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Rodrigo Herrera

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. Gaete, Michael & Herrera, Rodrigo, 2022. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," MPRA Paper 115641, University Library of Munich, Germany.

    Cited by:

    1. Chen, Zhenlong & Chang, Jing & Hao, Xiaozhen, 2024. "Portfolio selection via high-dimensional stochastic factor Copula," Finance Research Letters, Elsevier, vol. 67(PA).
    2. Ayesha Sayed & Christo Auret, 2025. "Is corn still king? Unravelling time-varying interactions among soft commodities," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(1), pages 259-284, March.
    3. Benjamin E. Radoc, Jr. & Karl Robert L. Jandoc & Majah-Leah V. Ravago, 2025. "Alternative Trading Rules in an Experimental Forward Market for a Non-Storable Commodity," BSP Discussion Paper Series 202520, Bangko Sentral ng Pilipinas.

  2. Fernanda Fuentes & Rodrigo Herrera & Adam Clements, 2016. "Modelling Extreme Risks in Commodities and Commodity Currencies," NCER Working Paper Series 115, National Centre for Econometric Research.

    Cited by:

    1. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    2. Go, You-How & Lau, Wee-Yeap, 2021. "Extreme risk spillovers between crude palm oil prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    3. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    4. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    5. Song, Shiyu, 2024. "The valuation of arithmetic Asian options with mean reversion and jump clustering," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    6. Albrecht, Peter & Kočenda, Evžen & de Oliveira, Alexandre Silva & Ceretta, Paulo Sergio & Drábek, Michal, 2025. "Event-driven changes in connectedness among commodities and commodity currencies: A quantile, network and probabilistic analysis," Research in International Business and Finance, Elsevier, vol. 75(C).
    7. Huang, Jionghao & Li, Hongqiao & Chen, Baifan & Liu, Mengai & An, Chaofan & Xia, Xiaohua, 2025. "Revisiting the currency-commodity nexus: New insights into the R2 decomposed connectedness and the role of global shocks," International Review of Economics & Finance, Elsevier, vol. 98(C).
    8. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    9. Go, You-How & Lau, Wee-Yeap, 2024. "Terms of trade or market power? Further evidence from dynamic spillovers in return and volatility between Malaysian crude palm oil and foreign exchange markets," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).

  3. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).

    Cited by:

    1. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    2. Inés Jiménez & Andrés Mora-Valencia & Javier Perote, 2026. "Cross-moment interaction in multivariate semi-nonparametric densities for risk forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-22, December.
    3. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    4. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    5. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    6. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    7. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    8. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    9. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).

  4. R Herrera & Adam Clements, 2015. "Point process models for extreme returns: Harnessing implied volatility," NCER Working Paper Series 104, National Centre for Econometric Research.

    Cited by:

    1. Zhang, Jier & Yin, Libo & Li, Ying & Fang, Tong, 2026. "Forecasting stock market volatility with policy focus shifting: A GARCH-MIDAS model combined with machine learning approaches," Pacific-Basin Finance Journal, Elsevier, vol. 97(C).
    2. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Hong, Yanran & Ma, Feng & Wang, Lu & Liang, Chao, 2022. "How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme Granger causality test," Resources Policy, Elsevier, vol. 78(C).
    4. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    5. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    6. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.
    7. Nishio, Kazuki & Hoshino, Takahiro, 2022. "Joint modeling of effects of customer tier program on customer purchase duration and purchase amount," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    8. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    9. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    10. Xiaozhen Jing & Dezhong Xu & Bin Li & Tarlok Singh, 2024. "Does the U.S. extreme indicator matter in stock markets? International evidence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-27, December.
    11. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    12. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    13. Wang, Lu & Ruan, Hang & Lai, Xiaodong & Li, Dongxin, 2024. "Economic extremes steering renewable energy trajectories: A time-frequency dissection of global shocks," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    14. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    15. Liu, Yanchen & Yi, Siyu & Li, Sitong & Chen, Gengxuan, 2025. "Asymmetric impacts of energy market-related uncertainty on clean energy stock volatility: The role of extreme shocks," International Review of Financial Analysis, Elsevier, vol. 103(C).
    16. Xi, Yue & Zeng, Qing & Lu, Xinjie & Huynh, Toan L.D., 2022. "Oil and renewable energy stock markets: Unique role of extreme shocks," Energy Economics, Elsevier, vol. 109(C).
    17. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    18. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    19. Duan, Huayou & Zhao, Chenchen & Wang, Lu & Liu, Guangqiang, 2024. "The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability," Research in International Business and Finance, Elsevier, vol. 71(C).
    20. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    21. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    22. Siyue Zheng & Mingdong Xu & Min Zhu, 2025. "Generalized Modeling of Oil Futures Volatility Through Uncertainty Indicator Selection: A GARCH–MIDAS–AES Framework," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(9), pages 1182-1201, September.
    23. Wang, Lu & Ma, Feng & Niu, Tianjiao & He, Chengting, 2020. "Crude oil and BRICS stock markets under extreme shocks: New evidence," Economic Modelling, Elsevier, vol. 86(C), pages 54-68.
    24. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).

