IDEAS home Printed from https://ideas.repec.org/e/c/plu102.html

Thomas Lux

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. Alfarano, Simone & Lux, Thomas, 2010. "Extreme Value Theory as a Theoretical Background for Power Law Behavior," MPRA Paper 24718, University Library of Munich, Germany.

    Cited by:

    1. Noemi Schmitt & Frank Westerhoff, 2017. "Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1041-1070, November.
    2. Omar Blanco & Simone Alfarano, 2016. "Granularity of the business cycle fluctuations: The Spanish case," Working Papers 2016/25, Economics Department, Universitat Jaume I, Castellón (Spain).
    3. Laurent Gauthier, 2024. "Digital Humanities, Complexity Sciences and the Modeling of Ancient Greek Culture," Working Papers hal-03315002, HAL.
    4. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

  2. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2010. "Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation," MPRA Paper 24719, University Library of Munich, Germany.

    Cited by:

    1. Sandrine Jacob Leal & Mauro Napoletano, 2016. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High- Frequency Trading," Sciences Po Economics Publications (main) hal-03459346, HAL.
    2. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2006. "Estimation of a simple agent-based model of financial markets: An application to Australian stock and foreign exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 38-42.

  3. David Colander & Hans Föllmer & Armin Haas & Michael Goldberg & Katarina Juselius & Alan Kirman & Thomas Lux & Brigitte Sloth, 2009. "The Financial Crisis and the Systemic Failure of Academic Economics," Middlebury College Working Paper Series 0901, Middlebury College, Department of Economics.

    Cited by:

