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Francesco Lisi

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. Massimiliano Caporin & Grégory M. Jannin & Francesco Lisi & Bertrand Maillet, 2014. "A Survey on the Four Families of Performance Measures," Post-Print hal-01243416, HAL.

    Cited by:

    1. Massimiliano Caporin & Michele Costola & Gregory Mathieu Jannin & Bertrand Maillet, 2016. "On the (Ab)Use of Omega?," Working Papers hal-01697640, HAL.
    2. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2015. "Asset Allocation Strategies Based On Penalized Quantile Regression," "Marco Fanno" Working Papers 0199, Dipartimento di Scienze Economiche "Marco Fanno".
    3. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    4. León, Angel & Navarro, Lluís & Nieto, Belén, 2019. "Screening rules and portfolio performance," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 642-662.
    5. Caporin, Massimiliano & Lisi, Francesco, 2013. "A Conditional Single Index model with Local Covariates for detecting and evaluating active portfolio management," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 236-249.
    6. Niu, Cuizhen & Wong, Wing-Keung & Zhu, Lixing, 2017. "Farinelli and Tibiletti ratio and Stochastic Dominance," MPRA Paper 82737, University Library of Munich, Germany.
    7. Voelzke, Jan, 2015. "Weakening the Gain–Loss-Ratio measure to make it stronger," Finance Research Letters, Elsevier, vol. 12(C), pages 58-66.
    8. Kristiaan Kerstens & Paolo Mazza & Tiantian Ren & Ignace van de Woestyne, 2022. "Multi-Time and Multi-Moment Nonparametric Frontier-Based Fund Rating: Proposal and Buy-and-Hold Backtesting Strategy," Post-Print hal-03833261, HAL.
    9. Carole Bernard & Massimiliano Caporin & Bertrand Maillet & Xiang Zhang, 2023. "Omega Compatibility: A Meta-analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 493-526, August.
    10. Sally G. Arcidiacono & Damiano Rossello, 2022. "A hybrid approach to the discrepancy in financial performance’s robustness," Operational Research, Springer, vol. 22(5), pages 5441-5476, November.
    11. Abdelbari El Khamlichi & Thi Hong Van Hoang & Wing‐keung Wong, 2016. "Is Gold Different for Islamic and Conventional Portfolios? A Sectorial Analysis," Post-Print hal-02964594, HAL.
    12. León, Ángel & Moreno, Manuel, 2015. "Lower Partial Moments under Gram Charlier Distribution: Performance Measures and Efficient Frontiers," QM&ET Working Papers 15-3, University of Alicante, D. Quantitative Methods and Economic Theory.
    13. Monica Billio & Massimiliano Caporin & Michele Costola, 2012. "Backward/forward optimal combination of performance measures for equity screening," Working Papers 2012_13, Department of Economics, University of Venice "Ca' Foscari".
    14. Andrey Leonidov & Ilya Tipunin & Ekaterina Serebryannikova, 2020. "On Evaluation of Risky Investment Projects. Investment Certainty Equivalence," Papers 2005.12173, arXiv.org.
    15. Philippe Bernard & Najat El Mekkaoui de Freitas & Bertrand Maillet, 2022. "A Financial Fraud Detection Indicator for Investors: An IDeA," Post-Print hal-02312401, HAL.
    16. Fischer, Thomas & Lundtofte , Frederik, 2018. "Unequal Returns: Using the Atkinson Index to Measure Financial Risk," Working Papers 2018:25, Lund University, Department of Economics.
    17. Lu, Jin-Ray & Li, Xiu-Yan, 2021. "Identifying the fair value of Sharpe ratio by an option valuation approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 63-70.
    18. Thi-Hong-Van Hoang & Wing-Keung Wong & Zhenzhen Zhu, 2015. "Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange," Post-Print hal-02010732, HAL.
    19. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2021. "A meta-measure of performance related to both investors and investments characteristics," Post-Print hal-03543398, HAL.
    20. Caporin, Massimiliano & Jimenez-Martin, Juan-Angel & Gonzalez-Serrano, Lydia, 2014. "Currency hedging strategies in strategic benchmarks and the global and Euro sovereign financial crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 159-177.
    21. Korn, Olaf & Möller, Philipp M. & Schwehm, Christian, 2019. "Drawdown measures: Are they all the same?," CFR Working Papers 19-04, University of Cologne, Centre for Financial Research (CFR).
    22. Dipankar Mondal & N. Selvaraju, 2020. "Upside Beta Ratio: A Performance Measure For Potential-Seeking Investors," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-26, April.
    23. Aytaç, Beysül & Hoang, Thi-Hong-Van & Mandou, Cyrille, 2016. "Wine: To drink or invest in? A study of wine as an investment asset in French portfolios," Research in International Business and Finance, Elsevier, vol. 36(C), pages 591-614.
    24. Amélie Charles & Olivier Darné & Jessica Fouilloux, 2016. "The impact of screening strategies on the performance of ESG indices," Working Papers hal-01344699, HAL.
    25. Potrykus Marcin, 2018. "Comparison of Investment Performance Measures Using the Example of Selected Stock Exchanges," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(2), pages 30-46, June.
    26. León, Angel & Moreno, Manuel, 2017. "One-sided performance measures under Gram-Charlier distributions," Journal of Banking & Finance, Elsevier, vol. 74(C), pages 38-50.
    27. Jan Voelzke, 2014. "Weakening the Gain-Loss-Ratio measure to make it stronger," CQE Working Papers 3114, Center for Quantitative Economics (CQE), University of Muenster.
    28. Bernard, Carole & Vanduffel, Steven & Ye, Jiang, 2019. "Optimal strategies under Omega ratio," European Journal of Operational Research, Elsevier, vol. 275(2), pages 755-767.
    29. Peyman Alipour & Ali Foroush Bastani, 2023. "Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market," Papers 2305.12539, arXiv.org.
    30. Anna E. Olkova, 2017. "Mutual Funds Performance Assessment Techniques: Comparative Analysis," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 85-95, June.
    31. Auer, Benjamin R. & Marohn, Marcel, 2024. "Computational dynamics of information ratios," Economics Letters, Elsevier, vol. 236(C).
    32. Elisa Pagani, 2015. "Certainty Equivalent: Many Meanings of a Mean," Working Papers 24/2015, University of Verona, Department of Economics.

