stochastic modelling and forecasting of wind capacity utilization with applications to risk management: The Australian case," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).

  19. Benth, Fred & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2009. "Pricing of Asian temperature risk," SFB 649 Discussion Papers 2009-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457.
    2. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    3. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    4. Ole E. Barndorff-Nielsen & Fred Espen Benth & Almut E. D. Veraart, 2013. "Modelling energy spot prices by volatility modulated L\'{e}vy-driven Volterra processes," Papers 1307.6332, arXiv.org.
    5. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    6. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.

  20. Härdle, Wolfgang Karl & Cabrera, Brenda López, 2007. "Calibrating CAT bonds for Mexican earthquakes," SFB 649 Discussion Papers 2007-037, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Borensztein, Eduardo & Cavallo, Eduardo & Jeanne, Olivier, 2017. "The welfare gains from macro-insurance against natural disasters," Journal of Development Economics, Elsevier, vol. 124(C), pages 142-156.
    2. Nowak, Piotr & Romaniuk, Maciej, 2013. "Pricing and simulations of catastrophe bonds," Insurance: Mathematics and Economics, Elsevier, vol. 52(1), pages 18-28.
    3. Olivier Lopez & Daniel Nkameni, 2025. "Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses," Papers 2507.18207, arXiv.org, revised Feb 2026.
    4. Giuricich, Mario Nicoló & Burnecki, Krzysztof, 2019. "Modelling of left-truncated heavy-tailed data with application to catastrophe bond pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 498-513.
    5. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework," Papers 2208.02609, arXiv.org.
    6. Denis-Alexandre Trottier & Van Son Lai, 2017. "Reinsurance or CAT Bond? How to Optimally Combine Both," Working Papers 2017-003, Department of Research, Ipag Business School.
    7. Jacek Wszo{l}a & Krzysztof Burnecki & Marek Teuerle & Martyna Zdeb, 2025. "Design and valuation of multi-region CoCoCat bonds," Papers 2510.17221, arXiv.org.
    8. Braun, Alexander, 2011. "Pricing catastrophe swaps: A contingent claims approach," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 520-536.
    9. Y. Esmaeelzade Aghdam & A. Neisy & A. Adl, 2024. "Simulating and Pricing CAT Bonds Using the Spectral Method Based on Chebyshev Basis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 423-435, January.
    10. Harsh K. Mistry & Domenico Lombardi, 2023. "A stochastic exposure model for seismic risk assessment and pricing of catastrophe bonds," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 803-829, May.
    11. Chang Carolyn W. & Feng Yalan, 2021. "Hurricane Bond Price Dependency on Underlying Hurricane Parameters," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 15(1), pages 1-21, January.