    1. Oswin Krüger Ruiz, 2020. "The Self According to Others: Explaining Social Preferences with Social Approbation," Economic Thought, World Economics Association, vol. 9(2), pages 38-54, December.
    2. Cross, R. & McNamara, H. & Pokrovskii, A.V., 2010. "Memory of Recessions," SIRE Discussion Papers 2010-40, Scottish Institute for Research in Economics (SIRE).
    3. Maziarz Mariusz, 2019. "A disequilibrium mechanism: When managerial decisions cause macroeconomic instability," Economics and Business Review, Sciendo, vol. 5(1), pages 79-92, March.
    4. Alessandro Spelta & Guido Ascari & Nicolò Pecora, 2012. "Boom and Burst in Housing Market with Heterogeneous Agents," Quaderni di Dipartimento 177, University of Pavia, Department of Economics and Quantitative Methods.
    5. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    6. Lans, Cheryl, 2012. "Money and the epistemologies of ignorance concerning climate change," MPRA Paper 62147, University Library of Munich, Germany.
    7. Rocco Mosconi & Paolo Paruolo, 2022. "A Conversation with Katarina Juselius," Econometrics, MDPI, vol. 10(2), pages 1-21, April.
    8. Gebhard Kirchgässner, 2009. "Die Krise der Wirtschaft: Auch eine Krise der Wirtschaftswissenschaften?," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 10(4), pages 436-468, November.
    9. Xing Chen & Yilei Wu & Yi Ding & Tongxin Zhang, 2025. "RETRACTED ARTICLE: Exploring the Nexus of Liquidity Regulation, Bank Risk-Taking, and Shadow Banking: A Comprehensive Analysis of Chinese Commercial Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 601-629, March.
    10. Su, Yang & Lucey, Brian M. & Jha, Ashish Kumar & Zheng, Yuqi, 2025. "Tracing the evolution of finance research: A topic modeling analysis of AJG-ranked journals," International Review of Financial Analysis, Elsevier, vol. 107(C).
    11. Poledna, Sebastian & Miess, Michael Gregor & Hommes, Cars & Rabitsch, Katrin, 2023. "Economic forecasting with an agent-based model," European Economic Review, Elsevier, vol. 151(C).
    12. Thomas Theobald, 2012. "Agent-based risk management - A regulatory approach to financial markets," IMK Working Paper 95-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    13. Felipe Rodrigues Sousa & Everton Sotto Tibiriçá Rosa, 2020. "Filosofia Econômica e Social: uma Conexão entre Alfred Marshall e John Maynard Keynes," Working papers - Textos para Discussao do Curso de Ciencias Economicas da UFG 083, Curso de Ciencias Economicas da Universidade Federal de Goias - FACE.
    14. Pinto, Hugo, 2009. "A Economia em Ebulição: Integrando o Plural e a Moral numa Ciência Económica Satisfatória [Economics in Turmoil: Integrating Moral and Plural in a Satisfactory Economic Science]," MPRA Paper 18718, University Library of Munich, Germany.
    15. Orlando, Giuseppe & Zimatore, Giovanna, 2018. "Recurrence quantification analysis of business cycles," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 82-94.
    16. Sherstnev, Mikhail, 2011. "Экономический Кризис, Мировая Экономика, Экономическая Наука И Экономическая Политика [Economic crisis, world economy, economics and economic policy]," MPRA Paper 31912, University Library of Munich, Germany.
    17. Mesly, Olivier & Chkir, Imed & Racicot, François-Éric, 2019. "Predatory cells and puzzling financial crises: Are toxic products good for the financial markets?," Economic Modelling, Elsevier, vol. 78(C), pages 11-31.
    18. Fatouh, Mahmoud & Markose, Sheri & Giansante, Simone, 2021. "The impact of quantitative easing on UK bank lending: Why banks do not lend to businesses?," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 928-953.
    19. Hassan Syed & Sema Yilmaz Genc, 2019. "The Queen Asked: State Of Mainstream Economics," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 28(2), pages 681-697, december.
    20. Røpke, Inge, 2020. "Econ 101—In need of a sustainability transition," Ecological Economics, Elsevier, vol. 169(C).
    21. Gonzalo Castañeda, 2010. "Crisis económicas y cambios de paradigma," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 25(2), pages 425-441.
    22. Maria Alvarado & Laura Muro & Kirk Lee Tennant, 2011. "Europe’s Crisis of Accounting," Chapters, in: David Howden (ed.), Institutions in Crisis, chapter 5, Edward Elgar Publishing.
    23. Peter Flaschel & Florian Hartmann & Chris Malikane & Christian R. Proaño, 2014. "A Behavioral Macroeconomic Model of Exchange Rate Fluctuations with Complex Market Expectations Formation," IEER Working Papers 98, Institute of Empirical Economic Research, Osnabrueck University, revised 29 Jan 2014.
    24. Krzysztof Jajuga & Józef Pociecha & Mirosław Szreder, 2024. "Statistical inference and statistical learning in economic research – selected challenges," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 2, pages 138-154.
    25. Chau, Michael & Lin, Chih-Yung & Lin, Tse-Chun, 2020. "Wisdom of crowds before the 2007–2009 global financial crisis," Journal of Financial Stability, Elsevier, vol. 48(C).
    26. Philipp Frey, 2021. "Visions of Automation: A Comparative Discussion of Two Approaches," Societies, MDPI, vol. 11(2), pages 1-21, June.
    27. Francisco Louçã & Alexandre Abreu & Gonçalo Pessa Costa, 2021. "Disarray at the headquarters: Economists and Central bankers tested by the subprime and the COVID recessions [Forward guidance without common knowledge]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(2), pages 273-296.
    28. Lengnick, Matthias & Wohltmann, Hans-Werner, 2011. "Agent-based financial markets and New Keynesian macroeconomics: A synthesis," Economics Working Papers 2011-09, Christian-Albrechts-University of Kiel, Department of Economics.
    29. Gunther Tichy, 2012. "The Sovereign Debt Crisis: Causes and Consequences," Austrian Economic Quarterly, WIFO, vol. 17(2), pages 95-107, May.
    30. Boubaker, Sabri & Essaddam, Naceur & Nguyen, Duc Khuong & Saadi, Samir, 2017. "On the robustness of week-day effect to error distributional assumption: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 114-130.
    31. Andrew Brown, 2013. "Methodological issues in theorising the financial, economic and social system: realistic and systematic abstraction," Working papers wpaper03, Financialisation, Economy, Society & Sustainable Development (FESSUD) Project.
    32. Olivier Mesly & Hareesh Mavoori, 2024. "Mapping borrowers’ and lenders’ interactions according to their dark financial profiles," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 1090-1104, December.
    33. Fischer, Thomas & Riedler, Jesper, 2012. "Prices, debt and market structure in an agent-based model of the financial market," ZEW Discussion Papers 12-045, ZEW - Leibniz Centre for European Economic Research.
    34. Stolzenburg, Ulrich, 2015. "The agent-based Solow growth model with endogenous business cycles," Economics Working Papers 2015-01, Christian-Albrechts-University of Kiel, Department of Economics.
    35. Ana-Maria Gavril, 2009. "Exchange Rate Risk: Heads or Tails," Advances in Economic and Financial Research - DOFIN Working Paper Series 35, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    36. Gunther Tichy, 2010. "War die Finanzkrise vorhersehbar?," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 11(4), pages 356-382, November.
    37. Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2015. "An agent-based “proof of principle” for Walrasian macroeconomic theory," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 150-183, June.
    38. Giovanni Dosi, 2012. "Economic Coordination and Dynamics: Some Elements of an Alternative "Evolutionary" Paradigm," LEM Papers Series 2012/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    39. Kirchgässner, Gebhard, 2014. "On Self-Interest and Greed," Economics Working Paper Series 1416, University of St. Gallen, School of Economics and Political Science.
    40. Johannes Buchner, 2025. "Critical Mathematical Economics and Progressive Data Science," Papers 2502.06015, arXiv.org, revised Apr 2025.
    41. McBrayer Markie & Shea Patrick E. & Kirkland Justin H., 2018. "The Financial Crisis, Fiscal Federalism, and the Creditworthiness of US State Governments," Statistics, Politics and Policy, De Gruyter, vol. 9(1), pages 1-30, June.
    42. Justus Haucap & Johannes Muck, 2015. "What drives the relevance and reputation of economics journals? An update from a survey among economists," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 849-877, June.
    43. Poul Thøis Madsen, 2013. "The Financial Crisis and Principles of Economics Textbooks," The Journal of Economic Education, Taylor & Francis Journals, vol. 44(3), pages 197-216, September.
    44. Mauro Napoletano & Jean-Luc Gaffard & Zakaria Babutsidze, 2012. "Agent Based Models A New Tool for Economic and Policy Analysis," Sciences Po Economics Publications (main) hal-01070338, HAL.
    45. Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Forthcomi.
    46. Kopczewski, Tomasz & Okhrimenko, Iana, 2019. "Can homo economicus be an altruist? A classroom experimental method," International Review of Economics Education, Elsevier, vol. 32(C), pages 1-1.
    47. Osharin Alexander & Verbus Valery, 2015. "Heterogeneous consumers and market structure in a monopolistically competitive setting," EERC Working Paper Series 15/03e, EERC Research Network, Russia and CIS.
    48. Diane Wilcox & Tim Gebbie, 2013. "Factorising equity returns in an emerging market through exogenous shocks and capital flows," Papers 1306.5302, arXiv.org, revised Jul 2013.
    49. Domenico Gatti & Edoardo Gaffeo & Mauro Gallegati, 2010. "Complex agent-based macroeconomics: a manifesto for a new paradigm," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(2), pages 111-135, December.
    50. Tom van Veen, 2020. "Have Macroeconomic Models Lost Their Connection with Economic Reality?," CESifo Working Paper Series 8256, CESifo.
    51. Zoya Mladenova, 2017. "Reflections of the Global Crisis 2008-2009 upon Economic Theory: Attempt for Generalization," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 3-40.
    52. Mauro Napoletano & Jean-Luc Gaffard & Zakaria Babutsidze, 2012. "Agent Based Models," Post-Print hal-03461262, HAL.
    53. Drakopoulos, Stavros A., 2016. "Economic Crisis, Economic Methodology and the Scientific Ideal of Physics," MPRA Paper 74306, University Library of Munich, Germany.
    54. Nibbering, Didier & Paap, Richard & van der Wel, Michel, 2018. "What do professional forecasters actually predict?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 288-311.
    55. Rodrigo López & Andrée Palet, 2015. "La UDP con nueva malla curricular, ¿Nueva economía o más de lo mismo?," Estudios Nueva Economía, Estudios Nueva Economía, vol. 5(2), pages 81-87.
    56. Christian Grimm & Jakob Kapeller & Stephan Puehringer, 2018. "Paradigms and Policies: The state of economics in the german-speaking countries," ICAE Working Papers 77, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    57. Schupp, Claudia & Wache, Benjamin, 2014. "Wie groß ist der Einfluss von deutschen Wirtschaftsforschungsinstituten? Ein Ranking anhand von RePEc-Daten [How large is the influence of German economic research institutes? A ranking analysis using RePEc data]," MPRA Paper 55519, University Library of Munich, Germany.
    58. David Colander & Hans Föllmer & Armin Haas & Michael Goldberg & Katarina Juselius & Alan Kirman & Thomas Lux & Birgitte Sloth, 2009. "The Financial Crisis and the Systemic Failure of Academic Economics," Discussion Papers 09-03, University of Copenhagen. Department of Economics.
    59. Rahim, Yasmin & Masih, Mansur, 2015. "Is gold good for hedging? lessons from the Malaysian sectoral stock indices," MPRA Paper 63928, University Library of Munich, Germany.
    60. Damien Francey & Ralph Bergmüller, 2012. "Images of Eyes Enhance Investments in a Real-Life Public Good," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-7, May.
    61. Yonatan Berman & Yoash Shapira & Eshel Ben-Jacob, 2014. "Unraveling Hidden Order in the Dynamics of Developed and Emerging Markets," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
    62. Jason Glynos & Robin Klimecki & Hugh Willmott, 2012. "Cooling Out The Marks," Journal of Cultural Economy, Taylor & Francis Journals, vol. 5(3), pages 297-320, January.
    63. Kakarot-Handtke, Egmont, 2012. "The rhetoric of failure: a hyper-dialog about method in economics and how to get things going," MPRA Paper 43276, University Library of Munich, Germany.
    64. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    65. Jun, Bogang & Kim, Tai-Yoo, 2015. "A neo-Schumpeterian perspective on the analytical macroeconomic framework: The expanded reproduction system," Hohenheim Discussion Papers in Business, Economics and Social Sciences 11-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    66. Vikas Kumar, 2016. "Economics, an Uncertain Glory," Homo Oeconomicus: Journal of Behavioral and Institutional Economics, Springer, vol. 33(4), pages 321-332, December.
    67. Caiani, Alessandro & Godin, Antoine & Caverzasi, Eugenio & Gallegati, Mauro & Kinsella, Stephen & Stiglitz, Joseph E., 2016. "Agent based-stock flow consistent macroeconomics: Towards a benchmark model," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 375-408.
    68. Di Domenico, Lorenzo & Raberto, Marco & Safarzynska, Karolina, 2023. "Resource scarcity, circular economy and the energy rebound: A macro-evolutionary input-output model," Energy Economics, Elsevier, vol. 128(C).
    69. Freeman, Alan, 2009. "The Economists of Tomorrow," MPRA Paper 15691, University Library of Munich, Germany.
    70. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    71. Dirk Helbing, 2013. "Economics 2.0: The Natural Step towards A Self-Regulating, Participatory Market Society," Papers 1305.4078, arXiv.org, revised Jun 2013.
    72. David Dequech, 2016. "Some Institutions (Social Norms And Conventions) Of Contemporary Mainstream Economics, Macroeconomics, And Financial Economics," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 006, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    73. Sherstnev, Mikhail, 2013. "World economy, economics and economic policy: what emerges after the crisis?," MPRA Paper 49019, University Library of Munich, Germany.
    74. Germana Bottone, 2009. "A new notion of progress: Institutional quality," ISAE Working Papers 117, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    75. Chami Figueira, F. & Moura, N.J. & Ribeiro, M.B., 2011. "The Gompertz–Pareto income distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 689-698.
    76. Jayme Lemke & John Kroencke, 2020. "Methodological confusions and the science wars in economics," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 33(1), pages 87-106, March.
    77. Andersson, Fredrik N. G., 2020. "Macroeconomic Equilibriums, Crises and Fiscal Policy," Working Papers 2020:21, Lund University, Department of Economics.
    78. Theresa Schäfer & Sebastian Utz, 2022. "Values-Based and Global Systemically Important Banks: Their Stability and the Impact of Regulatory Changes After the Financial Crisis on it," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(1), pages 5-32, March.
    79. Michael W. M. Roos, 2009. "Die deutsche Fiskalpolitik während der Wirtschaftskrise 2008/2009," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 10(4), pages 389-412, November.
    80. Dilger, Alexander, 2018. "Die Verantwortung von Wirtschaftswissenschaftlern für Wirtschaftskrisen und die Wirtschaft allgemein," Discussion Papers of the Institute for Organisational Economics 4/2018, University of Münster, Institute for Organisational Economics.
    81. Timothy C. Johnson, 2013. "Reciprocity as the foundation of Financial Economics," Papers 1310.2798, arXiv.org.
    82. Anna Ząbkowicz & Sławomir Czech, 2016. "Revisiting conventional wisdom: does financialization have to leave sovereigns subordinated?," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 47.
    83. Santiago J. Gangotena, 2017. "Dynamic coordinating non-equilibrium," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(1), pages 51-82, March.
    84. Sabrina Aufiero & Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2025. "Mapping Microscopic and Systemic Risks in TradFi and DeFi: a literature review," Papers 2508.12007, arXiv.org.
    85. Stephan Puehringer, 2021. "Zur Pluralitaet der oekonomischen Politikberatung in Deutschland," ICAE Working Papers 132, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    86. Cross, R. & McNamara, H. & Pokrovskii, A.V. & Kalachev, L., 2010. "Hysteresis in the fundamentals of macroeconomics," SIRE Discussion Papers 2010-36, Scottish Institute for Research in Economics (SIRE).
    87. Altug YALCINTAS, 2020. "Why is economics not part of a system of scientific ethics? A review essay on Wilfred Dolfsma and Ioana Negru’s The Ethical Formation of Economists," The Journal of Philosophical Economics, Bucharest Academy of Economic Studies, The Journal of Philosophical Economics, vol. 13(2), pages 202-214, November.
    88. Aldegwy, Mohamed & Thiemann, Matthias, 2016. "How economics got it wrong: Formalism, equilibrium modelling and pseudo-optimization in banking regulatory studies," SAFE Working Paper Series 138, Leibniz Institute for Financial Research SAFE.
    89. Hafner, Sarah & Anger-Kraavi, Annela & Monasterolo, Irene & Jones, Aled, 2020. "Emergence of New Economics Energy Transition Models: A Review," Ecological Economics, Elsevier, vol. 177(C).
    90. Dilger, Alexander, 2017. "Wirtschaftsethische Überlegungen zur Finanz- und Eurokrise," Discussion Papers of the Institute for Organisational Economics 5/2017, University of Münster, Institute for Organisational Economics.
    91. Neuberger, Doris, 2018. "Kann Karl Marx die Finanzkrise 2007/08 erklären? Eine Einordnung seiner Geld- und Kredittheorie," Thuenen-Series of Applied Economic Theory 155, University of Rostock, Institute of Economics, revised 2018.
    92. Alexandrova-Kabadjova Biliana & Castellanos Pascacio Sara Gabriela & García-Almanza Alma L., 2012. "The Adoption Process of Payment Cards -An Agent- Based Approach," Working Papers 2012-02, Banco de México.
    93. Kleinert, H. & Korbel, J., 2016. "Option pricing beyond Black–Scholes based on double-fractional diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 200-214.
    94. Hale Balseven, 2016. "The Political Economy of Financial Regulation Policies Following the Global Crisis," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 607-616.
    95. Christophe Schinckus, 2011. "What can econophysics contribute to financial economics?," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 58(2), pages 147-163, June.
    96. Torsten Trimborn & Martin Frank & Stephan Martin, 2017. "Mean Field Limit of a Behavioral Financial Market Model," Papers 1711.02573, arXiv.org.
    97. Costa Cabral, Nazare, 2010. "Breve guia temático e bibliográfico sobre o estudo da actual crise financeira e económica [Short thematic guide to the study of current financial and economic crisis]," MPRA Paper 20743, University Library of Munich, Germany.
    98. Huck, Nicolas & Mavoori, Hareesh & Mesly, Olivier, 2020. "The rationality of irrationality in times of financial crises," Economic Modelling, Elsevier, vol. 89(C), pages 337-350.
    99. Jorge Candia & Joaquín Nilo, 2015. "La enseñanza de economía en una Universidad Jesuita: Un análisis comparativo," Estudios Nueva Economía, Estudios Nueva Economía, vol. 5(2), pages 73-80.
    100. Brooks, Chris & Fenton, Evelyn & Schopohl, Lisa & Walker, James, 2019. "Why does research in finance have so little impact?," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 58(C), pages 24-52.
    101. Peter M. Spiegler & William Milberg, 2013. "Methodenstreit 2013? Historical Perspective on the Contemporary Debate Over How to Reform Economics," Forum for Social Economics, Taylor & Francis Journals, vol. 42(4), pages 311-345, November.
    102. Abdala Rioja, Yamile E, 2011. "All Things Considered: The Interaction of the Reasons for the Financial Crisis," MPRA Paper 33408, University Library of Munich, Germany.
    103. Schinckus, Christophe, 2018. "Pataphysics of finance: An essay of visual epistemology," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 52(C), pages 57-68.
    104. André Cartapanis, 2011. "La crise financière et les politiques macroprudentielles. Inflexion réglementaire ou nouveau paradigme ?," Revue économique, Presses de Sciences-Po, vol. 62(3), pages 349-382.
    105. Rod Cross, 2014. "Unemployment: natural rate epicycles or hysteresis?," Working Papers 1402, University of Strathclyde Business School, Department of Economics.
    106. Katarina Juselius, 2009. "Time to reject the privileging of economic theory over empirical evidence? A Reply to Lawson (2009)," Discussion Papers 09-16, University of Copenhagen. Department of Economics.
    107. Hatcher, Michael & Minford, Patrick, 2023. "Chameleon models in economics: A note," Cardiff Economics Working Papers E2023/10, Cardiff University, Cardiff Business School, Economics Section.
    108. Wolfram Elsner & Frederic S. Lee, 2010. "Editors' Introduction," American Journal of Economics and Sociology, Wiley Blackwell, vol. 69(5), pages 1333-1344, November.
    109. Røpke, Inge, 2016. "Complementary system perspectives in ecological macroeconomics — The example of transition investments during the crisis," Ecological Economics, Elsevier, vol. 121(C), pages 237-245.
    110. Peter J. Boettke & Alexander W. Salter & Daniel J. Smith, 2018. "Money as meta-rule: Buchanan’s constitutional economics as a foundation for monetary stability," Public Choice, Springer, vol. 176(3), pages 529-555, September.
    111. Jennifer K Gippel, 2013. "A revolution in finance?," Australian Journal of Management, Australian School of Business, vol. 38(1), pages 125-146, April.
    112. Biliana Alexandrova-Kabadjova & Alma L. Garcia-Almanza & Sara G. Castellanos Pascacio, 2012. "El proceso de adopcion de tarjetas de pago: un enfoque basado en agentes," Working Papers 1214, BBVA Bank, Economic Research Department.
    113. Bakeev, M., 2022. "A compromise between formalism and realism as a way to influence economic policy," Journal of the New Economic Association, New Economic Association, vol. 57(5), pages 113-125.
    114. Barbara Dluhosch, 2011. "European Economics at a Crossroads, by J. Barkley Rosser, Jr., Richard P. F. Holt, and David Colander," Journal of Regional Science, Wiley Blackwell, vol. 51(3), pages 629-631, August.
    115. Gregory Waymire & Sudipta Basu, 2011. "Economic crisis and accounting evolution," Accounting and Business Research, Taylor & Francis Journals, vol. 41(3), pages 207-232, August.
    116. Verónica Amarante & Ivone Perazzo, 2011. "Cantidad de niños en los hogares uruguayos: un análisis de los determinantes económicos, 1996-2006," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 26(1), pages 3-34.
    117. Roberto Cárdenas, 2015. "La no-reforma curricular en Ingeniería Comercial, Universidad de Concepción," Estudios Nueva Economía, Estudios Nueva Economía, vol. 5(2), pages 64-72.
    118. Steinbacher, Matjaz & Steinbacher, Mitja & Steinbacher, Matej, 2013. "Credit Contagion in Financial Markets: A Network-Based Approach," MPRA Paper 49616, University Library of Munich, Germany.
    119. Lisa Kustina & Junedi, 2017. "ASEAN Economic Community Impact on SMSs: A Regional Case Study," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 432-438.
    120. Cerruti, Gianluca & Lombardini, Simone, 2022. "Financial bubbles as a recursive process lead by short-term strategies," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 555-568.
    121. Ioana Negru, 2013. "How reflexive have economists been in the wake of the crisis: 'The times they are a -changin'?," Working Papers PKWP1306, Post Keynesian Economics Society (PKES).
    122. Spahn, Peter, 2013. "Subprime and euro crisis: Should we blame the economists?," FZID Discussion Papers 83-2013, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    123. Taisiia Bondaruk & Nataliia Melnychuk & Igor Bondaruk, 2018. "Instability And Its Government Regulation," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 4(2).
    124. Rafael Galvão de Almeida, 2019. "How economics became an interventionist science (and how it ceased to be)," Textos para Discussão Cedeplar-UFMG 612, Cedeplar, Universidade Federal de Minas Gerais.
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    160. Situngkir, Hokky, 2012. "Indonesian Stock Market Crisis Observation with Spectral and Composite Index," MPRA Paper 35961, University Library of Munich, Germany.
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    164. Krug, Sebastian & Lengnick, Matthias & Wohltmann, Hans-Werner, 2014. "The impact of Basel III on financial (in)stability: An agent-based credit network approach," Economics Working Papers 2014-13, Christian-Albrechts-University of Kiel, Department of Economics.
    165. Rod O’Donnell, 2009. "The permanent need for political economy," Agenda - A Journal of Policy Analysis and Reform, Australian National University, College of Business and Economics, School of Economics, vol. 16(4), pages 89-100.
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    171. Ekaterina Svetlova & Matthias Fiedler, 2011. "Understanding Crisis: On the Meaning of Uncertainty and Probability," Chapters, in: Óscar Dejuán & Eladio Febrero & Maria Cristina Marcuzzo (ed.), The First Great Recession of the 21st Century, chapter 3, Edward Elgar Publishing.
    172. Ghassan, Hassan Belkacem & Alhajhoj, Hassan R. & Kbiri, Mohammed A., 2013. "The impacts of the International Financial Crisis on Saudi Arabia's Economy: Evidence from Asymmetric SVAR modelling," MPRA Paper 122987, University Library of Munich, Germany, revised 26 Sep 2013.
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    175. Dilip M. Nachane, 2016. "Dynamic stochastic general equilibrium (dsge) modelling: Theory and practice," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2016-004, Indira Gandhi Institute of Development Research, Mumbai, India.
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  4. Hommes, C.H. & Lux, T., 2009. "Individual Expectations and Aggregate Behavior in Learning to Forcast Experiments," CeNDEF Working Papers 09-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.