  2. Caporin, Massimiliano & Lisi, Francesco, 2011. "Comparing and selecting performance measures using rank correlations," Economics Discussion Papers 2011-14, Kiel Institute for the World Economy (IfW Kiel).

    Cited by:

    1. León, Angel & Navarro, Lluís & Nieto, Belén, 2019. "Screening rules and portfolio performance," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 642-662.
    2. Massimiliano Caporin & Grégory M. Jannin & Francesco Lisi & Bertrand Maillet, 2014. "A Survey on the Four Families of Performance Measures," Post-Print hal-01243416, HAL.
    3. León, Ángel & Moreno, Manuel, 2015. "Lower Partial Moments under Gram Charlier Distribution: Performance Measures and Efficient Frontiers," QM&ET Working Papers 15-3, University of Alicante, D. Quantitative Methods and Economic Theory.
    4. Anand, Abhinav & Li, Tiantian & Kurosaki, Tetsuo & Kim, Young Shin, 2016. "Foster–Hart optimal portfolios," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 117-130.
    5. Korn, Olaf & Möller, Philipp M. & Schwehm, Christian, 2019. "Drawdown measures: Are they all the same?," CFR Working Papers 19-04, University of Cologne, Centre for Financial Research (CFR).
    6. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    7. Zhang, Hanxiong & Auer, Benjamin R. & Vortelinos, Dimitrios I., 2018. "Performance ranking (dis)similarities in commodity markets," Global Finance Journal, Elsevier, vol. 35(C), pages 115-137.

  3. Massimiliano Caporin & Francesco Lisi, 2009. "Comparing and selecting performance measures for ranking assets," "Marco Fanno" Working Papers 0099, Dipartimento di Scienze Economiche "Marco Fanno".

    Cited by:

    1. Marco Taboga, 2014. "The Riskiness of Corporate Bonds," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 693-713, June.
    2. Yildiz Selim & Abdelbari El Khamlichi, 2017. "The Performance Ranking of Emerging Markets Islamic Indices Using Risk Adjusted Performance Measures," Post-Print hal-01653400, HAL.
    3. León, Ángel & Moreno, Manuel, 2015. "Lower Partial Moments under Gram Charlier Distribution: Performance Measures and Efficient Frontiers," QM&ET Working Papers 15-3, University of Alicante, D. Quantitative Methods and Economic Theory.
    4. Monica Billio & Massimiliano Caporin & Michele Costola, 2012. "Backward/forward optimal combination of performance measures for equity screening," Working Papers 2012_13, Department of Economics, University of Venice "Ca' Foscari".
    5. Anand, Abhinav & Li, Tiantian & Kurosaki, Tetsuo & Kim, Young Shin, 2016. "Foster–Hart optimal portfolios," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 117-130.
    6. Korn, Olaf & Möller, Philipp M. & Schwehm, Christian, 2019. "Drawdown measures: Are they all the same?," CFR Working Papers 19-04, University of Cologne, Centre for Financial Research (CFR).
    7. López, Raquel & Esparcia, Carlos, 2021. "Analysis of the performance of volatility-based trading strategies on scheduled news announcement days: An international equity market perspective," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 32-54.
    8. Mohammad Reza Tavakoli Baghdadabad & Paskalis Glabadanidis, 2013. "Average Drawdown Risk and Capital Asset Pricing," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-21.

