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Andrea Bucci

Personal Details

First Name:Andrea
Middle Name:
Last Name:Bucci
Suffix:
RePEc Short-ID:pbu464
https://sites.google.com/view/abucci

Affiliation

Dipartimento di Scienze Economiche e Sociali
Facoltà di Economia "Giorgio Fuà"
Università Politecnica delle Marche

Ancona, Italy
http://www.dises.univpm.it/
RePEc:edi:deancit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Andrea Bucci & Vito Ciciretti, 2021. "Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models," Papers 2104.03667, arXiv.org.
  2. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  3. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
  4. Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
  5. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.

Articles

  1. Bucci, Andrea & Ciciretti, Vito, 2022. "Market regime detection via realized covariances," Economic Modelling, Elsevier, vol. 111(C).
  2. Davide Golinelli & Andrea Bucci & Kadjo Yves Cedric Adja & Fabrizio Toscano, 2020. "Comment on: “The Italian NHS: What Lessons to Draw from COVID-19?”," Applied Health Economics and Health Policy, Springer, vol. 18(5), pages 739-741, October.
  3. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
  4. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
  5. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
  6. Davide Golinelli & Fabrizio Toscano & Andrea Bucci & Jacopo Lenzi & Maria Pia Fantini & Nicola Nante & Gabriele Messina, 2017. "Health Expenditure and All-Cause Mortality in the ‘Galaxy’ of Italian Regional Healthcare Systems: A 15-Year Panel Data Analysis," Applied Health Economics and Health Policy, Springer, vol. 15(6), pages 773-783, December.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Davide Golinelli & Andrea Bucci & Kadjo Yves Cedric Adja & Fabrizio Toscano, 2020. "Comment on: “The Italian NHS: What Lessons to Draw from COVID-19?”," Applied Health Economics and Health Policy, Springer, vol. 18(5), pages 739-741, October.

    Mentioned in:

    1. Chris Sampson’s journal round-up for 19th October 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-10-19 11:00:05

Working papers

  1. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    Cited by:

    1. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.

  2. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.

    Cited by:

    1. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    2. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    3. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    4. Wenbo Ge & Pooia Lalbakhsh & Leigh Isai & Artem Lensky & Hanna Suominen, 2023. "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data," Papers 2306.12446, arXiv.org, revised Jun 2023.
    5. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    6. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    7. Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
    8. Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
    9. Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li, 2022. "Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1458-1482, November.

  3. Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.

    Cited by:

    1. Philipp Ratz, 2022. "Nonparametric Value-at-Risk via Sieve Estimation," Papers 2205.07101, arXiv.org.
    2. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    3. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
    4. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    5. Natalia Roszyk & Robert 'Slepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers 2407.16780, arXiv.org.
    6. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Aug 2024.
    8. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    9. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    10. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
    11. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
    12. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    13. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
    14. Rangika Peiris & Minh-Ngoc Tran & Chao Wang & Richard Gerlach, 2024. "Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model," Papers 2408.13588, arXiv.org.
    15. Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.
    16. Ma, Chenyao & Yan, Sheng, 2022. "Deep learning in the Chinese stock market: The role of technical indicators," Finance Research Letters, Elsevier, vol. 49(C).
    17. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
    18. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    19. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    20. Kshitij Kakade & Aswini Kumar Mishra & Kshitish Ghate & Shivang Gupta, 2022. "Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 103-117, April.
    21. Guangying Liu & Ziyan Zhuang & Min Wang, 2024. "Forecasting the high‐frequency volatility based on the LSTM‐HIT model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1356-1373, August.
    22. Jonathan Chassot & Michael Creel, 2023. "Constructing Efficient Simulated Moments Using Temporal Convolutional Networks," Working Papers 1412, Barcelona School of Economics.
    23. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
    24. Ke Yang & Nan Hu & Fengping Tian, 2024. "Forecasting Crude Oil Volatility Using the Deep Learning‐Based Hybrid Models With Common Factors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1429-1446, August.
    25. Zian Wang & Xinyi Lu, 2024. "COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning," Papers 2409.08356, arXiv.org.
    26. Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
    27. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
    28. Caio Mário Mesquita & Cristiano Arbex Valle & Adriano César Machado Pereira, 2024. "Scenario Generation for Financial Data with a Machine Learning Approach Based on Realized Volatility and Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1879-1919, May.
    29. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.
    30. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    31. Zhou, Dong-hai & Liu, Xiao-xing, 2023. "Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    32. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    33. Chen Liu & Minh-Ngoc Tran & Chao Wang & Richard Gerlach & Robert Kohn, 2023. "Data Scaling Effect of Deep Learning in Financial Time Series Forecasting," Papers 2309.02072, arXiv.org, revised May 2024.

  4. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.

    Cited by:

    1. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    2. Isaenko, Sergey, 2023. "Trading strategies and the frequency of time-series," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 267-283.
    3. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    4. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.