    Cited by:

    1. Anufriev, Mikhail & Arifovic, Jasmina & Donmez, Anil & Ledyard, John & Panchenko, Valentyn, 2025. "IEL-CDA model: A more accurate theory of behavior in continuous double auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 172(C).
    2. Anufriev, M. & Hommes, C.H. & Philipse, R., 2010. "Evolutionary Selection of Expectations in Positive and Negative Feedback Markets," CeNDEF Working Papers 10-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. Colasante, Annarita & Alfarano, Simone & Camacho-Cuena, Eva, 2019. "Heuristic Switching Model and Exploration-Explotation Algorithm to describe long-run expectations in LtFEs: a comparison," MPRA Paper 92391, University Library of Munich, Germany.
    4. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    5. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    6. Selten, Reinhard & Neugebauer, Tibor, 2019. "Experimental stock market dynamics: Excess bids, directional learning, and adaptive style-investing in a call-auction with multiple multi-period lived assets," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 209-224.
    7. Cars Hommes & Tomasz Makarewicz & Domenico Massaro & Tom Smits, 2017. "Genetic algorithm learning in a New Keynesian macroeconomic setup," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1133-1155, November.
    8. Mikhail Anufriev & Cars Hommes & Tomasz Makarewicz, 2015. "Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments," Working Paper Series 29, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    9. Guerci, E. & Kirman, A. & Moulet, S., 2014. "Learning to bid in sequential Dutch auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 374-393.
    10. Hommes, Cars, 2018. "Carl’s nonlinear cobweb," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 7-20.
    11. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2020. "Long-run expectations in a learning-to-forecast experiment: a simulation approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 75-116, January.
    12. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy.
    13. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    14. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 12, pages 1-21.
    15. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    16. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    17. Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    18. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City St George's, University of London.
    19. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Shocks in financial markets, price expectation, and damped harmonic oscillators," Papers 1103.1992, arXiv.org, revised Sep 2011.
    20. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    21. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    22. Tai, Chung-Ching & Chen, Shu-Heng & Yang, Lee-Xieng, 2018. "Cognitive ability and earnings performance: Evidence from double auction market experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 409-440.
    23. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    24. Mario Gutiérrez-Roig & Carlota Segura & Jordi Duch & Josep Perelló, 2016. "Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.

  5. Lux, Thomas, 2008. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Economics Working Papers 2008-07, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Lux, Thomas, 2016. "Network effects and systemic risk in the banking sector," FinMaP-Working Papers 62, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    2. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters," Discussion Papers on Economics 1/2012, University of Southern Denmark, Department of Economics.
    3. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of animal spirits in a bounded rationality model: An application to the euro area," Economics Working Papers 2012-12, Christian-Albrechts-University of Kiel, Department of Economics.
    4. Finger, Karl & Lux, Thomas, 2014. "Friendship between banks: An application of an actor-oriented model of network formation on interbank credit relations," Kiel Working Papers 1916, Kiel Institute for the World Economy.
    5. Pierdzioch, Christian & Reitz, Stefan & Ruelke, Jan-Christoph, 2015. "Heteroeneous forecasters and nonlinear expectation formation in US stock market," FinMaP-Working Papers 29, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    6. Michele Catalano & Corrado Di Guilmi, 2016. "Uncertainty, Rationality and Complexity in a Multi Sectoral Dynamic Model: The Dynamic Stochastic Generalized Aggregation Approach," CAMA Working Papers 2016-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Jørgen Vitting Andersen & Ioannis Vrontos & Petros Dellaportas & Serge Galam, 2014. "Communication impacting financial markets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01215750, HAL.
    8. Lines Marji & Westerhoff Frank, 2012. "Effects of Inflation Expectations on Macroeconomic Dynamics: Extrapolative Versus Regressive Expectations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-30, October.
    9. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    10. Marco D'Errico & Gulnur Muradoglu & Silvana Stefani & Giovanni Zambruno, 2014. "Opinion Dynamics and Price Formation: a Nonlinear Network Model," Papers 1408.0308, arXiv.org.
    11. Petar Sorić & Ivana Lolić & Marina Matošec, 2023. "The persistence of economic sentiment: a trip down memory lane," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 371-395, April.
    12. Rülke Jan-Christoph, 2012. "Do Private Sector Forecasters Desire to Deviate From the German Council of Economic Experts?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 414-428, August.
    13. Mokinski, Frieder, 2016. "Using time-stamped survey responses to measure expectations at a daily frequency," International Journal of Forecasting, Elsevier, vol. 32(2), pages 271-282.
    14. Steinbacher, Mitja & Raddant, Matthias & Karimi, Fariba & Camacho-Cuena, Eva & Alfarano, Simone & Iori, Giulia & Lux, Thomas, 2021. "Advances in the Agent-Based Modeling of Economic and Social Behavior," MPRA Paper 107317, University Library of Munich, Germany.
    15. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2019. "Exploiting ergodicity in forecasts of corporate profitability," BERG Working Paper Series 147, Bamberg University, Bamberg Economic Research Group.
    16. Zhenxi Chen, 2020. "Regional financial market bloc and spillover of the financial crisis: A heterogeneous agents approach," Manchester School, University of Manchester, vol. 88(2), pages 262-281, March.
    17. Shi, Yong & Tang, Ye-ran & Long, Wen, 2019. "Sentiment contagion analysis of interacting investors: Evidence from China’s stock forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 246-259.
    18. Matthieu Charpe & Carl Chiarella & Peter Flaschel & Christian R. Proaño, 2014. "Business Confidence and Macroeconomic Dynamics in a Nonlinear Two-Country Framework with Aggregate Opinion Dynamics," Working Papers 1401, New School for Social Research, Department of Economics.
    19. Jean-Philippe Bouchaud, 2012. "Crises and collective socio-economic phenomena: simple models and challenges," Papers 1209.0453, arXiv.org, revised Dec 2012.
    20. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    21. Stolzenburg, Ulrich & Lux, Thomas, 2010. "Identification of a core-periphery structure among participants of a business climate survey," Kiel Working Papers 1659, Kiel Institute for the World Economy.
    22. Xi Chen & Xiao Zhang & Yong Xie & Wei Li, 2017. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    23. Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," MPRA Paper 84886, University Library of Munich, Germany.
    24. Darren Grant, 2009. "What Makes a Good Economy? An Analysis of Survey Data," Working Papers 0909, Sam Houston State University, Department of Economics and International Business.
    25. Jan-Christoph Rülke, 2011. "Do private sector forecasters desire to deviate from the German council of economic experts?," WHU Working Paper Series - Economics Group 11-04, WHU - Otto Beisheim School of Management.
    26. Di Francesco, Tommaso & Hommes, Cars, 2025. "Sentiment-driven speculation in financial markets with heterogeneous beliefs: A machine learning approach," Journal of Economic Dynamics and Control, Elsevier, vol. 175(C).
    27. Hawkins, Raymond J., 2011. "Lending sociodynamics and economic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4355-4369.
    28. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    29. Lux, Thomas, 2012. "Estimation of an agent-based model of investor sentiment formation in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1284-1302.
    30. Reitz Stefan & Rülke Jan-Christoph & Stadtmann Georg, 2010. "Regressive Oil Price Expectations Toward More Fundamental Values of the Oil Price," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(4), pages 454-466, August.
    31. Markus Demary, 2017. "Yield curve responses to market sentiments and monetary policy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 309-344, July.
    32. Roberta Terranova, 2025. "Endogenous beliefs and social influence in a simple macroeconomic framework," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 20(3), pages 583-641, July.
    33. Wilko Bolt & Maria Demertzis & Cees Diks & Cars Hommes & Marco van der Leij, 2014. "Identifying Booms and Busts in House Prices under Heterogeneous Expectations," Tinbergen Institute Discussion Papers 14-157/II, Tinbergen Institute.
    34. Franke Reiner, 2012. "Microfounded Animal Spirits in the New Macroeconomic Consensus," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-41, October.
    35. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.
    36. Gardini, Laura & Radi, Davide & Schmitt, Noemi & Sushko, Iryna & Westerhoff, Frank, 2023. "Sentiment-driven business cycle dynamics: An elementary macroeconomic model with animal spirits," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 342-359.
    37. Darren Grant, 2014. "What Makes A Good Economy? Evidence From Public Opinion Surveys," Economic Inquiry, Western Economic Association International, vol. 52(3), pages 1120-1136, July.
    38. Nicolas, Maxime L.D., 2022. "Estimating a model of herding behavior on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    39. Finger, Karl & Lux, Thomas, 2014. "Friendship Between Banks: An Application of an Actor-Oriented Model of Network Formation on Interbank Credit Relations," FinMaP-Working Papers 1, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    40. Lux, Thomas, 2012. "Inference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approach," Kiel Working Papers 1781, Kiel Institute for the World Economy.
    41. Xu, Shaojun, 2023. "Behavioral asset pricing under expected feedback mode," International Review of Financial Analysis, Elsevier, vol. 86(C).
    42. Jongen, Ron & Verschoor, Willem F.C. & Wolff, Christian C.P. & Zwinkels, Remco C.J., 2012. "Explaining dispersion in foreign exchange expectations: A heterogeneous agent approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(5), pages 719-735.
    43. Bolt, W. & Demertzis, D. & Diks, C.G.H. & Van der Leij, M.J., 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," CeNDEF Working Papers 11-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    44. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 4, pages 1-44.
    45. Lux, Thomas, 2020. "Can heterogeneous agent models explain the alleged mispricing of the S&P 500?," Economics Working Papers 2020-03, Christian-Albrechts-University of Kiel, Department of Economics.
    46. Lux, Thomas, 2009. "Mass psychology in action: identification of social interaction effects in the German stock market," Kiel Working Papers 1514, Kiel Institute for the World Economy.
    47. Morales-Arias, Leonardo & Dross, Alexander, 2010. "Adaptive forecasting of exchange rates with panel data," Kiel Working Papers 1656, Kiel Institute for the World Economy.
    48. Thomas Lux & Jaba Ghonghadze, 2011. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Post-Print hal-00711445, HAL.
    49. Thomas Lux, 2013. "Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach," Annals of Finance, Springer, vol. 9(2), pages 217-248, May.
    50. Roberto Veneziani & Luca Zamparelli & Reiner Franke & Frank Westerhoff, 2017. "Taking Stock: A Rigorous Modelling Of Animal Spirits In Macroeconomics," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1152-1182, December.
    51. Xue-Zhong He & Youwei Li, 2015. "The Adaptiveness in Stock Markets: Testing the Stylized Facts in the Dax 30," Research Paper Series 364, Quantitative Finance Research Centre, University of Technology, Sydney.
    52. Brückbauer, Frank & Schröder, Michael, 2021. "Data resource profile: The ZEW FMS dataset," ZEW Discussion Papers 21-100, ZEW - Leibniz Centre for European Economic Research.
    53. Franke, Reiner, 2014. "Aggregate sentiment dynamics: A canonical modelling approach and its pleasant nonlinearities," Structural Change and Economic Dynamics, Elsevier, vol. 31(C), pages 64-72.
    54. Yamamoto, Ryuichi & Hirata, Hideaki, 2013. "Strategy switching in the Japanese stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 2010-2022.
    55. Brückbauer Frank & Schröder Michael, 2023. "The ZEW Financial Market Survey Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 451-469, June.
    56. Gerunov, Anton, 2013. "Връзка Между Икономическите Очаквания И Стопанската Динамика В Ес-27 [Linkages Between Expectations and Economic Dynamics in EU-27]," MPRA Paper 68795, University Library of Munich, Germany.
    57. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.