  5. Herrera, Rodrigo & Schipp, Bernhard, 2011. "Extreme value models in a conditional duration intensity framework," SFB 649 Discussion Papers 2011-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    2. Fernanda Fuentes & Rodrigo Herrera & Adam Clements, 2016. "Modelling Extreme Risks in Commodities and Commodity Currencies," NCER Working Paper Series 115, National Centre for Econometric Research.
    3. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.

Articles

  1. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.

    Cited by:

    1. Jiang, Zhuhua & Ozcelebi, Oguzhan & Lü, Zheng & El Khoury, Rim & Yoon, Seong-Min, 2026. "The impact of financial uncertainty on the price dynamics of global bond funds," Global Finance Journal, Elsevier, vol. 69(C).

  2. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).

    Cited by:

    1. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).

  3. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).

    Cited by:

    1. Jiao, Lei & Zhou, Qing (Clara), 2026. "Economic conditions and portfolio tail risk: A probability-weighted simulation approach," Journal of Empirical Finance, Elsevier, vol. 87(C).
    2. Alexiou, Lykourgos & Rompolis, Leonidas S., 2024. "Jump tail risk exposure and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 79(C).
    3. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    4. Haji Mohamad Zubir, Ahmad Shauqi bin & Mohd Nasir, Muhammad Luqman bin, 2026. "Where Volume Belongs in a Tail Risk Model: Extreme Quantile Forecasts, Subordination, and Market Depth," MPRA Paper 130162, University Library of Munich, Germany.
    5. Christian Laudag'e & Felix-Benedikt Liebrich, 2025. "Meyer risk measures," Papers 2509.24747, arXiv.org, revised Sep 2026.
    6. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    7. Wen, Limin, 2026. "Improving Marginal Expected Shortfall estimates using credibility methods," Finance Research Letters, Elsevier, vol. 91(C).
    8. Autcha Araveeporn, 2025. "Improved Probability-Weighted Moments and Two-Stage Order Statistics Methods of Generalized Extreme Value Distribution," Mathematics, MDPI, vol. 13(14), pages 1-20, July.
    9. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    10. Feipeng Zhang & Yuhan Ma & Yongchang Hui, 2026. "A Direct Nonparametric Estimator for EVaR of Dependent Financial Returns," Computational Economics, Springer;Society for Computational Economics, vol. 67(2), pages 991-1008, February.

  4. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.

    Cited by:

    1. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    2. Federico Gatta & Fabrizio Lillo & Piero Mazzarisi, 2024. "CAESar: Conditional Autoregressive Expected Shortfall," Papers 2407.06619, arXiv.org, revised Jun 2026.
    3. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    4. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).

  5. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    See citations under working paper version above.
  6. Alejandro Rodriguez & Gabriel Pino & Rodrigo Herrera, 2021. "A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(3), pages 471-497, February.

    Cited by:

    1. Stavros Kalogiannidis & Stamatis Kontsas & Dimitrios Kalfas & Fotios Chatzitheodoridis, 2024. "Operational risk management in managerial accounting: a comprehensive examination of strategies and implementation in medium size organizations," Operational Research, Springer, vol. 24(3), pages 1-27, September.

  7. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.