  4. F. Lisi & E. Otranto, 2008. "Clustering Mutual Funds by Return and Risk Levels," Working Paper CRENoS 200813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

    Cited by:

    1. R. Gargano & E. Otranto, 2013. "Financial Clustering in Presence of Dominant Markets," Working Paper CRENoS 201318, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.

  5. F, Lisi, 1997. "One-Step Prediction of Chaotic Time Series by Multivariate Reconstruction," Working Papers 97-02, Center for Research in Economics and Statistics.

    Cited by:

    1. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.

Articles

  1. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.

    Cited by:

    1. 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.
    2. Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar, 2023. "Forecasting electricity prices using bid data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1253-1271.
    3. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    4. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    5. 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.
    6. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
    7. Li, Zehang & Elías, Antonio & Morales, Juan M., 2024. "Clustering and forecasting of day-ahead electricity supply curves using a market-based distance," DES - Working Papers. Statistics and Econometrics. WS 43805, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. 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.
    9. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.

  2. Francesco Lisi and Enrico Edoli, 2018. "Analyzing and Forecasting Zonal Imbalance Signs in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).

    Cited by:

    1. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2021. "Electricity balancing as a market equilibrium," EconStor Preprints 233852, ZBW - Leibniz Information Centre for Economics.
    2. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    3. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Karakoyun, Ece Cigdem & Avci, Harun & Kocaman, Ayse Selin & Nadar, Emre, 2023. "Deviations from commitments: Markov decision process formulations for the role of energy storage," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    6. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2020. "Electricity balancing as a market equilibrium: Estimating supply and demand of imbalance energy," EconStor Preprints 223062, ZBW - Leibniz Information Centre for Economics.
    7. Daniel Felix Ahelegbey & Emmanuel Senyo Fianu & Luigi Grossi, 2020. "Modeling Risk Contagion in the Italian Zonal Electricity Market," DEM Working Papers Series 182, University of Pavia, Department of Economics and Management.
    8. Kaneko, Nanae & Fujimoto, Yu & Hayashi, Yasuhiro, 2022. "Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand: Case study of the Japanese electricity market," Applied Energy, Elsevier, vol. 313(C).
    9. Backer, Martijn & Keles, Dogan & Kraft, Emil, 2023. "The economic impacts of integrating European balancing markets: The case of the newly installed aFRR energy market-coupling platform PICASSO," Energy Economics, Elsevier, vol. 128(C).
    10. Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
    11. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    12. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2021. "Electricity balancing as a market equilibrium: An instrument-based estimation of supply and demand for imbalance energy," Energy Economics, Elsevier, vol. 102(C).

  3. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.

    Cited by:

    1. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    2. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    3. Milstein, Irena & Tishler, Asher, 2019. "On the effects of capacity payments in competitive electricity markets: Capacity adequacy, price cap, and reliability," Energy Policy, Elsevier, vol. 129(C), pages 370-385.
    4. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    5. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports HSC/18/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Grzegorz Marcjasz & Jesus Lago & Rafa{l} Weron, 2020. "Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs," Papers 2008.08006, arXiv.org.
    7. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    8. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    9. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
    10. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    11. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    12. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    13. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.

  4. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.

    Cited by:

    1. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.
    2. Д.О. Афанасьев1 & * & Е.А. Федорова2 & **, 2019. "Краткосрочное Прогнозирование Цены Электроэнергии На Российском Рынке С Использованием Класса Моделей Scarx," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(1), pages 68-84, январь.
    3. Mustafa Gülerce & Gazanfer Ünal, 2018. "Electricity price forecasting using multiple wavelet coherence method: Comparison of ARMA versus VARMA," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-20, March.
    4. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    5. Alexios Lekidis & Elpiniki I. Papageorgiou, 2023. "Edge-Based Short-Term Energy Demand Prediction," Energies, MDPI, vol. 16(14), pages 1-20, July.
    6. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    7. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    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.
    9. Rafal Weron & Michal Zator, 2014. "A note on using the Hodrick-Prescott filter in electricity markets," HSC Research Reports HSC/14/04, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    10. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    11. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    12. Loi, Tian Sheng Allan & Ng, Jia Le, 2018. "Anticipating electricity prices for future needs – Implications for liberalised retail markets," Applied Energy, Elsevier, vol. 212(C), pages 244-264.
    13. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    14. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    15. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    16. 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.
    17. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.
    18. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    19. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports HSC/18/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    20. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    21. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    22. Florian Ziel & Rafal Weron, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Papers 1805.06649, arXiv.org.
    23. 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.
    24. Wei Wei & Asger Lunde, 2023. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1647-1679.
    25. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    26. Pawel Maryniak & Rafal Weron, 2014. "Forecasting the occurrence of electricity price spikes in the UK power market," HSC Research Reports HSC/14/11, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    27. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2017. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting," HSC Research Reports HSC/17/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    28. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    29. Sapio, Alessandro, 2015. "The effects of renewables in space and time: A regime switching model of the Italian power price," Energy Policy, Elsevier, vol. 85(C), pages 487-499.
    30. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    31. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    32. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).