Articles

  1. Bucci, Andrea & Ciciretti, Vito, 2022. "Market regime detection via realized covariances," Economic Modelling, Elsevier, vol. 111(C).

    Cited by:

    1. Ciciretti, Vito & Bucci, Andrea, 2023. "Building optimal regime-switching portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).

  2. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
    See citations under working paper version above.
  3. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    See citations under working paper version above.
  4. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    See citations under working paper version above.
  5. Davide Golinelli & Fabrizio Toscano & Andrea Bucci & Jacopo Lenzi & Maria Pia Fantini & Nicola Nante & Gabriele Messina, 2017. "Health Expenditure and All-Cause Mortality in the ‘Galaxy’ of Italian Regional Healthcare Systems: A 15-Year Panel Data Analysis," Applied Health Economics and Health Policy, Springer, vol. 15(6), pages 773-783, December.

    Cited by:

    1. Rostand Arland Yebetchou Tchounkeu & Raffaella Santolini & Giulio Palomba & Elvina Merkaj, 2024. "Healthcare Efficiency And Elderly Mortality In Italy," Working Papers 485, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Mohammad Reza Farzanegan, 2020. "Ageing Society and SARS-CoV-2 Mortality: Does the Healthcare Absorptive Capacity Matter?," JRFM, MDPI, vol. 13(11), pages 1-13, November.
    3. Marina Vercelli & Roberto Lillini & Fabrizio Stracci & Valerio Brunori & Alessio Gili & Fortunato Bianconi & Francesco La Rosa & Alberto Izzotti & Elodie Guillaume & Guy Launoy, 2020. "Cancer Mortality and Deprivation: Comparison Among the Performances of the European Deprivation Index, the Italian Deprivation Index and Local Socio-Health Deprivation Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(2), pages 599-620, September.
    4. Cirulli, Vanessa & Marini, Giorgia, 2023. "Are austerity measures really distressing? Evidence from Italy," Economics & Human Biology, Elsevier, vol. 49(C).
    5. Guccio, C. & Pignataro, G. & Vidoli, F., 2024. "It never rains but it pours: Austerity and mortality rate in peripheral areas," Economics & Human Biology, Elsevier, vol. 54(C).
    6. Sanmarchi Francesco & Esposito Francesco & Bucci Andrea & Toscano Fabrizio & Golinelli Davide, 2021. "Association between Economic Growth, Mortality, and Healthcare Spending in 31 High-Income Countries," Forum for Health Economics & Policy, De Gruyter, vol. 24(2), pages 101-118, December.
    7. Cristina Borra & Jerònia Pons-Pons & Margarita Vilar-Rodríguez, 2020. "Austerity, healthcare provision, and health outcomes in Spain," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(3), pages 409-423, April.
    8. Yonsu Kim & Jae Hong Kim, 2022. "What drives variations in public health and social services expenditures? the association between political fragmentation and local expenditure patterns," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(5), pages 781-789, July.
    9. Mohammad Reza Farzanegan, 2021. "The Effect of Public Corruption on Covid-19 Fatality Rate: A Cross-Country Examination," CESifo Working Paper Series 8938, CESifo.
    10. Ana Rosa Rubio-Salvador & Vicente Escudero-Vilaplana & José Antonio Marcos Rodríguez & Irene Mangues-Bafalluy & Beatriz Bernardez & Carlos García Collado & Roberto Collado-Borrell & María Dolores Alva, 2021. "Cost of Venous Thromboembolic Disease in Patients with Lung Cancer: COSTECAT Study," IJERPH, MDPI, vol. 18(2), pages 1-9, January.
    11. Emanuele Arcà & Francesco Principe & Eddy Van Doorslaer, 2020. "Death by austerity? The impact of cost containment on avoidable mortality in Italy," Health Economics, John Wiley & Sons, Ltd., vol. 29(12), pages 1500-1516, December.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ETS: Econometric Time Series (4) 2017-12-18 2019-07-29 2019-08-26 2019-11-11. Author is listed
  2. NEP-FOR: Forecasting (4) 2017-12-18 2019-07-29 2019-08-26 2019-11-11. Author is listed
  3. NEP-ORE: Operations Research (3) 2019-07-29 2019-08-26 2019-11-11. Author is listed
  4. NEP-BIG: Big Data (2) 2019-07-29 2019-08-26. Author is listed
  5. NEP-CMP: Computational Economics (2) 2019-07-29 2019-08-26. Author is listed
  6. NEP-ECM: Econometrics (2) 2017-12-18 2019-07-29. Author is listed
  7. NEP-RMG: Risk Management (2) 2017-12-18 2019-11-11. Author is listed
  8. NEP-HIS: Business, Economic and Financial History (1) 2017-12-18. Author is listed
  9. NEP-PAY: Payment Systems and Financial Technology (1) 2019-07-29. Author is listed

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