    Cited by:

    1. Fadugba, Sunday Emmanuel, 2020. "Homotopy analysis method and its applications in the valuation of European call options with time-fractional Black-Scholes equation," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    3. Stindl, Tom, 2023. "Forecasting intraday market risk: A marked self-exciting point process with exogenous renewals," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 182-198.

  8. Nikolaus Hautsch & Rodrigo Herrera, 2020. "Multivariate dynamic intensity peaks‐over‐threshold models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 248-272, March.
    See citations under working paper version above.
  9. Fernanda Fuentes & Rodrigo Herrera, 2020. "Dynamics of Connectedness in Clean Energy Stocks," Energies, MDPI, vol. 13(14), pages 1-19, July.

    Cited by:

    1. Kliber, Agata & Będowska-Sójka, Barbara, 2024. "Proof-of-work versus proof-of-stake coins as possible hedges against green and dirty energy," Energy Economics, Elsevier, vol. 138(C).
    2. Zynobia Barson & Kwame Simpe Ofori & Peterson Owusu Junior & Kwabena G. Boakye & George Oppong Appiagyei Ampong, 2024. "Time-varying Connectedness Between ESG Stocks and BRVM Traditional Stocks," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 23(3), pages 306-335, September.
    3. Besma Hkiri & David Roubaud & Nasir Khan & Beysül Aytaç, 2026. "Dynamics interconnectedness between energy markets and global commodities: Evidence from global crisis and geopolitical tensions," Review of Financial Economics, John Wiley & Sons, vol. 44(2), April.
    4. Guangxi Cao & Fei Xie & Meijun Ling, 2022. "Spillover effects in Chinese carbon, energy and financial markets," International Finance, Wiley Blackwell, vol. 25(3), pages 416-434, December.
    5. Patel, Ritesh & Kumar, Sanjeev & Agnihotri, Shalini, 2025. "Unveiling the crypto-green nexus: A risk management and investment strategy approach through the lens of NFTs, DeFis, green cryptocurrencies, and green investments," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    6. Muñoz Mendoza, Jorge A. & Veloso Ramos, Carmen L. & Delgado Fuentealba, Carlos L. & Cornejo Saavedra, Edinson E. & Sepúlveda Yelpo, Sandra M., 2024. "Stock, foreign exchange and commodity markets linkages: Implications for risk diversification and portfolio management," Global Finance Journal, Elsevier, vol. 63(C).
    7. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    8. Kuang, Wei, 2021. "Which clean energy sectors are attractive? A portfolio diversification perspective," Energy Economics, Elsevier, vol. 104(C).
    9. Li, Di & Wu, Zhige & Tang, Yixuan, 2024. "Do climate risks affect dirty–clean energy stock price dynamic correlations?," Energy Economics, Elsevier, vol. 136(C).
    10. Chuliá, Helena & Muñoz-Mendoza, Jorge A. & Uribe, Jorge M., 2023. "Energy firms in emerging markets: Systemic risk and diversification opportunities," Emerging Markets Review, Elsevier, vol. 56(C).
    11. Talat S. Genc & Stephen Kosempel, 2023. "Energy Transition and the Economy: A Review Article," Energies, MDPI, vol. 16(7), pages 1-26, March.
    12. Çelik, İsmail & Sak, Ahmet Furkan & Höl, Arife Özdemir & Vergili, Gizem, 2022. "The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    13. Naveed Khan & Anam Tariq & Syed Zulfiqar Ali Shah & Hassan Javed, 2026. "Quantile time–frequency connectedness and spillover between artificial intelligence, clean energy, and traditional asset classes: insights and portfolio implications," Future Business Journal, Springer, vol. 12(1), pages 1-39, December.
    14. Kuang, Wei, 2025. "A tale of two risks: Differential diversification roles of clean energy sector stocks in physical and transition climate risk management," Renewable Energy, Elsevier, vol. 249(C).
    15. Zheng, Qingying & Wu, Jintao & Lin, Boqiang, 2026. "Asymmetric volatility spillover between clean energy and nonferrous metal markets under climate risks: Portfolio hedging implications," Resources Policy, Elsevier, vol. 112(C).
    16. K. Abhaya Kumar & Prakash Pinto & Iqbal Thonse Hawaldar & Saheem Shaikh & Shravan Bhagav & B. Padmanabha, 2022. "Investigating the Nexus between Crude Oil Price and Stock Prices of Oil Exploration Companies," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 12(4), pages 40-47, July.
    17. Ibrahim D. Raheem & Oluyele Akinkugbe & Agboola H. Yusuf & Mahdi Ghaemi Asl, 2023. "Hedging strategies among financial markets: the case of green and brown assets," Empirical Economics, Springer, vol. 65(2), pages 831-873, August.
    18. Kocaarslan, Baris & Soytas, Ugur, 2021. "Reserve currency and the volatility of clean energy stocks: The role of uncertainty," Energy Economics, Elsevier, vol. 104(C).
    19. Maksim Fayzulin & Tamara Teplova & Aleksei Kurkin, 2025. "Dynamic connectedness between trading volumes and retail investor sentiment in the Russian stock market with Bitcoin during external shock periods," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 99-121.
    20. Shehzad, Khurram & Yang, Ruicheng & Kocak, Emrah & Akadiri, Seyi Saint & Zaman, Umer, 2026. "Financial stress, lithium prices, and renewable energy nexus in times of geopolitical crisis: A time-frequency analysis," Renewable Energy, Elsevier, vol. 260(C).
    21. Guangxi Cao & Fei Xie, 2024. "Extreme risk spillovers across energy and carbon markets: Evidence from the quantile extended joint connectedness approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2155-2175, April.
    22. Rui Dias & Nicole Horta & Mariana Chambino, 2023. "Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts," Energies, MDPI, vol. 16(9), pages 1-18, May.
    23. Belkhir, Nadia & Masmoudi, Wafa Kammoun & Loukil, Sahar & Belguith, Rihab, 2024. "Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 15(1), pages 519-531, December.