    12. Han-Bin KANG & Hsuling CHANG & Tsangyao CHANG, 2022. "Catastrophe Reinsurance Pricing -Modification of Dynamic Asset-Liability Management," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-20, December.
    13. Shao, Jia & Papaioannou, Apostolos D. & Pantelous, Athanasios A., 2017. "Pricing and simulating catastrophe risk bonds in a Markov-dependent environment," Applied Mathematics and Computation, Elsevier, vol. 309(C), pages 68-84.
    14. Loretta Mastroeni & Alessandro Mazzoccoli & Maurizio Naldi, 2022. "Pricing Cat Bonds for Cloud Service Failures," JRFM, MDPI, vol. 15(10), pages 1-18, October.
    15. Têtu Alexandre & Lai Van Son & Soumaré Issouf & Gendron Michel, 2015. "Hedging Flood Losses Using Cat Bonds," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 9(2), pages 149-184, July.
    16. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework ," Post-Print hal-03745077, HAL.
    17. Lo, Chien-Ling & Lee, Jin-Ping & Yu, Min-Teh, 2013. "Valuation of insurers’ contingent capital with counterparty risk and price endogeneity," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5025-5035.
    18. Krzysztof Burnecki & Mario Nicoló Giuricich, 2017. "Stable Weak Approximation at Work in Index-Linked Catastrophe Bond Pricing," Risks, MDPI, vol. 5(4), pages 1-19, December.
    19. Ben Ammar, Semir & Braun, Alexander & Eling, Martin, 2015. "Alternative Risk Transfer and Insurance-Linked Securities: Trends, Challenges and New Market Opportunities," I.VW HSG Schriftenreihe, University of St.Gallen, Institute of Insurance Economics (I.VW-HSG), volume 56, number 56.
    20. Ma, Zong-Gang & Ma, Chao-Qun, 2013. "Pricing catastrophe risk bonds: A mixed approximation method," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 243-254.
    21. Alexis Louaas & Pierre Picard, 2017. "Optimal insurance for catastrophic risk: theory and application to nuclear corporate liability," Working Papers hal-01527478, HAL.
    22. Truong, Chi & Trück, Stefan, 2016. "It’s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events," European Journal of Operational Research, Elsevier, vol. 253(3), pages 856-868.
    23. Carolyn W. Chang & Jack S. K. Chang & Min‐Teh Yu & Yang Zhao, 2020. "Portfolio optimization in the catastrophe space," European Financial Management, European Financial Management Association, vol. 26(5), pages 1414-1448, November.
    24. Martin Eling, 2013. "Recent Research Developments Affecting Nonlife Insurance—The CAS Risk Premium Project 2011 Update," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 16(1), pages 35-46, March.
    25. Krzysztof Burnecki & Marek Teuerle & Martyna Zdeb, 2025. "Modelling and valuation of catastrophe bonds across multiple regions," Papers 2512.08890, arXiv.org.
    26. Sukono & Hafizan Juahir & Riza Andrian Ibrahim & Moch Panji Agung Saputra & Yuyun Hidayat & Igif Gimin Prihanto, 2022. "Application of Compound Poisson Process in Pricing Catastrophe Bonds: A Systematic Literature Review," Mathematics, MDPI, vol. 10(15), pages 1-19, July.