  6. Liu, Ruipeng & Di Matteo, Tiziana & Lux, Thomas, 2008. "Multifractality and long-range dependence of asset returns: The scaling behaviour of the Markov-switching multifractal model with lognormal volatility components," Economics Working Papers 2008-09, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
    2. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    4. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
    5. Kukacka, Jiri & Kristoufek, Ladislav, 2021. "Does parameterization affect the complexity of agent-based models?," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 324-356.
    6. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
    7. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    8. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    9. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    10. Raffaello Morales & T. Di Matteo & Ruggero Gramatica & Tomaso Aste, 2011. "Dynamical Hurst exponent as a tool to monitor unstable periods in financial time series," Papers 1109.0465, arXiv.org.
    11. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
    12. Saâdaoui, Foued, 2018. "Testing for multifractality of Islamic stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 263-273.
    13. Riccardo Junior Buonocore & Tomaso Aste & Tiziana Di Matteo, 2015. "Measuring multiscaling in financial time-series," Papers 1509.05471, arXiv.org, revised Sep 2015.

  7. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Torsten Trimborn, 2018. "A Macroscopic Portfolio Model: From Rational Agents to Bounded Rationality," Papers 1805.11036, arXiv.org, revised Oct 2018.
    2. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    3. Leoni, Patrick L., 2011. "Psychological determinants of occurrence and magnitude of market crashes," Economic Modelling, Elsevier, vol. 28(5), pages 2190-2196, September.
    4. Daniel Fricke & Thomas Lux, 2015. "The effects of a financial transaction tax in an artificial financial market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 119-150, April.
    5. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    6. Maximilian Beikirch & Torsten Trimborn, 2020. "Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model," Papers 2002.10222, arXiv.org.
    7. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    8. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2018. "Simulation of Stylized Facts in Agent-Based Computational Economic Market Models," Papers 1812.02726, arXiv.org, revised Nov 2019.
    9. Torsten Trimborn & Martin Frank & Stephan Martin, 2017. "Mean Field Limit of a Behavioral Financial Market Model," Papers 1711.02573, arXiv.org.
    10. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 4, pages 1-44.
    11. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2016. "A new structural stochastic volatility model of asset pricing and its stylized facts," Papers 1604.08824, arXiv.org.
    12. Trimborn, Torsten & Frank, Martin & Martin, Stephan, 2018. "Mean field limit of a behavioral financial market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 613-631.
    13. Carl Chiarella & Xue-Zhong He & Paolo Pellizzari, 2009. "A Dynamic Analysis of the Microstructure of Moving Average Rules in a Double Auction Market," Research Paper Series 251, Quantitative Finance Research Centre, University of Technology, Sydney.
    14. Torsten Trimborn & Lorenzo Pareschi & Martin Frank, 2017. "Portfolio Optimization and Model Predictive Control: A Kinetic Approach," Papers 1711.03291, arXiv.org, revised Feb 2019.
    15. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.

  8. Liu, Ruipeng & Di Matteo, Tiziana & Lux, Thomas, 2007. "True and Apparent Scaling: The Proximity of the Markov- Switching Multifractal Model to Long-Range Dependence," Economics Working Papers 2007-06, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    2. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy.
    3. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
    4. Topa Răzvan, 2025. "Business Models and Their Impact on Climate Change: A Comprehensive Review of Recent Literature," Proceedings of the International Conference on Business Excellence, Paradigm, vol. 19(1), pages 2765-2776.
    5. Lahmiri, Salim & Bekiros, Stelios, 2019. "Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 334-341.
    6. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    8. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
    9. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    10. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," CQE Working Papers 7118, Center for Quantitative Economics (CQE), University of Muenster.
    11. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    12. Pagnottoni, Paolo & Spelta, Alessandro & Pecora, Nicolò & Flori, Andrea & Pammolli, Fabio, 2021. "Financial earthquakes: SARS-CoV-2 news shock propagation in stock and sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    13. Erdős, Péter & Li, Youwei & Liu, Ruipeng & Mende, Alexander, 2021. "Same same but different – Stylized facts of CTA sub strategies," International Review of Financial Analysis, Elsevier, vol. 74(C).
    14. Pagnottoni, Paolo & Spelta, Alessandro & Flori, Andrea & Pammolli, Fabio, 2022. "Climate change and financial stability: Natural disaster impacts on global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    15. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    16. Yufang Liu & Weiguo Zhang & Junhui Fu & Xiang Wu, 2020. "Multifractal Analysis of Realized Volatilities in Chinese Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 319-336, August.
    17. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models," FinMaP-Working Papers 46, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    18. Mulligan, Robert F., 2014. "Multifractality of sectoral price indices: Hurst signature analysis of Cantillon effects in disequilibrium factor markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 252-264.
    19. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    20. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.
    21. Raffaello Morales & T. Di Matteo & Ruggero Gramatica & Tomaso Aste, 2011. "Dynamical Hurst exponent as a tool to monitor unstable periods in financial time series," Papers 1109.0465, arXiv.org.
    22. Mulligan, Robert F., 2017. "The multifractal character of capacity utilization over the business cycle: An application of Hurst signature analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 147-152.
    23. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
    24. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    25. Pakrashi, Vikram & Kelly, Joe & Harkin, Julie & Farrell, Aidan, 2013. "Hurst exponent footprints from activities on a large structural system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1803-1817.
    26. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    27. Wang, Yi & Sun, Qi & Zhang, Zilu & Chen, Liqing, 2022. "A risk measure of the stock market that is based on multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    28. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2011. "A copula–multifractal volatility hedging model for CSI 300 index futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4260-4272.
    29. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2015. "Modeling and forecasting crude oil price volatility: Evidence from historical and recent data," FinMaP-Working Papers 31, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    30. Jozef Barunik & Tomaso Aste & Tiziana Di Matteo & Ruipeng Liu, 2012. "Understanding the source of multifractality in financial markets," Papers 1201.1535, arXiv.org, revised Jan 2012.
    31. Liu, Yufang & Zhang, Weiguo & Fu, Junhui, 2016. "Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 56-66.
    32. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy.
    33. Riccardo Junior Buonocore & Tomaso Aste & Tiziana Di Matteo, 2015. "Measuring multiscaling in financial time-series," Papers 1509.05471, arXiv.org, revised Sep 2015.
    34. Malo, Pekka, 2009. "Modeling electricity spot and futures price dependence: A multifrequency approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4763-4779.
    35. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
    36. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    37. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    38. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.

  9. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.

    Cited by:

    1. Rebecca Westphal & Didier Sornette, 2019. "Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model," Swiss Finance Institute Research Paper Series 19-29, Swiss Finance Institute.
    2. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    3. Zoltan Kuscsik & Denis Horvath, 2007. "Statistical properties of agent-based market area model," Papers 0710.0459, arXiv.org.
    4. Sabiou Inoua, 2016. "The Random Walk behind Volatility Clustering," Papers 1612.09344, arXiv.org.
    5. Han Wang & Damien Fay & Kenneth N. Brown & Liam Kilmartin, 2016. "Modelling revenue generation in a dynamically priced mobile telephony service," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(4), pages 711-734, August.
    6. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    7. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    8. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.
    9. Goykhman, Mikhail, 2017. "Wealth dynamics in a sentiment-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 132-148.
    10. Nils Bertschinger & Iurii Mozzhorin, 2021. "Bayesian estimation and likelihood-based comparison of agent-based volatility models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 173-210, January.
    11. Elena Green & Daniel M. Heffernan, 2019. "An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns," Papers 1901.05053, arXiv.org.
    12. Di Xiao & Jun Wang & Hongli Niu, 2016. "Volatility Analysis of Financial Agent-Based Market Dynamics from Stochastic Contact System," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 607-625, December.
    13. Janusz Miśkiewicz & Paweł Witkowicz, 2025. "Time Series Determinism Recognition by LSTM Model," Mathematics, MDPI, vol. 13(12), pages 1-13, June.
    14. Nguyen Tien Zung, 2017. "Second order stochastic differential models for financial markets," Papers 1707.05419, arXiv.org.
    15. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    16. Kristian Lindgren & Vilhelm Verendel, 2013. "Evolutionary Exploration of the Finitely Repeated Prisoners’ Dilemma—The Effect of Out-of-Equilibrium Play," Games, MDPI, vol. 4(1), pages 1-20, January.
    17. Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
    18. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    19. Pietro DeLellis & Anna DiMeglio & Franco Garofalo & Francesco Lo Iudice, 2017. "The evolving cobweb of relations among partially rational investors," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    20. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    21. Vishwesha Guttal & Srinivas Raghavendra & Nikunj Goel & Quentin Hoarau, 2016. "Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    22. Matthias Lengnick & Hans-Werner Wohltmann, 2013. "Agent-based financial markets and New Keynesian macroeconomics: a synthesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 1-32, April.
    23. Mundt, Philipp & Förster, Niels & Alfarano, Simone & Milaković, Mishael, 2014. "The real versus the financial economy: A global tale of stability versus volatility," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 8, pages 1-26.
    24. Baxter L. M. Williams & R. J. Hooper & Daniel Gnoth & J. G. Chase, 2025. "Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach," Energies, MDPI, vol. 18(6), pages 1-22, March.
    25. Miquel Montero, 2021. "Predator–prey model for stock market fluctuations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 29-57, January.
    26. Lindgren, Kristian & Verendel, Vilhelm, 2013. "Evolutionary Exploration of the Finitely Repeated Prisoners' Dilemma--The Effect of Out-of-Equilibrium Play," MPRA Paper 43662, University Library of Munich, Germany.
    27. Aleksandras Vytautas Rutkauskas & Tomas Ramanauskas, 2009. "Building an artificial stock market populated by reinforcement‐learning agents," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(4), pages 329-341, September.
    28. Tomonori Takahashi & Takayuki Mizuno, 2024. "Generation of synthetic financial time series by diffusion models," Papers 2410.18897, arXiv.org.
    29. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    30. Birte Ewers & Jonathan F. Donges & Jobst Heitzig & Sonja Peterson, 2019. "Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy," Papers 1902.07481, arXiv.org.
    31. Broda, Simon A. & Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Stable mixture GARCH models," Journal of Econometrics, Elsevier, vol. 172(2), pages 292-306.
    32. Luca Fraccascia & Ilaria Giannoccaro & Vito Albino, 2017. "Efficacy of Landfill Tax and Subsidy Policies for the Emergence of Industrial Symbiosis Networks: An Agent-Based Simulation Study," Sustainability, MDPI, vol. 9(4), pages 1-18, March.
    33. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024, arXiv.org, revised May 2015.
    34. Constantin Zopounidis & Michalis Doumpos & Dimitrios Niklis, 2018. "Financial decision support: an overview of developments and recent trends," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 63-76, June.
    35. Mimkes, Jürgen, 2010. "Stokes integral of economic growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1665-1676.
    36. Yazan, Devrim Murat & Fraccascia, Luca & Mes, Martijn & Zijm, Henk, 2018. "Cooperation in manure-based biogas production networks: An agent-based modeling approach," Applied Energy, Elsevier, vol. 212(C), pages 820-833.
    37. Yang, ChunXia & Hu, Sen & Xia, BingYing, 2012. "The endogenous dynamics of financial markets: Interaction and information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3513-3525.
    38. Angle, John, 2006. "Not a Hollowing Out, a Stretching: Trends in U.S. Nonmetro Wage Income Distribution, 1961-2003," MPRA Paper 10111, University Library of Munich, Germany, revised 18 Aug 2008.
    39. Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
    40. Krause, Sebastian M. & Bornholdt, Stefan, 2013. "Spin models as microfoundation of macroscopic market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4048-4054.
    41. David G. Green, 2023. "Emergence in complex networks of simple agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 419-462, July.
    42. Anantya Bhatnagar & Dimitri D. Vvedensky, 2022. "Quantum effects in an expanded Black–Scholes model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-12, August.
    43. Maximilian Beikirch & Torsten Trimborn, 2020. "Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model," Papers 2002.10222, arXiv.org.
    44. Annalisa Fabretti & Tommy Gärling & Stefano Herzel & Martin Holmen, 2017. "Convex incentives in financial markets: an agent-based analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 375-395, November.
    45. Silvano Cincotti & Ilaria Giannoccaro & Cristina Ponsiglione & Linda Ponta & Andreas Pyka, 2024. "Editorial to the special issue on applications of complexity for resilient organizations, management and innovation systems," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(2), pages 193-200, April.
    46. Antoine Kopp & Rebecca Westphal & Didier Sornette, 2022. "Agent-based model generating stylized facts of fixed income markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 947-992, October.
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    69. Alexander Lykov & Stepan Muzychka & Kirill Vaninsky, 2016. "Investor'S Sentiment In Multi-Agent Model Of The Continuous Double Auction," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(06), pages 1-29, September.
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    75. Sabiou M. Inoua, 2020. "News-Driven Expectations and Volatility Clustering," JRFM, MDPI, vol. 13(1), pages 1-14, January.
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    77. Yuan, Qianshun & Semba, Sherehe & Zhang, Jing & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2021. "Multi-scale transition matrix approach to time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    78. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
    79. Musciotto, Federico & Marotta, Luca & Miccichè, Salvatore & Piilo, Jyrki & Mantegna, Rosario N., 2016. "Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 267-278.
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    81. Aleksejus Kononovicius & Valentas Daniunas, 2013. "Agent-based and macroscopic modeling of the complex socio-economic systems," Papers 1303.3693, arXiv.org, revised Apr 2013.
    82. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    83. Lei Tan & Bo Zheng & Jun-Jie Chen & Xiong-Fei Jiang, 2015. "How Volatilities Nonlocal in Time Affect the Price Dynamics in Complex Financial Systems," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-16, February.
    84. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2019. "Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models," Papers 1904.04951, arXiv.org, revised Mar 2021.
    85. Robin Nicole & Peter Sollich, 2017. "Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria," Papers 1706.09763, arXiv.org.
    86. Haochong Xia & Yao Long Teng & Regan Tan & Molei Qin & Xinrun Wang & Bo An, 2026. "History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis," Papers 2601.10143, arXiv.org.
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    89. Kamana-Williams, Baxter & Cerdeira, Alice E. & Hooper, R.J. & Peer, Rebecca & Chase, J. Geoffrey, 2026. "Rethinking residential energy policy: A review of priorities and directions for effective and equitable demand-side management," Applied Energy, Elsevier, vol. 406(C).
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  10. Lux, Thomas, 2007. "Applications of statistical physics in finance and economics," Economics Working Papers 2007-05, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Shu-Peng Chen & Ling-Yun He, 2013. "Bubble Formation and Heterogeneity of Traders: A Multi-Agent Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 42(3), pages 267-289, October.
    2. Kristoufek, Ladislav, 2009. "Distinguishing between short and long range dependence: Finite sample properties of rescaled range and modified rescaled range," MPRA Paper 16424, University Library of Munich, Germany.
    3. Heping Pan, 2011. "A Basic Theory Of Intelligent Finance," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 197-227.
    4. Paulo L. dos Santos, 2017. "The Principle of Social Scaling," Complexity, Hindawi, vol. 2017, pages 1-9, December.
    5. Ladislav Krištoufek, 2010. "Dlouhá paměť a její vývoj ve výnosech burzovního indexu PX v letech 1997-2009 [Long-Term Memory and Its Evolution in Returns of Stock Index PX Between 1997 and 2009]," Politická ekonomie, Prague University of Economics and Business, vol. 2010(4), pages 471-487.
    6. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 4, pages 1-44.
    7. Kristoufek, Ladislav, 2009. "Procesy s dlouhou pamětí a jejich vývoj ve výnosech indexu PX v letech 1999 – 2009 [Long-term memory and its evolution in returns of PX between 1999 and 2009]," MPRA Paper 16435, University Library of Munich, Germany.