  10. Pino, Gabriel & Herrera, Rodrigo & Rodríguez, Alejandro, 2019. "Geographical spillovers on the relation between risk-taking and market power in the US banking sector," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 351-364.

    Cited by:

    1. Amanda, Citra, 2023. "Rural banking spatial competition and stability," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 492-504.

  11. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2018. "Modeling extreme risks in commodities and commodity currencies," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 108-120.
    See citations under working paper version above.
  12. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.

    Cited by:

    1. Guo, Ranran & Ye, Wuyi, 2021. "A model of dynamic tail dependence between crude oil prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Hong Qiu & Genhua Hu & Yuhong Yang & Jeffrey Zhang & Ting Zhang, 2020. "Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets," Sustainability, MDPI, vol. 12(19), pages 1-15, September.

  13. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.

    Cited by:

    1. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    2. Zhongfei Li & Kai Gan & Shaolong Sun & Shouyang Wang, 2023. "A new PM2.5 concentration forecasting system based on AdaBoost‐ensemble system with deep learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 154-175, January.
    3. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    4. Du, Ruijin & Li, Jingjing & Dong, Gaogao & Tian, Lixin & Qing, Ting & Fang, Guochang & Dong, Yujuan, 2020. "Percolation analysis of urban air quality: A case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Pei Du & Jianzhou Wang & Wendong Yang & Tong Niu, 2022. "A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 64-85, January.
    6. Xiang Xu, 2020. "Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 117-125, March.
    7. Peng-Yeng Yin, 2025. "A Review on PM 2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges," Sustainability, MDPI, vol. 17(3), pages 1-25, January.
    8. Clements, Adam & Hurn, Stan & Volkov, Vladimir, 2021. "A simple linear alternative to multiplicative error models with an application to trading volume," Working Papers 2021-06, University of Tasmania, Tasmanian School of Business and Economics.

  14. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
    See citations under working paper version above.
  15. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.