  5. Massimiliano Caporin & Grégory M. Jannin & Francesco Lisi & Bertrand B. Maillet, 2014. "A Survey On The Four Families Of Performance Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 917-942, December.
    See citations under working paper version above.
  6. Caporin, Massimiliano & Lisi, Francesco, 2013. "A Conditional Single Index model with Local Covariates for detecting and evaluating active portfolio management," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 236-249.

    Cited by:

    1. Chia-Lin Chang & David Allen & Michael McAleer, 2013. "Recent Developments in Financial Economics and Econometrics: An Overview," Documentos de Trabajo del ICAE 2013-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Ortas, E. & Salvador, M. & Moneva, J.M., 2015. "Improved beta modeling and forecasting: An unobserved component approach with conditional heteroscedastic disturbances," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 27-51.
    3. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu, 2021. "An extended regularized Kalman filter based on Genetic Algorithm: Application to dynamic asset pricing models," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 28-44.
    4. Yang, Tingting & Huang, Xiaoxia, 2022. "Two new mean–variance enhanced index tracking models based on uncertainty theory," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).

  7. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    3. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    4. Figueiredo, Nuno Carvalho & Silva, Patrícia Pereira da & Bunn, Derek, 2016. "Weather and market specificities in the regional transmission of renewable energy price effects," Energy, Elsevier, vol. 114(C), pages 188-200.
    5. Jakub Nowotarski, 2013. "Short-term forecasting of electricity spot prices using model averaging (Krótkoterminowe prognozowanie spotowych cen energii elektrycznej z wykorzystaniem uśredniania modeli)," HSC Research Reports HSC/13/17, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Füss, Roland & Mahringer, Steffen & Prokopczuk, Marcel, 2013. "Electricity Derivatives Pricing with Forward-Looking Information," Working Papers on Finance 1317, University of St. Gallen, School of Finance.
    7. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    8. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    9. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    10. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    11. Thao Pham & Killian Lemoine, 2020. "Impacts of subsidized renewable electricity generation on spot market prices in Germany : Evidence from a GARCH model with panel data," Working Papers hal-02568268, HAL.
    12. Marie Bessec & Julien Fouquau & Sophie Meritet, 2014. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Working Papers 2014-588, Department of Research, Ipag Business School.
    13. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    14. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    15. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    16. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    17. 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.
    18. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    19. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    20. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    21. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    22. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    23. Rafal Weron & Michal Zator, 2014. "A note on using the Hodrick-Prescott filter in electricity markets," HSC Research Reports HSC/14/04, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    24. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    25. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    26. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.
    27. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    28. Umut Ugurlu & Oktay Tas & Aycan Kaya & Ilkay Oksuz, 2018. "The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company," Energies, MDPI, vol. 11(8), pages 1-19, August.
    29. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    30. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
    31. Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    33. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    34. Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2013. "Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices," HSC Research Reports HSC/13/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    35. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    36. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    37. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    38. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market," Energies, MDPI, vol. 8(9), pages 1-23, September.
    39. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    40. Jakub Nowotarski & Rafal Weron, 2014. "Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices," HSC Research Reports HSC/14/03, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    41. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    42. Derek Bunn, Arne Andresen, Dipeng Chen, Sjur Westgaard, 2016. "Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    43. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    44. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    45. Huang, Yujun, 2024. "Do ESG ETFs provide downside risk protection during Covid-19? Evidence from forecast combination models," International Review of Financial Analysis, Elsevier, vol. 94(C).
    46. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    47. Hollyman, Ross & Petropoulos, Fotios & Tipping, Michael E., 2021. "Understanding forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 294(1), pages 149-160.
    48. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    49. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, vol. 9(11), pages 1-15, November.
    50. Jakub Nowotarski & Rafal Weron, 2013. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," HSC Research Reports HSC/13/12, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    51. Florian Ziel & Rafal Weron, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Papers 1805.06649, arXiv.org.
    52. Martina Assereto & Julie Byrne, 2020. "The Implications of Policy Uncertainty on Solar Photovoltaic Investment," Energies, MDPI, vol. 13(23), pages 1-20, November.
    53. Afanasyev, Dmitriy & Fedorova, Elena, 2015. "The long-term trends on Russian electricity market: comparison of empirical mode and wavelet decompositions," MPRA Paper 62391, University Library of Munich, Germany.
    54. Arthur Thomas & Olivier Massol & Benoît Sévi, 2020. "How are Day-Ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas ?," Working Papers hal-03178474, HAL.
    55. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    56. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    57. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    58. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    59. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    60. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2017. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting," HSC Research Reports HSC/17/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    61. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    62. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    63. Jakub Nowotarski & Bidong Liu & Rafal Weron & Tao Hong, 2015. "Improving short term load forecast accuracy via combining sister forecasts," HSC Research Reports HSC/15/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    64. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    65. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
    66. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    67. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    68. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    69. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    70. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    71. Marin Cerjan & Ana Petričić & Marko Delimar, 2019. "HIRA Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 12(3), pages 1-32, February.
    72. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    73. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    74. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    75. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    76. Miguel Pinhão & Miguel Fonseca & Ricardo Covas, 2022. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    77. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    78. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    79. Rick Steinert & Florian Ziel, 2018. "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures," Papers 1801.10583, arXiv.org.
    80. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).
    81. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    82. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    83. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    84. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2016. "The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions," Energy Economics, Elsevier, vol. 56(C), pages 432-442.