  11. Samanidou, Egle & Zschischang, Elmar & Stauffer, Dietrich & Lux, Thomas, 2006. "Microscopic models of financial markets," Economics Working Papers 2006-15, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Lengnick, Matthias & Wohltmann, Hans-Werner, 2011. "Agent-based financial markets and New Keynesian macroeconomics: A synthesis," Economics Working Papers 2011-09, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Barroso, Ricardo Vieira & Lima, Joaquim Ignacio Alves Vasconcellos & Lucchetti, Alexandre Henrique & Cajueiro, Daniel Oliveira, 2016. "Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning," MPRA Paper 73308, University Library of Munich, Germany.

  12. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Christophe Schinckus & Çınla Akdere, 2015. "Towards a New Way of Teaching Statistics in Economics: The Case for Econophysics," Ekonomi-tek - International Economics Journal, Turkish Economic Association, vol. 4(3), pages 89-108, September.
    2. Paolo Pellizzari & Frank Westerhoff, 2009. "Some effects of transaction taxes under different microstructures," Post-Print hal-00727590, HAL.
    3. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters," Discussion Papers on Economics 1/2012, University of Southern Denmark, Department of Economics.
    4. Heinrich, Torsten, 2016. "The Narrow and the Broad Approach to Evolutionary Modeling in Economics," MPRA Paper 75797, University Library of Munich, Germany.
    5. Sabiou Inoua, 2015. "The Intrinsic Instability of Financial Markets," Papers 1508.02203, arXiv.org.
    6. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    7. Fabio Tramontana & Laura Gardini & Frank Westerhoff, 2011. "Heterogeneous Speculators and Asset Price Dynamics: Further Results from a One-Dimensional Discontinuous Piecewise-Linear Map," Computational Economics, Springer;Society for Computational Economics, vol. 38(3), pages 329-347, October.
    8. Chiarella Carl & Di Guilmi Corrado, 2015. "The limit distribution of evolving strategies in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 137-159, April.
    9. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    10. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    11. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    12. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    13. Alfarano, Simone & Milakovic, Mishael, 2009. "Network structure and N-dependence in agent-based herding models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 78-92, January.
    14. Alexander Smirnov, 2016. "A Simple Model of Credit Expansion," Papers 1609.05055, arXiv.org.
    15. Sandrine Jacob Leal & Mauro Napoletano, 2016. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent Based Model with Low- and High-Frequency Trading," LEM Papers Series 2016/15, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Jovanovic, Franck & Schinckus, Christophe, 2016. "Breaking down the barriers between econophysics and financial economics," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 256-266.
    17. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    18. Ausloos, Marcel & Jovanovic, Franck & Schinckus, Christophe, 2016. "On the “usual” misunderstandings between econophysics and finance: Some clarifications on modelling approaches and efficient market hypothesis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 7-14.
    19. Nicolas Brisset, 2017. "Models as Speech Acts: The Telling Case of Financial Models," GREDEG Working Papers 2017-25, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    20. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