    Cited by:

    1. Muğaloğlu, Erhan & Kuşkaya, Sevda & Aldieri, Luigi & Alnour, Mohammed & Hoque, Mohammad Enamul & Magazzino, Cosimo & Bilgili, Faik, 2023. "Dynamic regime differences in the market behavior of primary natural resources in response to geopolitical risk and economic policy uncertainty," Resources Policy, Elsevier, vol. 87(PB).
    2. Oscar V. De la Torre-Torres & José Álvarez-García & María de la Cruz del Río-Rama, 2024. "An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
    3. Hong, Yanran & Ma, Feng & Wang, Lu & Liang, Chao, 2022. "How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme Granger causality test," Resources Policy, Elsevier, vol. 78(C).
    4. Oscar V. De la Torre-Torres & Francisco Venegas-Martínez & Mᵃ Isabel Martínez-Torre-Enciso, 2021. "Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models," Mathematics, MDPI, vol. 9(2), pages 1-22, January.
    5. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M., 2020. "The (in)efficiency of NYMEX energy futures: A multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    6. Fernandes, Leonardo H.S. & Araújo, Fernando H.A., 2020. "Taxonomy of commodities assets via complexity-entropy causality plane," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    7. Haider Ali & Faheem Aslam & Paulo Ferreira, 2021. "Modeling Dynamic Multifractal Efficiency of US Electricity Market," Energies, MDPI, vol. 14(19), pages 1-16, September.
    8. Oscar V. De la Torre-Torres & Dora Aguilasocho-Montoya & María de la Cruz del Río-Rama, 2020. "A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    9. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
    10. Li, Dongxin & Hong, Yanran & Wang, Lu & Xu, Pengfei & Pan, Zhigang, 2022. "Extreme risk transmission among bitcoin and crude oil markets," Resources Policy, Elsevier, vol. 77(C).
    11. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    12. Xin-Lan Fu & Xing-Lu Gao & Zheng Shan & Yin-Jie Ma & Zhi-Qiang Jiang & Wei-Xing Zhou, 2025. "Multifractal characteristics and return predictability in the Chinese stock markets," Annals of Operations Research, Springer, vol. 352(3), pages 415-440, September.
    13. Muckenhaupt, Jan & Hoesli, Martin & Zhu, Bing, 2025. "Real estate as an inflation hedge: new evidence from an international analysis," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    14. Xi, Yue & Zeng, Qing & Lu, Xinjie & Huynh, Toan L.D., 2022. "Oil and renewable energy stock markets: Unique role of extreme shocks," Energy Economics, Elsevier, vol. 109(C).
    15. Raphael Amaro & Carlos Pinho, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
    16. Song, Shiyu, 2024. "The valuation of arithmetic Asian options with mean reversion and jump clustering," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    17. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2021. "A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance," Mathematics, MDPI, vol. 9(9), pages 1-28, May.
    18. Oscar V. De la Torre-Torres & María de la Cruz del Río-Rama & Álvarez-García José, 2024. "Non-Commodity Agricultural Price Hedging with Minimum Tracking Error Portfolios: The Case of Mexican Hass Avocado," Agriculture, MDPI, vol. 14(10), pages 1-28, September.
    19. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    20. Wang, Lu & Ma, Feng & Niu, Tianjiao & He, Chengting, 2020. "Crude oil and BRICS stock markets under extreme shocks: New evidence," Economic Modelling, Elsevier, vol. 86(C), pages 54-68.
    21. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.

  16. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.

    Cited by:

    1. Lu, Ye & Suthaharan, Neyavan, 2023. "Electricity price spike clustering: A zero-inflated GARX approach," Energy Economics, Elsevier, vol. 124(C).
    2. Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019. "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, vol. 79(C), pages 45-58.
    3. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    4. Giuseppe Cavaliere & Ye Lu & Anders Rahbek & Jacob St{ae}rk-{O}stergaard, 2021. "Bootstrap Inference for Hawkes and General Point Processes," Papers 2104.03122, arXiv.org, revised Sep 2021.
    5. Sheybanivaziri, Samaneh & Le Dréau, Jérôme & Kazmi, Hussain, 2024. "Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead," Discussion Papers 2024/1, Norwegian School of Economics, Department of Business and Management Science.
    6. Wong, Jin Boon & Zhang, Qin, 2022. "Impact of carbon tax on electricity prices and behaviour," Finance Research Letters, Elsevier, vol. 44(C).
    7. Philip Protter & Qianfan Wu & Shihao Yang, 2021. "Order Book Queue Hawkes-Markovian Modeling," Papers 2107.09629, arXiv.org, revised Jan 2022.
    8. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    9. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    10. Ming, Wei & Nazifi, Fatemeh & Trück, Stefan, 2024. "Emission intensities in the Australian National Electricity Market – An econometric analysis," Energy Economics, Elsevier, vol. 129(C).
    11. Nishio, Kazuki & Hoshino, Takahiro, 2022. "Joint modeling of effects of customer tier program on customer purchase duration and purchase amount," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    12. Hui Qu & Tianyang Wang & Peng Shangguan & Mengying He, 2024. "Revisiting the puzzle of jumps in volatility forecasting: The new insights of high‐frequency jump intensity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 218-251, February.
    13. Horst, Ulrich & Xu, Wei, 2021. "Functional limit theorems for marked Hawkes point measures," Stochastic Processes and their Applications, Elsevier, vol. 134(C), pages 94-131.
    14. Tselika, Kyriaki & Tselika, Maria & Demetriades, Elias, 2024. "Quantifying the short-term asymmetric effects of renewable energy on the electricity merit-order curve," Energy Economics, Elsevier, vol. 132(C).
    15. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    16. Emma Hubert & Dimitrios Lolas & Ronnie Sircar, 2026. "Trading Electrons: Predicting DART Spread Spikes in ISO Electricity Markets," Papers 2601.05085, arXiv.org, revised May 2026.
    17. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    18. Manuel Zamudio López & Hamidreza Zareipour, 2025. "Modeling the Duration of Electricity Price Spikes Using Survival Analysis," Energies, MDPI, vol. 18(19), pages 1-25, October.
    19. Nazifi, Fatemeh & Trück, Stefan & Zhu, Liangxu, 2021. "Carbon pass-through rates on spot electricity prices in Australia," Energy Economics, Elsevier, vol. 96(C).
    20. Yan, Guan & Trück, Stefan, 2020. "A dynamic network analysis of spot electricity prices in the Australian national electricity market," Energy Economics, Elsevier, vol. 92(C).
    21. Pawel Maryniak & Stefan Trueck & Rafal Weron, 2016. "Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets," HSC Research Reports HSC/16/10, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    22. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    23. Lau, Chi Keung Marco & Wojewodzki, Michal & Dai, Xingyu & Wang, Qunwei, 2025. "Detecting the macro drivers in the Australian National Electricity Market asymmetric volatility co-movement," Energy Economics, Elsevier, vol. 143(C).
    24. Rangarajan, Arvind & Svec, Jiri & Foley, Sean & Trück, Stefan, 2025. "Revisiting the crisis: An empirical analysis of the NEM suspension," Energy Economics, Elsevier, vol. 141(C).
    25. Bigerna, Simona & Bollino, Carlo Andrea & Ciferri, Davide & Polinori, Paolo, 2017. "Renewables diffusion and contagion effect in Italian regional electricity markets: Assessment and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 199-211.
    26. Fernanda Fuentes & Rodrigo Herrera & Adam Clements, 2016. "Modelling Extreme Risks in Commodities and Commodity Currencies," NCER Working Paper Series 115, National Centre for Econometric Research.
    27. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    28. Pham, Son Duy & Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2025. "Tail risk connectedness in the Australian National Electricity Markets: The impact of rare events," Energy Economics, Elsevier, vol. 141(C).
    29. Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
    30. Hung Do & Rabindra Nepal & Russell Smyth, 2020. "Interconnectedness in the Australian National Electricity Market: A Higher‐Moment Analysis," The Economic Record, The Economic Society of Australia, vol. 96(315), pages 450-469, December.
    31. Ulrich Horst & Wei Xu, 2019. "Functional Limit Theorems for Marked Hawkes Point Measures ," Working Papers hal-02443841, HAL.
    32. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    33. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    34. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    35. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Value-at-risk methodologies for effective energy portfolio risk management," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 197-212.
    36. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    37. Wierzbowski, Michal & Filipiak, Izabela, 2017. "Enhanced operational reserve as a tool for development of optimal energy mix," Energy Policy, Elsevier, vol. 102(C), pages 602-615.
    38. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    39. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    40. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    41. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    42. Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2024. "Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study," Forecasting, MDPI, vol. 6(1), pages 1-23, February.
    43. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).

  17. Alexander Karmann & Rodrigo Herrera, 2014. "Special Issue: Issues in Asia. Guest Editor: Laixun Zhao," Review of Development Economics, Wiley Blackwell, vol. 18(2), pages 354-371, May.