  21. Haerdle, Wolfgang & Cabrera, Brenda Lopez, 2007. "Calibrating CAT bonds for Mexican earthquakes," 101st Seminar, July 5-6, 2007, Berlin Germany 9265, European Association of Agricultural Economists.

    Cited by:

    1. Borensztein, Eduardo & Cavallo, Eduardo & Jeanne, Olivier, 2017. "The welfare gains from macro-insurance against natural disasters," Journal of Development Economics, Elsevier, vol. 124(C), pages 142-156.
    2. Nowak, Piotr & Romaniuk, Maciej, 2013. "Pricing and simulations of catastrophe bonds," Insurance: Mathematics and Economics, Elsevier, vol. 52(1), pages 18-28.
    3. Olivier Lopez & Daniel Nkameni, 2025. "Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses," Papers 2507.18207, arXiv.org, revised Feb 2026.
    4. Giuricich, Mario Nicoló & Burnecki, Krzysztof, 2019. "Modelling of left-truncated heavy-tailed data with application to catastrophe bond pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 498-513.
    5. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework," Papers 2208.02609, arXiv.org.
    6. Denis-Alexandre Trottier & Van Son Lai, 2017. "Reinsurance or CAT Bond? How to Optimally Combine Both," Working Papers 2017-003, Department of Research, Ipag Business School.
    7. Jacek Wszo{l}a & Krzysztof Burnecki & Marek Teuerle & Martyna Zdeb, 2025. "Design and valuation of multi-region CoCoCat bonds," Papers 2510.17221, arXiv.org.
    8. Braun, Alexander, 2011. "Pricing catastrophe swaps: A contingent claims approach," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 520-536.
    9. Y. Esmaeelzade Aghdam & A. Neisy & A. Adl, 2024. "Simulating and Pricing CAT Bonds Using the Spectral Method Based on Chebyshev Basis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 423-435, January.
    10. Harsh K. Mistry & Domenico Lombardi, 2023. "A stochastic exposure model for seismic risk assessment and pricing of catastrophe bonds," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 803-829, May.
    11. Chang Carolyn W. & Feng Yalan, 2021. "Hurricane Bond Price Dependency on Underlying Hurricane Parameters," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 15(1), pages 1-21, January.
    12. Han-Bin KANG & Hsuling CHANG & Tsangyao CHANG, 2022. "Catastrophe Reinsurance Pricing -Modification of Dynamic Asset-Liability Management," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-20, December.
    13. Shao, Jia & Papaioannou, Apostolos D. & Pantelous, Athanasios A., 2017. "Pricing and simulating catastrophe risk bonds in a Markov-dependent environment," Applied Mathematics and Computation, Elsevier, vol. 309(C), pages 68-84.
    14. Loretta Mastroeni & Alessandro Mazzoccoli & Maurizio Naldi, 2022. "Pricing Cat Bonds for Cloud Service Failures," JRFM, MDPI, vol. 15(10), pages 1-18, October.
    15. Têtu Alexandre & Lai Van Son & Soumaré Issouf & Gendron Michel, 2015. "Hedging Flood Losses Using Cat Bonds," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 9(2), pages 149-184, July.
    16. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework ," Post-Print hal-03745077, HAL.
    17. Lo, Chien-Ling & Lee, Jin-Ping & Yu, Min-Teh, 2013. "Valuation of insurers’ contingent capital with counterparty risk and price endogeneity," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5025-5035.
    18. Krzysztof Burnecki & Mario Nicoló Giuricich, 2017. "Stable Weak Approximation at Work in Index-Linked Catastrophe Bond Pricing," Risks, MDPI, vol. 5(4), pages 1-19, December.
    19. Ben Ammar, Semir & Braun, Alexander & Eling, Martin, 2015. "Alternative Risk Transfer and Insurance-Linked Securities: Trends, Challenges and New Market Opportunities," I.VW HSG Schriftenreihe, University of St.Gallen, Institute of Insurance Economics (I.VW-HSG), volume 56, number 56.
    20. Ho, Joanne & Odening, Martin, 2009. "Weather-based estimation of wildfire risk," SFB 649 Discussion Papers 2009-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Ma, Zong-Gang & Ma, Chao-Qun, 2013. "Pricing catastrophe risk bonds: A mixed approximation method," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 243-254.
    22. Alexis Louaas & Pierre Picard, 2017. "Optimal insurance for catastrophic risk: theory and application to nuclear corporate liability," Working Papers hal-01527478, HAL.
    23. Truong, Chi & Trück, Stefan, 2016. "It’s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events," European Journal of Operational Research, Elsevier, vol. 253(3), pages 856-868.
    24. Carolyn W. Chang & Jack S. K. Chang & Min‐Teh Yu & Yang Zhao, 2020. "Portfolio optimization in the catastrophe space," European Financial Management, European Financial Management Association, vol. 26(5), pages 1414-1448, November.
    25. Martin Eling, 2013. "Recent Research Developments Affecting Nonlife Insurance—The CAS Risk Premium Project 2011 Update," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 16(1), pages 35-46, March.
    26. Krzysztof Burnecki & Marek Teuerle & Martyna Zdeb, 2025. "Modelling and valuation of catastrophe bonds across multiple regions," Papers 2512.08890, arXiv.org.
    27. Sukono & Hafizan Juahir & Riza Andrian Ibrahim & Moch Panji Agung Saputra & Yuyun Hidayat & Igif Gimin Prihanto, 2022. "Application of Compound Poisson Process in Pricing Catastrophe Bonds: A Systematic Literature Review," Mathematics, MDPI, vol. 10(15), pages 1-19, July.

Articles

  1. Chen, Shi & Karl Härdle, Wolfgang & López Cabrera, Brenda, 2019. "Regularization approach for network modeling of German power derivative market," Energy Economics, Elsevier, vol. 83(C), pages 180-196.