  13. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2006. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2006-16, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Simone Landini & Mauro Gallegati, 2014. "Heterogeneity, interaction and emergence: effects of composition," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(3/4), pages 339-361.
    3. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Ali Naqvi & Miriam Rehm, 2014. "A multi-agent model of a low income economy: simulating the distributional effects of natural disasters," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 275-309, October.
    5. Kyrtsou, Catherine, 2008. "Re-examining the sources of heteroskedasticity: The paradigm of noisy chaotic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6785-6789.
    6. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    7. Alfarano, Simone & Milakovic, Mishael & Raddant, Matthias, 2011. "A Note on institutional hierarchy and volatility in financial markets," MPRA Paper 30902, University Library of Munich, Germany.
    8. Xue-Zhong He & Huanhuan Zheng, 2016. "Trading Heterogeneity Under Information Uncertainty," Research Paper Series 373, Quantitative Finance Research Centre, University of Technology, Sydney.
    9. Donovan Platt, 2022. "Bayesian Estimation of Economic Simulation Models Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 599-650, February.
    10. Di Guilmi, Corrado & He, Xue-Zhong & Li, Kai, 2014. "Herding, trend chasing and market volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 349-373.
    11. Kirill S. Glavatskiy & Mikhail Prokopenko & Adrian Carro & Paul Ormerod & Michael Harré, 2021. "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model," SN Business & Economics, Springer, vol. 1(6), pages 1-21, June.
    12. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    13. Domenico Delli Gatti & Tommaso Ferraresi & Filippo Gusella & Lilit Popoyan & Giorgio Ricchiuti & Andrea Roventini, 2024. "The complex interplay between exchange rate and real markets: an agent-based model exploration," LEM Papers Series 2024/24, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    15. Lux, Thomas, 2017. "Estimation of agent-based models using sequential Monte Carlo methods," Economics Working Papers 2017-07, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Michele Catalano & Corrado Di Guilmi, 2016. "Uncertainty, Rationality and Complexity in a Multi Sectoral Dynamic Model: The Dynamic Stochastic Generalized Aggregation Approach," CAMA Working Papers 2016-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Kononovicius, Aleksejus & Ruseckas, Julius, 2019. "Order book model with herd behavior exhibiting long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 171-191.
    18. Kononovicius, Aleksejus, 2021. "Supportive interactions in the noisy voter model," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    19. Rytis Kazakevicius & Aleksejus Kononovicius & Bronislovas Kaulakys & Vygintas Gontis, 2021. "Understanding the nature of the long-range memory phenomenon in socioeconomic systems," Papers 2108.02506, arXiv.org, revised Aug 2021.
    20. Weihong Huang & Huanhuan Zheng & Wai-Mun Chia, 2013. "Asymmetric returns, gradual bubbles and sudden crashes," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 420-437, May.
    21. Ghonghadze, Jaba & Lux, Thomas, 2015. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," FinMaP-Working Papers 38, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    22. Nils Bertschinger & Iurii Mozzhorin, 2021. "Bayesian estimation and likelihood-based comparison of agent-based volatility models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 173-210, January.
    23. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    24. Makoto Nirei & John Stachurski & Koichiro Takaoka & Tsutomu Watanabe, 2018. "Herding and Power Laws in Financial Markets," CARF F-Series CARF-F-434, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    25. Giovanni Campisi & Silvia Muzzioli, 2020. "Investor sentiment and trading behavior," Department of Economics 0163, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    26. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2010. "Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation," MPRA Paper 24719, University Library of Munich, Germany.
    27. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," Papers 1605.01354, arXiv.org.
    28. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    29. Светлов К.В., 2019. "Стадное Поведение На Фондовом Рынке: Анализ И Прогнозирование," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 81-97, апрель.
    30. Kukacka, Jiri & Kristoufek, Ladislav, 2021. "Does parameterization affect the complexity of agent-based models?," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 324-356.
    31. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda, . "A multilayer model of order book dynamics," Journal of Network Theory in Finance, Journal of Network Theory in Finance.
    32. Roy Cerqueti & Giulia Rotundo, 2015. "A review of aggregation techniques for agent-based models: understanding the presence of long-term memory," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1693-1717, July.
    33. Nirei, Makoto & Stachurski, John & Watanabe, Tsutomu, 2020. "Trade clustering and power laws in financial markets," Theoretical Economics, Econometric Society, vol. 15(4), November.
    34. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    35. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    36. Kazakevičius, Rytis & Kononovicius, Aleksejus, 2026. "Mean first passage time of the symmetric noisy voter model with arbitrary initial and boundary conditions," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).
    37. Menkhoff, Lukas & Rebitzky, Rafael R. & Schröder, Michael, 2009. "Heterogeneity in exchange rate expectations: Evidence on the chartist-fundamentalist approach," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 241-252, May.
    38. Carl Chiarella & Corrado Di Guilmi, 2011. "Limit Distribution of Evolving Strategies in Financial Markets," Research Paper Series 294, Quantitative Finance Research Centre, University of Technology, Sydney.
    39. Aleksejus Kononovicius, 2017. "Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections," Complexity, Hindawi, vol. 2017, pages 1-15, November.
    40. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    41. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda, 2016. "Order Book, Financial Markets and Self-Organized Criticality," Papers 1602.08270, arXiv.org.
    42. Bleher, Johannes & Dimpfl, Thomas, 2019. "Today I got a million, tomorrow, I don't know: On the predictability of cryptocurrencies by means of Google search volume," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 147-159.
    43. Domenico Delli Gatti & Tommaso Ferraresi & Filippo Gusella & Lilit Popoyan & Giorgio Ricchiuti & Andrea Roventini, 2024. "The interplay between real and exchange rate market: an agent-based model approach," Working Papers - Economics wp2024_10.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    44. Daniele Giachini, 2018. "Rationality and Asset Prices under Belief Heterogeneity," LEM Papers Series 2018/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    45. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    46. Thomas Lux, 2022. "Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 451-477, August.
    47. Lux, Thomas, 2018. "Estimation of agent-based models using sequential Monte Carlo methods," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 391-408.
    48. Friedrich Wagner, 2011. "Market clearing by maximum entropy in agent models of stock markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(2), pages 121-138, November.
    49. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024, arXiv.org, revised May 2015.
    50. Makoto Nirei & John Stachurski & Tsutomu Watanabe, 2018. "Trade Clustering and Power Laws in Financial Markets (Published in Theoretical Economics, 15:1365?1398, 2020)," CARF F-Series CARF-F-450, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    51. Chen, Zhenxi, 2016. "Regimes dependent speculative trading: Evidence from the United States housing market," FinMaP-Working Papers 66, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    52. Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
    53. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    54. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    55. Alfarano, Simone & Milaković, Mishael & Raddant, Matthias, 2009. "Network hierarchy in Kirman's ant model: fund investment can create systemic risk," Economics Working Papers 2009-09, Christian-Albrechts-University of Kiel, Department of Economics.
    56. Mikhail Anufriev & Te Bao & Jan Tuinstra, 2015. "Microfoundations for Switching Behavior in Heterogeneous Agent Models: An Experiment," Working Paper Series 31, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    57. Lux, Thomas, 2012. "Estimation of an agent-based model of investor sentiment formation in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1284-1302.
    58. Luisanna Cocco & Giulio Concas & Michele Marchesi, 2014. "Using an Artificial Financial Market for studying a Cryptocurrency Market," Papers 1406.6496, arXiv.org.
    59. Di Guilmi, Corrado & Carvalho, Laura, 2017. "The dynamics of leverage in a demand-driven model with heterogeneous firms," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 70-90.
    60. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy.
    61. Lux, Thomas, 2020. "Bayesian estimation of agent-based models via adaptive particle Markov chain Monte Carlo," Economics Working Papers 2020-01, Christian-Albrechts-University of Kiel, Department of Economics.
    62. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    63. Rytis Kazakeviv{c}ius & Aleksejus Kononovicius, 2023. "Anomalous diffusion and long-range memory in the scaled voter model," Papers 2301.08088, arXiv.org, revised Feb 2023.
    64. Makoto Nirei & Tsutomu Watanabe, 2014. "Beauty Contests and Fat Tails in Financial Markets," CARF F-Series CARF-F-346, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    65. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    66. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    67. Alfarano, Simone & Milakovic, Mishael, 2009. "Network structure and N-dependence in agent-based herding models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 78-92, January.
    68. Emna Mnif & Anis Jarboui & M. Kabir Hassan & Khaireddine Mouakhar, 2020. "Big data tools for Islamic financial analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 10-21, January.
    69. A. E. Biondo & A. Pluchino & A. Rapisarda, 2015. "Modelling Financial Markets by Self-Organized Criticality," Papers 1507.04298, arXiv.org, revised Oct 2015.
    70. Kai Li, 2014. "Asset Price Dynamics with Heterogeneous Beliefs and Time Delays," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2014, January-A.
    71. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2016. "The noisy voter model on complex networks," Papers 1602.06935, arXiv.org, revised Apr 2016.
    72. Nicolas, Maxime L.D., 2022. "Estimating a model of herding behavior on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    73. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    74. Jovanovic, Franck & Schinckus, Christophe, 2016. "Breaking down the barriers between econophysics and financial economics," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 256-266.
    75. Chen, Zhenxi & Zheng, Huanhuan, 2022. "Herding in the Chinese and US stock markets: Evidence from a micro-founded approach," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 597-604.
    76. Aleksejus Kononovicius & Vygintas Gontis & Valentas Daniunas, 2012. "Agent-based Versus Macroscopic Modeling of Competition and Business Processes in Economics and Finance," Papers 1202.3533, arXiv.org, revised Jun 2012.
    77. David Vidal-Tomás & Simone Alfarano, 2020. "An agent-based early warning indicator for financial market instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 49-87, January.
    78. Kononovicius, A. & Gontis, V., 2014. "Control of the socio-economic systems using herding interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 80-84.
    79. Irle, Albrecht & Kauschke, Jonas & Lux, Thomas & Milaković, Mishael, 2010. "Switching rates and the asymptotic behavior of herding models," Kiel Working Papers 1595, Kiel Institute for the World Economy.
    80. Malliaris, A.G. & Kyrtsou, C., 2009. "Editorial introduction of the special issue: "Energy sector pricing and macroeconomic dynamics"," Energy Economics, Elsevier, vol. 31(6), pages 825-826, November.
    81. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.
    82. Moran, José & Fosset, Antoine & Kirman, Alan & Benzaquen, Michael, 2021. "From ants to fishing vessels: a simple model for herding and exploitation of finite resources," Journal of Economic Dynamics and Control, Elsevier, vol. 129(C).
    83. Peralta, Antonio F. & Khalil, Nagi & Toral, Raúl, 2020. "Ordering dynamics in the voter model with aging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 552(C).
    84. Aleksejus Kononovicius & Vygintas Gontis, 2012. "Three-state herding model of the financial markets," Papers 1210.1838, arXiv.org, revised Jan 2013.
    85. Kaltwasser, Pablo Rovira, 2010. "Uncertainty about fundamentals and herding behavior in the FOREX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1215-1222.
    86. M. Cristelli & L. Pietronero & A. Zaccaria, 2011. "Critical Overview of Agent-Based Models for Economics," Papers 1101.1847, arXiv.org.
    87. Tang, Yinan & Chen, Ping, 2014. "Time varying moments, regime switch, and crisis warning: The birth–death process with changing transition probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 56-64.
    88. Lux, Thomas, 2009. "Mass psychology in action: identification of social interaction effects in the German stock market," Kiel Working Papers 1514, Kiel Institute for the World Economy.
    89. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda, 2017. "Informative Contagion Dynamics in a Multilayer Network Model of Financial Markets," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 343-366, November.
    90. H. Lamba, 2009. "A queueing theory description of fat-tailed price returns in imperfect financial markets," Papers 0908.0949, arXiv.org, revised Aug 2010.
    91. Muhammad Arslan & Ahmed Imran Hunjra & Wajid Shakeel Ahmed & Younes Ben Zaied, 2024. "Forecasting multi‐frequency intraday exchange rates using deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1338-1355, August.
    92. Palczewski, Jan & Schenk-Hoppé, Klaus Reiner, 2010. "From discrete to continuous time evolutionary finance models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(5), pages 913-931, May.
    93. Bharti & Nupur Soti & Ashish Kumar, 2025. "Thematic Review and Discussion of Research on Herd Behavior in Capital Markets: Highlighting the Gaps and Proposing Future Research Avenues," SAGE Open, , vol. 15(1), pages 21582440251, February.
    94. Domenico Delli Gatti & Corrado Di Guilmi & Mauro Gallegati & Simone Landini, 2012. "Reconstructing Aggregate Dynamics in Heterogeneous Agents Models. A Markovian Approach," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 117-146.
    95. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2019. "Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models," Papers 1904.04951, arXiv.org, revised Mar 2021.
    96. Hohnisch, Martin & Westerhoff, Frank, 2008. "Business cycle synchronization in a simple Keynesian macro-model with socially transmitted economic sentiment and international sentiment spill-over," Structural Change and Economic Dynamics, Elsevier, vol. 19(3), pages 249-259, September.
    97. Mengling Li & Huanhuan Zheng, 2017. "Heterogeneous trading and complex price dynamics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 437-442, July.
    98. Chiarella, Carl & Di Guilmi, Corrado, 2011. "The financial instability hypothesis: A stochastic microfoundation framework," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1151-1171, August.
    99. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    100. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    101. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005-13, Christian-Albrechts-University of Kiel, Department of Economics.
    102. Pastushkov, A., 2025. "Evolutionary and agent-based computational finance: The new paradigms for asset pricing," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 196-222.
    103. Di Guilmi, C. & Gallegati, M. & Landini, S. & Stiglitz, J.E., 2020. "An analytical solution for network models with heterogeneous and interacting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 189-220.
    104. Zhenxi Chen & Jing Ru, 2021. "Herding and capitalization size in the Chinese stock market: a micro-foundation evidence," Empirical Economics, Springer, vol. 60(4), pages 1895-1911, April.
    105. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    106. Alfarano Simone & Milakovic Mishael, 2012. "Identification of Interaction Effects in Survey Expectations: A Cautionary Note," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-23, October.
    107. Khalil, Nagi & Toral, Raúl, 2019. "The noisy voter model under the influence of contrarians," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 81-92.
    108. Aleksejus Kononovicius & Julius Ruseckas, 2018. "Order book model with herd behavior exhibiting long-range memory," Papers 1809.02772, arXiv.org, revised Apr 2019.
    109. Michael Hatcher & Tim Hellmann, 2024. "Communication, networks and asset price dynamics: a survey," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(1), pages 1-58, January.
    110. Vygintas Gontis & Aleksejus Kononovicius & Stefan Reimann, 2012. "The class of nonlinear stochastic models as a background for the bursty behavior in financial markets," Papers 1201.3083, arXiv.org, revised May 2012.
    111. Jia-Ping Huang & Yang Zhang & Juanxi Wang, 2023. "Dynamic effects of social influence on asset prices," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 671-699, July.
    112. Tang, Yinan & Chen, Ping, 2015. "Transition probability, dynamic regimes, and the critical point of financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 11-20.
    113. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2013. "Signal amplification in an agent-based herding model," Papers 1302.6477, arXiv.org, revised Sep 2015.
    114. Aleksejus Kononovicius & Vygintas Gontis, 2013. "Control of the socio-economic systems using herding interactions," Papers 1309.6105, arXiv.org, revised Feb 2014.

  14. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006-13, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    2. Cevik, Emrah Ismail, 2012. "İstanbul Menkul Kıymetler Borsası’nda etkin piyasa hipotezinin uzun hafıza modelleri ile analizi: sektörel bazda bir inceleme [The testing of efficient market hypothesis in the Istanbul Stock Exchange by using long memory models: a sector-specific," MPRA Paper 71484, University Library of Munich, Germany, revised 2012.
    3. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    4. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy.
    5. González-Pla, Francisco & Lovreta, Lidija, 2019. "Persistence in firm’s asset and equity volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Aßmuth, Pascal, 2015. "Stock price related financial fragility and growth patterns," Center for Mathematical Economics Working Papers 539, Center for Mathematical Economics, Bielefeld University.
    7. Cai, Guixin & Zhang, Hao & Chen, Ziyue, 2019. "Comovement between commodity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1247-1258.
    8. Aßmuth, Pascal, 2017. "Stock price related financial fragility and growth patterns," Economics Discussion Papers 2017-108, Kiel Institute for the World Economy.
    9. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2009. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns," CARF F-Series CARF-F-183, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    10. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    11. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    12. Kurt Dopfer, 2011. "Economics in a Cultural Key: Complexity and Evolution Revisited," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 14, Edward Elgar Publishing.
    13. Xin-Lan Fu & Xing-Lu Gao & Zheng Shan & Zhi-Qiang Jiang & Wei-Xing Zhou, 2018. "Multifractal characteristics and return predictability in the Chinese stock markets," Papers 1806.07604, arXiv.org.
    14. Kukacka, Jiri & Kristoufek, Ladislav, 2021. "Does parameterization affect the complexity of agent-based models?," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 324-356.
    15. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    16. Julien Idier, 2008. "Long term vs. short term comovements in stock markets: the use of Markov-switching multifractal models," Working papers 218, Banque de France.
    17. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    18. Siokis, Fotios M., 2013. "Multifractal analysis of stock exchange crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1164-1171.
    19. Korkmaz, Turhan & Cevik, Emrah Ismail & Özataç, Nesrin, 2009. "Testing for long memory in ISE using Arfima-Figarch model and structural break test," MPRA Paper 71302, University Library of Munich, Germany.
    20. Calvet, Laurent E. & Fearnley, Marcus & Fisher, Adlai J. & Leippold, Markus, 2015. "What is beneath the surface? Option pricing with multifrequency latent states," Journal of Econometrics, Elsevier, vol. 187(2), pages 498-511.
    21. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "Predicting bid-ask spreads using long memory autoregressive conditional poisson models," SFB 649 Discussion Papers 2011-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    22. Söderberg, Jonas, 2008. "Do Macroeconomic Variables Forecast Changes in Liquidity? An Out-of-sample Study on the Order-driven Stock Markets in Scandinavia," CAFO Working Papers 2009:10, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
    23. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.
    24. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
    25. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    26. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.
    27. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    28. Lux, Thomas, 2013. "Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model," Kiel Working Papers 1871, Kiel Institute for the World Economy.
    29. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    30. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    31. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    32. Aßmuth, Pascal, 2020. "Stock price related financial fragility and growth patterns," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 14, pages 1-34.
    33. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    34. Thomas Lux & Jaba Ghonghadze, 2011. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Post-Print hal-00711445, HAL.
    35. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    36. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2015. "Modeling and forecasting crude oil price volatility: Evidence from historical and recent data," FinMaP-Working Papers 31, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    37. Elliott, Robert J. & Siu, Tak Kuen & Badescu, Alexandru, 2011. "On pricing and hedging options in regime-switching models with feedback effect," Journal of Economic Dynamics and Control, Elsevier, vol. 35(5), pages 694-713, May.
    38. Liu, Yufang & Zhang, Weiguo & Fu, Junhui, 2016. "Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 56-66.
    39. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    40. Jianxin Wamg, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.
    41. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
    42. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy.
    43. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    44. Argyroudis, G. & Siokis, F., 2018. "The complexity of the HANG SENG Index and its constituencies during the 2007–2008 Great Recession," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 463-474.
    45. Siokis, Fotios M., 2014. "European economies in crisis: A multifractal analysis of disruptive economic events and the effects of financial assistance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 283-292.
    46. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.
    47. Ivan Letteri & Giuseppe Della Penna & Giovanni De Gasperis & Abeer Dyoub, 2022. "DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks," Papers 2210.11532, arXiv.org.