    Cited by:

    1. Chen, Muzi & Li, Nan & Zheng, Lifen & Huang, Difang & Wu, Boyao, 2022. "Dynamic correlation of market connectivity, risk spillover and abnormal volatility in stock price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    2. Huang, Ran & Zhou, Qi & Chang, Yingxin & Hu, Die & Wang, Yongmin, 2026. "Credit risk contagion across China’s real-estate industrial chain," The Quarterly Review of Economics and Finance, Elsevier, vol. 105(C).
    3. Muzi Chen & Nan Li & Lifen Zheng & Difang Huang & Boyao Wu, 2024. "Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price," Papers 2403.19363, arXiv.org.

  18. Herrera, Rodrigo & González, Nicolás, 2014. "The modeling and forecasting of extreme events in electricity spot markets," International Journal of Forecasting, Elsevier, vol. 30(3), pages 477-490.

    Cited by:

    1. Sheybanivaziri, Samaneh & Le Dréau, Jérôme & Kazmi, Hussain, 2024. "Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead," Discussion Papers 2024/1, Norwegian School of Economics, Department of Business and Management Science.
    2. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    3. Jiao, Ying & Ma, Chunhua & Scotti, Simone & Sgarra, Carlo, 2019. "A branching process approach to power markets," Energy Economics, Elsevier, vol. 79(C), pages 144-156.
    4. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    5. Emma Hubert & Dimitrios Lolas & Ronnie Sircar, 2026. "Trading Electrons: Predicting DART Spread Spikes in ISO Electricity Markets," Papers 2601.05085, arXiv.org, revised May 2026.
    6. Ying Jiao & Chunhua Ma & Simone Scotti & Carlo Sgarra, 2019. "A branching process approach to power markets," Post-Print hal-02954986, HAL.
    7. Auerbach, Jonathan & Wan, Phyllis, 2020. "Forecasting the urban skyline with extreme value theory," International Journal of Forecasting, Elsevier, vol. 36(3), pages 814-828.
    8. Bigerna, Simona & Bollino, Carlo Andrea & Ciferri, Davide & Polinori, Paolo, 2017. "Renewables diffusion and contagion effect in Italian regional electricity markets: Assessment and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 199-211.
    9. Bégin, Jean-François & Gómez, Fabio & Ignatieva, Katja & Li, Han, 2025. "The stochastic behavior of electricity prices under scrutiny: Evidence from spot and futures markets," Energy Economics, Elsevier, vol. 144(C).
    10. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.
    11. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    12. Song, Shiyu, 2024. "The valuation of arithmetic Asian options with mean reversion and jump clustering," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    13. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    14. Giorgia Callegaro & Andrea Mazzoran & Carlo Sgarra, 2019. "A Self-Exciting Modelling Framework for Forward Prices in Power Markets," Papers 1910.13286, arXiv.org.
    15. Stephen Chan & Saralees Nadarajah, 2015. "Extreme value analysis of electricity demand in the UK," Applied Economics Letters, Taylor & Francis Journals, vol. 22(15), pages 1246-1251, October.
    16. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    17. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    18. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
    19. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    20. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).
    21. Thomas Deschatre & Olivier F'eron & Pierre Gruet, 2021. "A survey of electricity spot and futures price models for risk management applications," Papers 2103.16918, arXiv.org, revised Jul 2021.

  19. Herrera, Rodrigo & Schipp, Bernhard, 2014. "Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 218-238.

    Cited by:

    1. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    2. Ahmad, Wasim & Kutan, Ali M. & Chahal, Rishman Jot Kaur & Kattumuri, Ruth, 2021. "COVID-19 pandemic and firm-level dynamics in the USA, UK, Europe, and Japan," LSE Research Online Documents on Economics 112454, London School of Economics and Political Science, LSE Library.
    3. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).
    4. Jing, Bo & Li, Shenghong & Ma, Yong, 2021. "Consistent pricing of VIX options with the Hawkes jump-diffusion model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    5. Florent Kanga Gbongue & Cyrille Gueï Okou & Cédric Mbeng Mezui, 2024. "L'impact de la crise russo‐ukrainienne sur les marchés financiers africains," African Development Review, African Development Bank, vol. 36(S1), pages 43-58, December.
    6. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    7. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    8. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    9. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    10. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    11. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).

