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Fernando Perez-Cruz

Personal Details

First Name:Fernando
Middle Name:
Last Name:Perez-Cruz
Suffix:
RePEc Short-ID:ppe1091
[This author has chosen not to make the email address public]
https://scholar.google.com/citations?user=8FfrHw0AAAAJhl=en

Affiliation

Bank for International Settlements (BIS)

Basel, Switzerland
http://www.bis.org/
RePEc:edi:bisssch (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Fernando Perez-Cruz & Hyun Song Shin, 2025. "Putting AI agents through their paces on general tasks," BIS Working Papers 1245, Bank for International Settlements.
  2. Fernando Perez-Cruz & Hyun Song Shin, 2024. "Testing the cognitive limits of large language models," BIS Bulletins 83, Bank for International Settlements.
  3. Zoran Utkovski & Melanie F. Pradier & Viktor Stojkoski & Fernando Perez-Cruz & Ljupco Kocarev, 2017. "Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies," Papers 1711.07327, arXiv.org, revised Jul 2018.

Articles

  1. Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.
  2. Zoran Utkovski & Melanie F Pradier & Viktor Stojkoski & Fernando Perez-Cruz & Ljupco Kocarev, 2018. "Economic complexity unfolded: Interpretable model for the productive structure of economies," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
  3. Melanie F. Pradier & Francisco J. R. Ruiz & Fernando Perez-Cruz, 2016. "Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-28, January.
  4. Ruiz Francisco J. R. & Perez-Cruz Fernando, 2015. "A generative model for predicting outcomes in college basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 39-52, March.
  5. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.

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. Zoran Utkovski & Melanie F. Pradier & Viktor Stojkoski & Fernando Perez-Cruz & Ljupco Kocarev, 2017. "Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies," Papers 1711.07327, arXiv.org, revised Jul 2018.

    Cited by:

    1. Campi, Mercedes & Dueñas, Marco & Ciarli, Tommaso, 2022. "Do Creative Industries Enhance Employment Growth? Regional Evidence from Colombia," IDB Publications (Working Papers) 11995, Inter-American Development Bank.
    2. Li, Zhiyuan & Doğan, Buhari & Ghosh, Sudeshna & Chen, Wei-Ming & Lorente, Daniel Balsalobre, 2024. "Economic complexity, natural resources and economic progress in the era of sustainable development: Findings in the context of resource deployment challenges," Resources Policy, Elsevier, vol. 88(C).
    3. Yimin Chen & Yulin Liu & Xin Fang, 2021. "The new evidence of China’s economic downturn: From structural bonus to structural imbalance," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-25, September.
    4. Emilie Sophie Le Caous & Fenghueih Huarng, 2021. "Economic Complexity and Human Development: Moderated by Logistics and International Migration," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    5. Abhijit Chakraborty & Hiroyasu Inoue & Yoshi Fujiwara, 2020. "Economic complexity of prefectures in Japan," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-13, August.
    6. Emilie Le Caous & Fenghueih Huarng, 2020. "Economic Complexity and the Mediating Effects of Income Inequality: Reaching Sustainable Development in Developing Countries," Sustainability, MDPI, vol. 12(5), pages 1-26, March.
    7. Inga Ivanova & Øivind Strand & Loet Leydesdorff, 2019. "An Eco-Systems Approach To Constructing Economic Complexity Measures: Endogenization Of The Technological Dimension Using Lotka–Volterra Equations," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-21, February.
    8. Olivera Kostoska & Sonja Mitikj & Petar Jovanovski & Ljupco Kocarev, 2020. "Core-periphery structure in sectoral international trade networks: A new approach to an old theory," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-24, April.
    9. Abhijit Chakraborty & Hiroyasu Inoue & Yoshi Fujiwara, 2020. "Economic complexity of prefectures in Japan," Papers 2002.05785, arXiv.org, revised Aug 2020.

Articles

  1. Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.

    Cited by:

    1. George Fatouros & Kostas Metaxas & John Soldatos & Manos Karathanassis, 2025. "MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents," Papers 2502.00415, arXiv.org.

  2. Zoran Utkovski & Melanie F Pradier & Viktor Stojkoski & Fernando Perez-Cruz & Ljupco Kocarev, 2018. "Economic complexity unfolded: Interpretable model for the productive structure of economies," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
    See citations under working paper version above.
  3. Melanie F. Pradier & Francisco J. R. Ruiz & Fernando Perez-Cruz, 2016. "Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-28, January.

    Cited by:

    1. Zoran Utkovski & Melanie F. Pradier & Viktor Stojkoski & Fernando Perez-Cruz & Ljupco Kocarev, 2017. "Economic Complexity Unfolded: Interpretable Model for the Productive Structure of Economies," Papers 1711.07327, arXiv.org, revised Jul 2018.

  4. Ruiz Francisco J. R. & Perez-Cruz Fernando, 2015. "A generative model for predicting outcomes in college basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 39-52, March.

    Cited by:

    1. Ludden Ian G. & Khatibi Arash & King Douglas M. & Jacobson Sheldon H., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    3. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.

  5. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.

    Cited by:

    1. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    2. Yushu Li & Hyunjoo Kim Karlsson, 2023. "Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1765-1790, April.
    3. Jun Lu & Shao Yi, 2022. "Reducing Overestimating and Underestimating Volatility via the Augmented Blending-ARCH Model," Applied Economics and Finance, Redfame publishing, vol. 9(2), pages 48-59, May.
    4. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    5. Guillermo Santamaría-Bonfil & Juan Frausto-Solís & Ignacio Vázquez-Rodarte, 2015. "Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 111-133, January.
    6. Nalbantov, G.I. & Franses, Ph.H.B.F. & Bioch, J.C. & Groenen, P.J.F., 2007. "Estimating the market share attraction model using support vector regressions," Econometric Institute Research Papers EI 2007-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Tristan Fletcher & John Shawe-Taylor, 2013. "Multiple Kernel Learning with Fisher Kernels for High Frequency Currency Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 217-240, August.
    8. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
    9. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
    10. Yong Shi & Wei Dai & Wen Long & Bo Li, 2021. "Deep Kernel Gaussian Process Based Financial Market Predictions," Papers 2105.12293, arXiv.org.
    11. Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
    12. Jun Lu & Shao Yi, 2022. "Reducing overestimating and underestimating volatility via the augmented blending-ARCH model," Papers 2203.12456, arXiv.org.
    13. Tristan Fletcher & Zakria Hussain & John Shawe-Taylor, 2010. "Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features," Papers 1011.6097, arXiv.org.
    14. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    15. Kim Karlsson, Hyunjoo & Li, Yushu, 2024. "Investigation of Swedish krona exchange rate volatility by APARCH-Support Vector Regression," Working Papers in Economics and Statistics 10/2024, Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    16. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
    17. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    18. Shusheng Ding & Tianxiang Cui & Yongmin Zhang & Jiawei Li, 2021. "Liquidity effects on oil volatility forecasting: From fintech perspective," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
    19. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, September.
    20. Nguyen, An Pham Ngoc & Mai, Tai Tan & Bezbradica, Marija & Crane, Martin, 2023. "Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    21. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 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-CMP: Computational Economics (2) 2025-02-10 2025-02-24
  2. NEP-NEU: Neuroeconomics (2) 2025-02-10 2025-02-24
  3. NEP-AIN: Artificial Intelligence (1) 2025-02-10
  4. NEP-BIG: Big Data (1) 2025-02-10
  5. NEP-HME: Heterodox Microeconomics (1) 2018-01-15

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