  15. Lux, Thomas, 2006. "The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility," Economics Working Papers 2006-17, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    3. Pengfei Wang & Wei Zhang & Xiao Li & Dehua Shen, 2019. "Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 377-418, June.
    4. Wahyudi, Imam & Luxianto, Rizky & Iwani, Niken & Sulung, Liyu Adhika Sari, 2008. "Early Warning System in ASEAN Countries Using Capital Market Index Return: Modified Markov Regime Switching Model," MPRA Paper 59723, University Library of Munich, Germany, revised 16 Jul 2010.
    5. Ola L{o}vsletten & Martin Rypdal, 2012. "A multifractal approach towards inference in finance," Papers 1202.5376, arXiv.org.
    6. Liu, Ruipeng & Lux, Thomas, 2017. "Generalized Method of Moment estimation of multivariate multifractal models," Economic Modelling, Elsevier, vol. 67(C), pages 136-148.
    7. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy.
    8. Calvet, Laurent E. & Fisher, Adlai J., 2008. "Multifrequency jump-diffusions: An equilibrium approach," Journal of Mathematical Economics, Elsevier, vol. 44(2), pages 207-226, January.
    9. Andria, Joseph & di Tollo, Giacomo & Kalda, Jaan, 2022. "The predictive power of power-laws: An empirical time-arrow based investigation," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    10. Lee, Hojin & Song, Jae Wook & Chang, Woojin, 2016. "Multifractal Value at Risk model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 113-122.
    11. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are Multifractal Processes Suited to Forecasting Electricity Price Volatility? Evidence from Australian Intraday Data," Working Papers 201739, University of Pretoria, Department of Economics.
    12. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    13. Sattarhoff, Cristina & Lux, Thomas, 2021. "Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities," Economics Working Papers 2021-02, Christian-Albrechts-University of Kiel, Department of Economics.
    14. 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.
    15. Patrice Abry & Yannick Malevergne & Herwig Wendt & Marc Senneret & Laurent Jaffrès & Blaise Liaustrat, 2019. "Shuffling for understanding multifractality, application to asset price time series," Post-Print hal-02361738, HAL.
    16. Lux, Thomas, 2022. "Inference for Nonlinear State Space Models: A Comparison of Different Methods applied to Markov-Switching Multifractal Models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 69-95.
    17. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," CQE Working Papers 7118, Center for Quantitative Economics (CQE), University of Muenster.
    18. Julien Idier, 2008. "Long term vs. short term comovements in stock markets: the use of Markov-switching multifractal models," Working papers 218, Banque de France.
    19. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark Wohar, 2020. "Volatility forecasting with bivariate multifractal models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 155-167, March.
    20. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
    21. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    22. Ruipeng Liu & Rangan Gupta, 2022. "Investors’ Uncertainty and Forecasting Stock Market Volatility," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(3), pages 327-337, July.
    23. Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
    24. Mawuli Segnon & Mark Trede, 2017. "Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach," CQE Working Papers 6617, Center for Quantitative Economics (CQE), University of Muenster.
    25. Filip Zikes & Jozef Barunik & Nikhil Shenai, 2012. "Modeling and Forecasting Persistent Financial Durations," Papers 1208.3087, arXiv.org, revised Apr 2013.
    26. Fernández-Martínez, M. & Sánchez-Granero, M.A. & Casado Belmonte, M.P. & Trinidad Segovia, J.E., 2020. "A note on power-law cross-correlated processes," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    27. Kostyantyn MALYSHENKO & Vadim MALYSHENKO & Elena Yu. PONOMAREVA & Marina ANASHKINA, 2019. "Analysis of the stock market anomalies in the context of changing the information paradigm," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 239-270, June.
    28. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    29. Giuseppe Brandi & Tiziana Di Matteo, 2026. "Multiscaling in the Rough Bergomi Model: A Tale of Tails," Papers 2601.11305, arXiv.org.
    30. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
    31. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    32. Calvet, Laurent E. & Fearnley, Marcus & Fisher, Adlai J. & Leippold, Markus, 2015. "What is beneath the surface? Option pricing with multifrequency latent states," Journal of Econometrics, Elsevier, vol. 187(2), pages 498-511.
    33. C. Gonçalves P., 2015. "Financial Market Modeling With Quantum Neural Networks," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(4), pages 44-63.
    34. M. Rypdal & O. L{o}vsletten, 2011. "Multifractal modeling of short-term interest rates," Papers 1111.5265, arXiv.org.
    35. G.-F. Gu & W.-X. Zhou, 2009. "On the probability distribution of stock returns in the Mike-Farmer model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(4), pages 585-592, February.
    36. Amélie Charles & Olivier Darné, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Post-Print hal-01598141, HAL.
    37. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models," FinMaP-Working Papers 46, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    38. R'emy Chicheportiche & Jean-Philippe Bouchaud, 2012. "The fine-structure of volatility feedback I: multi-scale self-reflexivity," Papers 1206.2153, arXiv.org, revised Sep 2013.
    39. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    40. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    41. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.
    42. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy.
    43. Lux, Thomas, 2018. "Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models," Economics Working Papers 2018-07, Christian-Albrechts-University of Kiel, Department of Economics.
    44. Ruipeng Liu & Mawuli Segnon & Oguzhan Cepni & Rangan Gupta, 2023. "Forecasting Volatility of Commodity, Currency, and Stock Markets: Evidence from Markov Switching Multifractal Models," Working Papers 202340, University of Pretoria, Department of Economics.
    45. Sattarhoff, Cristina & Lux, Thomas, 2023. "Forecasting the variability of stock index returns with the multifractal random walk model for realized volatilities," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1678-1697.
    46. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
    47. Lux, Thomas, 2013. "Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model," Kiel Working Papers 1871, Kiel Institute for the World Economy.
    48. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    49. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    50. Patrice Abry & Yannick Malevergne & Herwig Wendt & Stéphane Jaffard & Marc Senneret & Laurent Jaffrès, 2022. "Foreign Exchange Multivariate Multifractal Analysis," Post-Print hal-03735497, HAL.
    51. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    52. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    53. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2015. "Modeling and forecasting crude oil price volatility: Evidence from historical and recent data," FinMaP-Working Papers 31, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    54. Liu, Yufang & Zhang, Weiguo & Fu, Junhui, 2016. "Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 56-66.
    55. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
    56. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy.
    57. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
    58. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    59. Ruipeng Liu & Mawuli Segnon & Rangan Gupta & Elie Bouri, 2021. "Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective," Working Papers 202178, University of Pretoria, Department of Economics.
    60. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    61. J. Doyne Farmer & John Geanakoplos, 2008. "The Virtues and Vices of Equilibrium and the Future of Financial Economics," Cowles Foundation Discussion Papers 1647, Cowles Foundation for Research in Economics, Yale University.
    62. Jochen Heberle & Cristina Sattarhoff, 2017. "A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators," Econometrics, MDPI, vol. 5(1), pages 1-16, January.

  16. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005-13, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2017. "Market entry waves and volatility outbursts in stock markets," BERG Working Paper Series 128, Bamberg University, Bamberg Economic Research Group.
    3. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    4. O. Hermsen, 2010. "Does Basel II destabilize financial markets? An agent-based financial market perspective," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 29-40, January.
    5. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    6. Hermsen, Oliver & Witte, Björn-Christopher & Westerhoff, Frank, 2009. "Disclosure requirements, the release of new information and market efficiency: new insights from agent-based models," Economics Discussion Papers 2009-51, Kiel Institute for the World Economy.
    7. Alfarano, Simone & Milakovic, Mishael & Raddant, Matthias, 2011. "A Note on institutional hierarchy and volatility in financial markets," MPRA Paper 30902, University Library of Munich, Germany.
    8. Webel, Karsten, 2012. "Chaos in German stock returns — New evidence from the 0–1 test," Economics Letters, Elsevier, vol. 115(3), pages 487-489.
    9. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    10. Thomas Lux, 2009. "Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey," Post-Print hal-00720175, HAL.
    11. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The memory of stock return volatility: Asset pricing implications," Journal of Financial Markets, Elsevier, vol. 47(C).
    12. Biondo, Alessio Emanuele & Mazzarino, Laura & Pluchino, Alessandro, 2024. "Trading strategies and Financial Performances: A simulation approach," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    13. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    14. Ryuji Hashimoto & Kiyoshi Izumi, 2025. "Towards Realistic and Interpretable Market Simulations: Factorizing Financial Power Law using Optimal Transport," Papers 2507.09863, arXiv.org.
    15. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    16. Ghonghadze, Jaba & Lux, Thomas, 2015. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," FinMaP-Working Papers 38, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    17. Sattarhoff, Cristina & Gronwald, Marc, 2022. "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    18. La Spada Gabriele & Lillo Fabrizio, 2014. "The effect of round-off error on long memory processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 445-482, September.
    19. Yun Xiang & Shijie Deng, 2025. "Long-range dependence and asset return anomaly," Annals of Operations Research, Springer, vol. 346(1), pages 369-391, March.
    20. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    21. Menkhoff, Lukas & Rebitzky, Rafael R. & Schröder, Michael, 2009. "Heterogeneity in exchange rate expectations: Evidence on the chartist-fundamentalist approach," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 241-252, May.
    22. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    23. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    24. Mario A Bertella & Felipe R Pires & Ling Feng & Harry Eugene Stanley, 2014. "Confidence and the Stock Market: An Agent-Based Approach," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    25. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy.
    26. Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
    27. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    28. Erol Akcay & David Hirshleifer, 2020. "Social Finance: Cultural Evolution, Transmission Bias and Market Dynamics," NBER Working Papers 27745, National Bureau of Economic Research, Inc.
    29. Lux, Thomas, 2008. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Kiel Working Papers 1424, Kiel Institute for the World Economy.
    30. Susanne M. Schennach, 2018. "Long Memory via Networking," Econometrica, Econometric Society, vol. 86(6), pages 2221-2248, November.
    31. Irle, Albrecht & Kauschke, Jonas & Lux, Thomas & Milaković, Mishael, 2010. "Switching rates and the asymptotic behavior of herding models," Kiel Working Papers 1595, Kiel Institute for the World Economy.
    32. Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 625-655, November.
    33. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2005. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2005-14, Christian-Albrechts-University of Kiel, Department of Economics.
    34. Li, Wen & Yu, Cindy & Carriquiry, Alicia & Kliemann, Wolfgang, 2011. "The asymptotic behavior of the R/S statistic for fractional Brownian motion," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 83-91, January.
    35. Sato, Aki-Hiro, 2012. "Patterns of regional travel behavior: An analysis of Japanese hotel reservation data," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 55-65.
    36. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    37. Hernández, Juan Antonio & Benito, Rosa Marı´a & Losada, Juan Carlos, 2012. "An adaptive stochastic model for financial markets," Chaos, Solitons & Fractals, Elsevier, vol. 45(6), pages 899-908.
    38. V. Alfi & M. Cristelli & L. Pietronero & A. Zaccaria, 2008. "Mechanisms of Self-Organization and Finite Size Effects in a Minimal Agent Based Model," Papers 0811.4256, arXiv.org.
    39. Ghonghadze, Jaba & Lux, Thomas, 2009. "Modeling the dynamics of EU economic sentiment indicators: an interaction-based approach," Kiel Working Papers 1487, Kiel Institute for the World Economy.
    40. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2020. "Beta uncertainty," Journal of Banking & Finance, Elsevier, vol. 116(C).
    41. Thomas Lux & Jaba Ghonghadze, 2011. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Post-Print hal-00711445, HAL.
    42. Roberto Veneziani & Luca Zamparelli & Reiner Franke & Frank Westerhoff, 2017. "Taking Stock: A Rigorous Modelling Of Animal Spirits In Macroeconomics," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1152-1182, December.
    43. Georges, Christophre, 2008. "Staggered updating in an artificial financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2809-2825, September.
    44. Marwil J. Dávila-Fernández & Serena Sordi & Alessia Cafferata, 2024. "How do you feel about going green? Modelling environmental sentiments in a growing open economy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(4), pages 649-687, October.
    45. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    46. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    47. Makoto Nirei & Theodoros Stamatiou & Vladyslav Sushko, 2012. "Stochastic Herding in Financial Markets Evidence from Institutional Investor Equity Portfolios," BIS Working Papers 371, Bank for International Settlements.

  17. Lux, Thomas & Kaizoji, Taisei, 2004. "Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models," Economics Working Papers 2004-05, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Rachna Mahalwala, 2016. "A Study of Expiration-day Effects of Index Derivatives Trading in India," Metamorphosis: A Journal of Management Research, , vol. 15(1), pages 10-19, June.
    2. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    3. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.

  18. Taisei Kaizoji & Thomas Lux, 2004. "Forecasting Volume and Volatility in the Tokyo Stock Market: The Advantage of Long Memory Models," Computing in Economics and Finance 2004 158, Society for Computational Economics.

    Cited by:

    1. Rachna Mahalwala, 2016. "A Study of Expiration-day Effects of Index Derivatives Trading in India," Metamorphosis: A Journal of Management Research, , vol. 15(1), pages 10-19, June.
    2. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    3. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.