  8. Francesco Lisi & Massimiliano Caporin, 2012. "On the role of risk in the Morningstar rating for mutual funds," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1477-1486, October.

    Cited by:

    1. Kozo Omori & Tomoki Kitamura, 2021. "Managers’ skills and fund flows in the Japanese mutual fund market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 39(4), pages 675-696, November.
    2. Daniel Chiew & Judy Qiu & Sirimon Treepongkaruna & Jiping Yang & Chenxiao Shi, 2019. "The predictive ability of the expected utility-entropy based fund rating approach: A comparison investigation with Morningstar ratings in US," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-22, April.
    3. Massimiliano Caporin & Grégory M. Jannin & Francesco Lisi & Bertrand Maillet, 2014. "A Survey on the Four Families of Performance Measures," Post-Print hal-01243416, HAL.

  9. Giovanna Menardi & Francesco Lisi, 2012. "Are performance measures equally stable?," Annals of Finance, Springer, vol. 8(4), pages 553-570, November.

    Cited by:

    1. Kristiaan Kerstens & Paolo Mazza & Tiantian Ren & Ignace van de Woestyne, 2022. "Multi-Time and Multi-Moment Nonparametric Frontier-Based Fund Rating: Proposal and Buy-and-Hold Backtesting Strategy," Post-Print hal-03833261, HAL.
    2. Thomas A. Severini, 2016. "A nonparametric approach to measuring the sensitivity of an asset’s return to the market," Annals of Finance, Springer, vol. 12(2), pages 179-199, May.
    3. M. Haley, 2014. "Gaussian and logistic adaptations of smoothed safety first," Annals of Finance, Springer, vol. 10(2), pages 333-345, May.

  10. Caporin, Massimiliano & Lisi, Francesco, 2011. "Comparing and selecting performance measures using rank correlations," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-34.
    See citations under working paper version above.
  11. Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 487-506, November.

    Cited by:

    1. Xuan Vinh Vo & Thi Tuan Anh Tran, 2021. "Higher-order comoments and asset returns: evidence from emerging equity markets," Annals of Operations Research, Springer, vol. 297(1), pages 323-340, February.
    2. F. Pizzutilo, 2012. "The behaviour of the distributions of stock returns: an analysis of the European market using the Pearson system of continuous probability distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(20), pages 1743-1752, October.
    3. 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.
    4. Chan, Raymond H. & Chow, Sheung-Chi & Guo, Xu & Wong, Wing-Keung, 2022. "Central moments, stochastic dominance, moment rule, and diversification with an application," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    5. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    7. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    8. Fresoli, Diego Eduardo, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Ravi Kashyap, 2024. "The Blockchain Risk Parity Line: Moving From The Efficient Frontier To The Final Frontier Of Investments," Papers 2407.09536, arXiv.org.
    10. Stavros Stavroyiannis, 2016. "Value-at-Risk and backtesting with the APARCH model and the standardized Pearson type IV distribution," Papers 1602.05749, arXiv.org.