  20. Herrera, Rodrigo, 2013. "Energy risk management through self-exciting marked point process," Energy Economics, Elsevier, vol. 38(C), pages 64-76.

    Cited by:

    1. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2018. "Modeling extreme risks in commodities and commodity currencies," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 108-120.
    2. Herrera, Rodrigo & Schipp, Bernhard, 2014. "Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 218-238.
    3. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2019. "Quantifying Risk in Traditional Energy and Sustainable Investments," Sustainability, MDPI, vol. 11(3), pages 1-22, January.
    4. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    5. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.
    6. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    7. Song, Shiyu, 2024. "The valuation of arithmetic Asian options with mean reversion and jump clustering," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    8. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    9. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    10. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.

  21. Herrera, Rodrigo & Schipp, Bernhard, 2013. "Value at risk forecasts by extreme value models in a conditional duration framework," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 33-47.

    Cited by:

    1. Buccioli, Alice & Kokholm, Thomas & Nicolosi, Marco, 2019. "Expected shortfall and portfolio management in contagious markets," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 100-115.
    2. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Hong, Yanran & Ma, Feng & Wang, Lu & Liang, Chao, 2022. "How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme Granger causality test," Resources Policy, Elsevier, vol. 78(C).
    4. Herrera, Rodrigo & Schipp, Bernhard, 2014. "Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 218-238.
    5. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    6. Li, Dongxin & Hong, Yanran & Wang, Lu & Xu, Pengfei & Pan, Zhigang, 2022. "Extreme risk transmission among bitcoin and crude oil markets," Resources Policy, Elsevier, vol. 77(C).
    7. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2025. "Tail risk dynamics of banks with score-driven extreme value models," Journal of Empirical Finance, Elsevier, vol. 81(C).
    8. Stindl, Tom, 2023. "Forecasting intraday market risk: A marked self-exciting point process with exogenous renewals," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 182-198.
    9. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
    10. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    11. Herrera, Rodrigo & González, Nicolás, 2014. "The modeling and forecasting of extreme events in electricity spot markets," International Journal of Forecasting, Elsevier, vol. 30(3), pages 477-490.
    12. Fernanda Fuentes & Rodrigo Herrera & Adam Clements, 2016. "Modelling Extreme Risks in Commodities and Commodity Currencies," NCER Working Paper Series 115, National Centre for Econometric Research.
    13. Zouaoui Chikr-Elmezouar & Ali Laksaci & Ibrahim M. Almanjahie & Fatimah Alshahrani, 2025. "Nonparametric Estimation of Dynamic Value-at-Risk: Multifunctional GARCH Model Case," Mathematics, MDPI, vol. 13(12), pages 1-20, June.
    14. Bień-Barkowska, Katarzyna & Herrera, Rodrigo, 2026. "When to be discrete: The importance of time formulation in the modeling of extreme events in finance," International Journal of Forecasting, Elsevier, vol. 42(1), pages 61-84.
    15. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    16. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    17. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    18. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
    19. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.

  22. Herrera, R. & Eichler, S., 2011. "Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2916-2930, November.

    Cited by:

    1. Stefan Eichler, 2012. "The impact of banking and sovereign debt crisis risk in the eurozone on the euro/US dollar exchange rate," Applied Financial Economics, Taylor & Francis Journals, vol. 22(15), pages 1215-1232, August.
    2. Michael A. Goldstein & Joseph McCarthy & Alexei G. Orlov, 2019. "The Core, Periphery, and Beyond: Stock Market Comovements among EU and Non‐EU Countries," The Financial Review, Eastern Finance Association, vol. 54(1), pages 5-56, February.

Chapters

  1. Rodrigo Herrera & Jörg Kalcsics & Stefan Nickel, 2008. "Reliability Models for the Uncapacitated Facility Location Problem with User Preferences," Operations Research Proceedings, in: Jörg Kalcsics & Stefan Nickel (ed.), Operations Research Proceedings 2007, pages 135-140, Springer.

    Cited by:

    1. Fu Wang & Manqing Ye & Hongbin Zhu & Dengjun Gu, 2022. "Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram," Sustainability, MDPI, vol. 14(13), pages 1-20, June.

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