    Cited by:

    1. Tadahiro Nakajima & Yuki Toyoshima, 2020. "Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices," Energies, MDPI, vol. 13(7), pages 1-14, March.
    2. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2020. "Impact of Solar and Wind Prices on the Integrated Global Electricity Spot and Options Markets: A Time Series Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 337-353.
    3. Zhu, Bo & Deng, Yuanyue & Lin, Renda & Hu, Xin & Chen, Pingshe, 2022. "Energy security: Does systemic risk spillover matter? Evidence from China," Energy Economics, Elsevier, vol. 114(C).
    4. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.

  2. Brenda López Cabrera & Franziska Schulz, 2017. "Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 127-136, January.
    See citations under working paper version above.
  3. Wolfgang Karl Härdle & Brenda López Cabrera & Ostap Okhrin & Weining Wang, 2016. "Localizing Temperature Risk," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1491-1508, October.
    See citations under working paper version above.
  4. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    See citations under working paper version above.
  5. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    See citations under working paper version above.
  6. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    See citations under working paper version above.
  7. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    See citations under working paper version above.
  8. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.

    Cited by:

    1. Ragnhild Noven & Almut Veraart & Axel Gandy, 2015. "A Lévy-driven rainfall model with applications to futures pricing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 403-432, October.
    2. Simona Franzoni & Cristian Pelizzari, 2021. "Rainfall option impact on profits of the hospitality industry through scenario correlation and copulas," Annals of Operations Research, Springer, vol. 299(1), pages 939-962, April.
    3. Peng Li, 2021. "The Valuation of Weather Derivatives Using One Sided Crank–Nicolson Schemes," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 825-847, October.
    4. Tong, Zhigang & Liu, Allen, 2021. "A censored Ornstein–Uhlenbeck process for rainfall modeling and derivatives pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    5. Matsumoto, Takuji & Yamada, Yuji, 2025. "Advancing financial instruments and market trading framework for local solar power hedging with principal component derivatives," Energy Economics, Elsevier, vol. 149(C).
    6. Bertrand, Jean-Louis & Parnaudeau, Miia, 2019. "Understanding the economic effects of abnormal weather to mitigate the risk of business failures," Journal of Business Research, Elsevier, vol. 98(C), pages 391-402.
    7. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    8. Härdle, Wolfgang Karl & Osipenko, Maria, 2017. "Dynamic valuation of weather derivatives under default risk," SFB 649 Discussion Papers 2017-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    10. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    11. Edimilson Costa Lucas & Wesley Mendes Da Silva & Gustavo Silva Araujo, 2017. "Does Extreme Rainfall Lead to Heavy Economic Losses in the Food Industry?," Working Papers Series 462, Central Bank of Brazil, Research Department.
    12. Truong, Chi & Trück, Stefan, 2016. "It’s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events," European Journal of Operational Research, Elsevier, vol. 253(3), pages 856-868.
    13. Wolfgang Karl Härdle & Maria Osipenko, 2017. "A Dynamic Programming Approach for Pricing Weather Derivatives under Issuer Default Risk," IJFS, MDPI, vol. 5(4), pages 1-18, October.
    14. Ceballos, Francisco, 2016. "Estimating spatial basis risk in rainfall index insurance: Methodology and application to excess rainfall insurance in Uruguay," IFPRI discussion papers 1595, International Food Policy Research Institute (IFPRI).
    15. M. Ritter & O. Mußhoff & M. Odening, 2014. "Minimizing Geographical Basis Risk of Weather Derivatives Using A Multi-Site Rainfall Model," Computational Economics, Springer;Society for Computational Economics, vol. 44(1), pages 67-86, June.

  9. Wolfgang Karl Härdle & Brenda López Cabrera, 2012. "The Implied Market Price of Weather Risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(1), pages 59-95, February.
    See citations under working paper version above.
  10. Wolfgang Karl Härdle & Brenda López Cabrera, 2010. "Calibrating CAT Bonds for Mexican Earthquakes," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(3), pages 625-650, September.
    See citations under working paper version above.
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