  12. Francesco Lisi, 2011. "Dicing with the market: randomized procedures for evaluation of mutual funds," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 163-172.

    Cited by:

    1. Boby Chaitanya Villari & Mohammed Shahid Abdulla, 2017. "Portfolio choice decision making with NBP-effSAMWMIX: A Stochastic Multi-Armed Bandit Algorithm using Naïve Bandit Portfolio Approach," Working papers 219, Indian Institute of Management Kozhikode.
    2. Daniel Fricke, 2019. "Are specialist funds “special”?," Financial Management, Financial Management Association International, vol. 48(2), pages 441-472, June.

  13. Massimiliano Caporin & Francesco Lisi, 2010. "Misspecification tests for periodic long memory GARCH models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(1), pages 47-62, March.

    Cited by:

    1. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289, arXiv.org.
    2. Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.
    3. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting," Papers 2204.09568, arXiv.org.

  14. Silvano Bordignon & Massimiliano Caporin & Francesco Lisi, 2009. "Periodic Long-Memory GARCH Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 60-82.

    Cited by:

    1. Leschinski, Christian & Sibbertsen, Philipp, 2014. "Model Order Selection in Seasonal/Cyclical Long Memory Models," Hannover Economic Papers (HEP) dp-535, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Manabu Asai & Shelton Peiris & Michael McAleer, 2017. "Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory," Documentos de Trabajo del ICAE 2017-26, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Caporin, Massimiliano & Chang, Chia-Lin & McAleer, Michael, 2019. "Are the S&P 500 index and crude oil, natural gas and ethanol futures related for intra-day data?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 50-70.
    4. Caporin, Massimiliano & Preś, Juliusz & Torro, Hipolit, 2012. "Model based Monte Carlo pricing of energy and temperature Quanto options," Energy Economics, Elsevier, vol. 34(5), pages 1700-1712.
    5. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2013. "Stylized Facts and Dynamic Modeling of High-frequency Data on Precious Metals," Working Papers on Finance 1318, University of St. Gallen, School of Finance.
    7. Eduardo Rossi & Dean Fantazzini, 2015. "Long Memory and Periodicity in Intraday Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 922-961.
    8. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    9. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance 1409, University of St. Gallen, School of Finance.
    10. Massimiliano Caporin & Francesco Lisi, 2010. "Misspecification tests for periodic long memory GARCH models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(1), pages 47-62, March.
    11. Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.
    12. Heni Boubaker & Bassem Saidane & Mouna Ben Saad Zorgati, 2022. "Modelling the dynamics of stock market in the gulf cooperation council countries: evidence on persistence to shocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
    13. Khalifa, Ahmed & Caporin, Massimiliano & Hammoudeh, Shawkat, 2015. "Spillovers between energy and FX markets: The importance of asymmetry, uncertainty and business cycle," Energy Policy, Elsevier, vol. 87(C), pages 72-82.
    14. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    15. Rajesh Mohnot, 2011. "Forecasting Forex Volatility In Turbulent Times," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 5(1), pages 27-38.

  15. Matteo Grigoletto & Francesco Lisi, 2009. "Looking for skewness in financial time series," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 310-323, July.

    Cited by:

    1. So, Mike K.P. & Chan, Raymond K.S., 2014. "Bayesian analysis of tail asymmetry based on a threshold extreme value model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 568-587.
    2. Ewald, Christian & Hadina, Jelena & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Yahya, Muhammad, 2023. "Sample frequency robustness and accuracy in forecasting Value-at-Risk for Brent Crude Oil futures," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.
    4. 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.
    5. Stephen Taylor & Ming Fang, 2018. "Unbiased weighted variance and skewness estimators for overlapping returns," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-8, December.
    6. Nasari, Masoud M. & Ould-Haye, Mohamedou, 2021. "A consistent estimator for skewness of partial sums of dependent data," Statistics & Probability Letters, Elsevier, vol. 171(C).
    7. Zhu, Ke & Li, Wai Keung, 2014. "A new Pearson-type QMLE for conditionally heteroskedastic models," MPRA Paper 52732, University Library of Munich, Germany.
    8. Fujiwara, Ippei & Körber, Lena Mareen & Nagakura, Daisuke, 2013. "Asymmetry in government bond returns," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3218-3226.
    9. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    10. Timmy Elenjical & Patrick Mwangi & Barry Panulo & Chun-Sung Huang, 2016. "A comparative cross-regime analysis on the performance of GARCH-based value-at-risk models: Evidence from the Johannesburg stock exchange," Risk Management, Palgrave Macmillan, vol. 18(2), pages 89-110, August.
    11. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    12. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    13. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    14. Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 487-506, November.
    15. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    16. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    17. Mostafa R. Sarkandiz, 2023. "Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?," Papers 2302.08897, arXiv.org.
    18. Stavros Stavroyiannis, 2016. "Value-at-Risk and backtesting with the APARCH model and the standardized Pearson type IV distribution," Papers 1602.05749, arXiv.org.

  16. Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.

    Cited by:

    1. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    2. Manabu Asai & Shelton Peiris & Michael McAleer, 2017. "Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory," Documentos de Trabajo del ICAE 2017-26, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Shelton Peiris & Manabu Asai & Michael McAleer, 2016. "Estimating and forecasting generalized fractional Long memory stochastic volatility models," Documentos de Trabajo del ICAE 2016-08, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. Caporin, Massimiliano & Chang, Chia-Lin & McAleer, Michael, 2019. "Are the S&P 500 index and crude oil, natural gas and ethanol futures related for intra-day data?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 50-70.
    5. Caporin, Massimiliano & Preś, Juliusz & Torro, Hipolit, 2012. "Model based Monte Carlo pricing of energy and temperature Quanto options," Energy Economics, Elsevier, vol. 34(5), pages 1700-1712.
    6. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2013. "Stylized Facts and Dynamic Modeling of High-frequency Data on Precious Metals," Working Papers on Finance 1318, University of St. Gallen, School of Finance.
    8. Borovkova, Svetlana & Permana, Ferry J., 2009. "Implied volatility in oil markets," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2022-2039, April.
    9. Asai Manabu & Peiris Shelton & McAleer Michael & Allen David E., 2020. "Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-18, January.
    10. Eduardo Rossi & Dean Fantazzini, 2015. "Long Memory and Periodicity in Intraday Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 922-961.
    11. Boubaker, Heni & Sghaier, Nadia, 2015. "Semiparametric generalized long-memory modeling of some mena stock market returns: A wavelet approach," Economic Modelling, Elsevier, vol. 50(C), pages 254-265.
    12. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289, arXiv.org.
    13. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, vol. 42(3), pages 693-712, June.
    14. Artiach, Miguel & Arteche, Josu, 2012. "Doubly fractional models for dynamic heteroscedastic cycles," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2139-2158.
    15. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    16. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance 1409, University of St. Gallen, School of Finance.
    17. Heni Boubaker & Nadia Sghaier, 2014. "Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach," Working Papers 2014-66, Department of Research, Ipag Business School.
    18. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    19. Tommaso Proietti & Federico Maddanu, 2021. "Modelling Cycles in Climate Series: the Fractional Sinusoidal Waveform Process," CEIS Research Paper 518, Tor Vergata University, CEIS, revised 19 Oct 2021.
    20. Heni Boubaker & Bassem Saidane & Mouna Ben Saad Zorgati, 2022. "Modelling the dynamics of stock market in the gulf cooperation council countries: evidence on persistence to shocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
    21. Khalifa, Ahmed & Caporin, Massimiliano & Hammoudeh, Shawkat, 2015. "Spillovers between energy and FX markets: The importance of asymmetry, uncertainty and business cycle," Energy Policy, Elsevier, vol. 87(C), pages 72-82.
    22. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting," Papers 2204.09568, arXiv.org.
    23. Trinidad Segovia, J.E. & Fernández-Martínez, M. & Sánchez-Granero, M.A., 2019. "A novel approach to detect volatility clusters in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    24. Alexandra Chronopoulou & Frederi Viens, 2012. "Estimation and pricing under long-memory stochastic volatility," Annals of Finance, Springer, vol. 8(2), pages 379-403, May.
    25. Asai, M. & Peiris, S. & McAleer, M.J. & Allen, D.E., 2018. "Cointegrated Dynamics for A Generalized Long Memory Process," Econometric Institute Research Papers EI 2018-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    26. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  17. Francesco Lisi, 2007. "Testing asymmetry in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 687-696.

    Cited by:

    1. Mr. Julio Escolano & Vitor Gaspar, 2016. "Optimal Debt Policy Under Asymmetric Risk," IMF Working Papers 2016/178, International Monetary Fund.
    2. So, Mike K.P. & Chan, Raymond K.S., 2014. "Bayesian analysis of tail asymmetry based on a threshold extreme value model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 568-587.
    3. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    4. Valencia, Marisol & Bedoya, Alejandro, 2013. "Prueba de sesgo sobre rendimientos financieros en el mercado colombiano," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 80, pages 79-102, November.
    5. Pelagatti Matteo M, 2009. "Modelling Good and Bad Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-20, March.
    6. Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 487-506, November.
    7. Hasan F. Baklaci & Ömür Süer & Tezer Yelkenci̇, 2018. "Price Linkages Among Emerging Gold Futures Markets," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(05), pages 1345-1365, December.

  18. Luisa Bisaglia & Silvano Bordignon & Francesco Lisi, 2003. "k -Factor GARMA models for intraday volatility forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 10(4), pages 251-254.

    Cited by:

    1. Leschinski, Christian & Sibbertsen, Philipp, 2014. "Model Order Selection in Seasonal/Cyclical Long Memory Models," Hannover Economic Papers (HEP) dp-535, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. L.A. Gil-Alana & G.M. caporale, 2004. "Long-run and Cyclical Dynamics in the US Stock Market," Econometric Society 2004 Latin American Meetings 344, Econometric Society.
    3. Silvano Bordignon & Massimiliano Caporin & Francesco Lisi, 2009. "Periodic Long-Memory GARCH Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 60-82.
    4. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.
    5. Alva, Kenedy, 2009. "Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market," DES - Working Papers. Statistics and Econometrics. WS ws092809, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    7. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    8. Tommaso Proietti & Federico Maddanu, 2021. "Modelling Cycles in Climate Series: the Fractional Sinusoidal Waveform Process," CEIS Research Paper 518, Tor Vergata University, CEIS, revised 19 Oct 2021.
    9. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
    10. Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.
    11. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    12. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  19. Bordignon, Silvano & Lisi, Francesco, 2001. "Predictive accuracy for chaotic economic models," Economics Letters, Elsevier, vol. 70(1), pages 51-58, January.

    Cited by:

    1. Halbiniak, Zbigniew & Jóźwiak, Ireneusz J., 2007. "Deterministic chaos in the processor load," Chaos, Solitons & Fractals, Elsevier, vol. 31(2), pages 409-416.
    2. Adrien Bernard Bonache & Marc Filser, 2013. "Comment améliorer la prévision des ventes pour le marketing ? Les apports de la théorie du chaos," Post-Print hal-03822792, HAL.
    3. A. C. -L. Chian & E. L. Rempel & C. Rogers, 2007. "Crisis-induced intermittency in non-linear economic cycles," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 211-218.
    4. Silvano Bordignon & Francesco Lisi, 2001. "Interval prediction for chaotic time series," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 117-140.

  20. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.

    Cited by:

    1. Yoshio Kajitani & A. Ian Mcleod & Keith W. Hipel, 2005. "Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 105-117.
    2. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    3. Supanee Harnphattananusorn, 2020. "Relationship between Thai Baht and Oil Price: A Neural Network Model," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 371-376.
    4. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    5. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
    6. Huseyin Ince & Ali Fehim Cebeci & Salih Zeki Imamoglu, 2019. "An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 817-831, February.
    7. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    8. Gaudart, Jean & Giusiano, Bernard & Huiart, Laetitia, 2004. "Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 547-570, January.
    9. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    10. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    11. Koffi Dumor & Komlan Gbongli, 2021. "Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 17(02), pages 13-26.
    12. Manish Kumar, 2010. "Modelling Exchange Rate Returns Using Non-linear Models," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(1), pages 101-125, January.
    13. Chen, Shiyi & Jeong, Kiho & Härdle, Wolfgang Karl, 2008. "Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns," SFB 649 Discussion Papers 2008-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    14. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
    15. Kyrtsou, Catherine & Terraza, Michel, 2002. "Stochastic chaos or ARCH effects in stock series?: A comparative study," International Review of Financial Analysis, Elsevier, vol. 11(4), pages 407-431.
    16. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    17. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    18. Teresa Aparicio & Dulce Saura, 2013. "Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 5(10), pages 678-686.
    19. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.

  21. Lisi, Francesco & Medio, Alfredo, 1997. "Is a random walk the best exchange rate predictor?," International Journal of Forecasting, Elsevier, vol. 13(2), pages 255-267, June.

    Cited by:

    1. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.
    2. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    3. Thomakos, Dimitrios D. & Wang, Tao & Wille, Luc T., 2002. "Modeling daily realized futures volatility with singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 505-519.
    4. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    5. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Strozzi, Fernanda & Comenges, José-Manuel Zaldívar, 2006. "Towards a non-linear trading strategy for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 28(3), pages 601-615.
    8. Bordignon, Silvano & Lisi, Francesco, 2001. "Predictive accuracy for chaotic economic models," Economics Letters, Elsevier, vol. 70(1), pages 51-58, January.
    9. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    10. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    11. Strozzi, Fernanda & Zaldívar, José-Manuel & Zbilut, Joseph P., 2007. "Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 487-499.
    12. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    13. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.

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