  19. Lux, Thomas, 2003. "Detecting multi-fractal properties in asset returns: The failure of the scaling estimator," Economics Working Papers 2003-14, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Kwapień, J. & Drożdż, S. & Oświe¸cimka, P., 2006. "The bulk of the stock market correlation matrix is not pure noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 589-606.
    2. Bell, Peter, 2012. "Goodness of fit test for the multifractal model of asset returns," MPRA Paper 38689, University Library of Munich, Germany.
    3. Kwapień, J. & Oświe¸cimka, P. & Drożdż, S., 2005. "Components of multifractality in high-frequency stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 466-474.
    4. Oświe¸cimka, P. & Kwapień, J. & Drożdż, S., 2005. "Multifractality in the stock market: price increments versus waiting times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 626-638.
    5. Martin Rypdal & Espen Sirnes & Ola L{o}vsletten & Kristoffer Rypdal, 2012. "Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations," Papers 1202.4877, arXiv.org.

  20. Alfarano, Simone & Lux, Thomas, 2003. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2003-15, Christian-Albrechts-University of Kiel, Department of Economics.

    Cited by:

    1. Heni Boubaker & Nadia Sghaier, 2014. "Wavelet based Estimation of Time- Varying Long Memory Model with Nonlinear Fractional Integration Parameter," Working Papers 2014-284, Department of Research, Ipag Business School.
    2. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
    3. Vishwesha Guttal & Srinivas Raghavendra & Nikunj Goel & Quentin Hoarau, 2016. "Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    4. David Morton de Lachapelle & Damien Challet, 2009. "Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior," Papers 0912.4723, arXiv.org, revised Jun 2010.
    5. Gilles Dufrenot & Dominique Guegan & Anne Peguin-Feissolle, 2008. "Changing-regime volatility: A fractionally integrated SETAR model," PSE-Ecole d'économie de Paris (Postprint) halshs-00185369, HAL.
    6. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    7. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Moran, José & Fosset, Antoine & Kirman, Alan & Benzaquen, Michael, 2021. "From ants to fishing vessels: a simple model for herding and exploitation of finite resources," Journal of Economic Dynamics and Control, Elsevier, vol. 129(C).
    9. Luis Goncalves de Faria, 2022. "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers 2206.09772, arXiv.org.

  21. Thomas Lux, 2003. "The Multi-Fractal Model of Asset Returns:Its Estimation via GMM and Its Use for Volatility Forecasting," Computing in Economics and Finance 2003 14, Society for Computational Economics.

    Cited by:

    1. Thomas Lux, 2004. "Detecting Multifractal Properties In Asset Returns: The Failure Of The "Scaling Estimator"," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 481-491.
    2. Suárez-García, Pablo & Gómez-Ullate, David, 2014. "Multifractality and long memory of a financial index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 226-234.
    3. Kwapień, J. & Drożdż, S. & Oświe¸cimka, P., 2006. "The bulk of the stock market correlation matrix is not pure noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 589-606.
    4. Indranil Mukherjee & Amitava Sarkar, 2011. "Complexity, Financial Markets and their Scaling Laws," DEGIT Conference Papers c016_008, DEGIT, Dynamics, Economic Growth, and International Trade.
    5. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    6. Kwapień, J. & Oświe¸cimka, P. & Drożdż, S., 2005. "Components of multifractality in high-frequency stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 466-474.
    7. Eisler, Z. & Kertész, J., 2004. "Multifractal model of asset returns with leverage effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 603-622.
    8. Davies, Paul Lyndon, 2006. "Long range financial data and model choice," Technical Reports 2006,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. G.-F. Gu & W.-X. Zhou, 2009. "On the probability distribution of stock returns in the Mike-Farmer model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(4), pages 585-592, February.
    10. Lux, Thomas & Kaizoji, Taisei, 2004. "Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models," Economics Working Papers 2004-05, Christian-Albrechts-University of Kiel, Department of Economics.
    11. Oświe¸cimka, P. & Kwapień, J. & Drożdż, S., 2005. "Multifractality in the stock market: price increments versus waiting times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 626-638.
    12. E. Bacry & A. Kozhemyak & J. F. Muzy, 2011. "Log-normal continuous cascade model of asset returns: aggregation properties and estimation," Quantitative Finance, Taylor & Francis Journals, vol. 13(5), pages 795-818, October.
    13. Amir Safari & Detlef Seese, 2009. "Non-parametric estimation of a multiscale CHARN model using SVR," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 105-121.
    14. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    15. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    16. Pablo Su'arez-Garc'ia & David G'omez-Ullate, 2013. "Multifractality and long memory of a financial index," Papers 1306.0490, arXiv.org.
    17. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.

  22. Simone Alfarano & Thomas Lux, 2002. "A minimal noise trader model with realistic time series," Computing in Economics and Finance 2002 317, Society for Computational Economics.

    Cited by:

    1. Heni Boubaker & Nadia Sghaier, 2014. "Wavelet based Estimation of Time- Varying Long Memory Model with Nonlinear Fractional Integration Parameter," Working Papers 2014-284, Department of Research, Ipag Business School.
    2. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2010. "Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation," MPRA Paper 24719, University Library of Munich, Germany.
    3. Anne Peguin-Feissolle & Gilles Dufrénot & Dominique Guegan, 2006. "Changing-regime volatility : A fractionally integrated SETAR model," Working Papers halshs-00410540, HAL.
    4. David Morton de Lachapelle & Damien Challet, 2009. "Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior," Papers 0912.4723, arXiv.org, revised Jun 2010.
    5. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    6. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005-13, Christian-Albrechts-University of Kiel, Department of Economics.

  23. Thomas Lux & Sascha Schornstein, 2002. "Genetic Learning and the Stylized Facts of Foreign Exchange Markets," Computing in Economics and Finance 2002 22, Society for Computational Economics.

    Cited by:

    1. Isabelle Salle & Pascal Seppecher, 2013. "Social Learning about Consumption," Working Papers hal-00989233, HAL.
    2. Konrad RICHTER, 2010. "Revenue Equivalence Revisited: Bounded Rationality in Auctions," EcoMod2004 330600118, EcoMod.
    3. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2002. "Evolutionary dynamics in markets with many trader types," CeNDEF Working Papers 02-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. Davies, Paul Lyndon, 2006. "Long range financial data and model choice," Technical Reports 2006,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Alfarano, Simone & Lux, Thomas, 2003. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2003-15, Christian-Albrechts-University of Kiel, Department of Economics.

  24. Lux, Thomas & Schornstein, Sascha, 2002. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Discussion Paper Series 1: Economic Studies 2002,29, Deutsche Bundesbank.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Leonid A. Serkov, 2023. "Effect of sticky Wages on the Behavior of Economic Agents with Heterogeneous Expectations," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(2), pages 450-473.
    3. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Barbara Weißenberger & Benjamin Löhr, 2008. "Planung und Unternehmenserfolg: Stylized Facts aus der empirischen Controllingforschung im deutschsprachigen Raum von 1990–2007," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 18(4), pages 335-363, February.
    5. Salle, Isabelle & Yildizoglu, Murat & Zumpe, Martin & Sénégas, Marc-Alexandre, 2017. "Coordination through social learning in a general equilibrium model," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 64-82.
    6. Alfarano, Simone & Milakovic, Mishael & Raddant, Matthias, 2011. "A Note on institutional hierarchy and volatility in financial markets," MPRA Paper 30902, University Library of Munich, Germany.
    7. Chen, Shu-heng & Chang, Chia-ling, 2012. "Interactions in the New Keynesian DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 6, pages 1-32.
    8. Hens, Thorsten & Schenk-Hoppe, Klaus Reiner, 2005. "Evolutionary finance: introduction to the special issue," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 1-5, February.
    9. Hommes, Cars & Lux, Thomas, 2008. "Individual expectations and aggregate behavior in learning to forecast experiments," Kiel Working Papers 1466, Kiel Institute for the World Economy.
    10. Arifovic, Jasmina & Maschek, Michael K., 2012. "Currency crisis: Evolution of beliefs and policy experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 82(1), pages 131-150.
    11. Isabelle Salle & Pascal Seppecher, 2013. "Social Learning about Consumption," Working Papers hal-00989233, HAL.
    12. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Paul De Grauwe & Pablo Rovira Kaltwasser, 2006. "A Behavioral Finance Model of the Exchange Rate with Many Forecasting Rules," CESifo Working Paper Series 1849, CESifo.
    14. De Grauwe, Paul & Markiewicz, Agnieszka, 2013. "Learning to forecast the exchange rate: Two competing approaches," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 42-76.
    15. Stolzenburg, Ulrich & Lux, Thomas, 2010. "Identification of a core-periphery structure among participants of a business climate survey," Kiel Working Papers 1659, Kiel Institute for the World Economy.
    16. Daniel Fricke & Thomas Lux, 2015. "The effects of a financial transaction tax in an artificial financial market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 119-150, April.
    17. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01215947, HAL.
    18. Konrad RICHTER, 2010. "Revenue Equivalence Revisited: Bounded Rationality in Auctions," EcoMod2004 330600118, EcoMod.
    19. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2002. "Evolutionary dynamics in markets with many trader types," CeNDEF Working Papers 02-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    20. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-01215947, HAL.
    21. Xue-Zhong He & Youwei Li, 2015. "Testing of a Market Fraction Model and Power-Law Behaviour in the Dax 30," Research Paper Series 354, Quantitative Finance Research Centre, University of Technology, Sydney.
    22. Mikhail Anufriev & Cars Hommes & Tomasz Makarewicz, 2015. "Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments," Working Paper Series 29, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    23. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00983051, HAL.
    24. Alfarano, Simone & Milaković, Mishael & Raddant, Matthias, 2009. "Network hierarchy in Kirman's ant model: fund investment can create systemic risk," Economics Working Papers 2009-09, Christian-Albrechts-University of Kiel, Department of Economics.
    25. Paul De Grauwe & Pablo Rovira Kaltwasser, 2007. "Modeling Optimism and Pessimism in the Foreign Exchange Market," CESifo Working Paper Series 1962, CESifo.
    26. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy.
    27. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    28. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    29. Davies, Paul Lyndon, 2006. "Long range financial data and model choice," Technical Reports 2006,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    30. Diana MARICA, 2015. "SIMULATING AN EVOLUTIONARY MULTI-AGENT BASED MODEL OF THE STOCK MARKET Abstract : The paper focuses on artificial stock market simulations using a multi-agent model incorporating 2,000 heterogeneous agents interacting on the artificial market. The ag," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 4(Special I), pages 1-42, august.
    31. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    32. Laurie Davies & Walter Kramer, 2016. "Stylized Facts and Simulating Long Range Financial Data," Papers 1612.05229, arXiv.org.
    33. Alfarano, Simone & Milakovic, Mishael, 2009. "Network structure and N-dependence in agent-based herding models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 78-92, January.
    34. Isabelle Salle & Marc-Alexandre Sénégas & Murat Yıldızoğlu, 2019. "How transparent about its inflation target should a central bank be?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 391-427, March.
    35. Liu, Yi-Fang & Zhang, Wei & Xu, Chao & Vitting Andersen, Jørgen & Xu, Hai-Chuan, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 204-215.
    36. Pichl, Lukáš & Kaizoji, Taisei & Yamano, Takuya, 2007. "Stylized facts in internal rates of return on stock index and its derivative transactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 219-227.
    37. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    38. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-00983051, HAL.
    39. Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 625-655, November.
    40. Arifovic, Jasmina & Schmitt-Grohé, Stephanie & Uribe, Martín, 2018. "Learning to live in a liquidity trap," Journal of Economic Dynamics and Control, Elsevier, vol. 89(C), pages 120-136.
    41. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy.
    42. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2005. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2005-14, Christian-Albrechts-University of Kiel, Department of Economics.
    43. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    44. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    45. Georges, Christophre, 2006. "Learning with misspecification in an artificial currency market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(1), pages 70-84, May.
    46. Alfarano, Simone & Lux, Thomas, 2003. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2003-15, Christian-Albrechts-University of Kiel, Department of Economics.
    47. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, vol. 2(2), pages 1-14, April.
    48. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    49. Yi-Fang Liu & Wei Zhang & Chao Xu & J{o}rgen Vitting Andersen & Hai-Chuan Xu, 2013. "Impact of information cost and switching of trading strategies in an artificial stock market," Papers 1311.4274, arXiv.org, revised Jul 2014.
    50. Carl Chiarella & Xue-Zhong He & Min Zheng, 2007. "The Stochastic Dynamics of Speculative Prices," Research Paper Series 208, Quantitative Finance Research Centre, University of Technology, Sydney.
    51. Hidayet Beyhan & Burç Ülengin, 2021. "Modelling an Artificial Financial Market: Agent Based Approach," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., vol. 36(Special2), pages 71-96, January.
    52. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    53. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005-13, Christian-Albrechts-University of Kiel, Department of Economics.
    54. Pastushkov, A., 2025. "Evolutionary and agent-based computational finance: The new paradigms for asset pricing," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 196-222.
    55. Gregory Gagnon, 2012. "Exchange rate bifurcation in a stochastic evolutionary finance model," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 35(1), pages 29-58, May.
    56. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    57. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Documents de travail du Centre d'Economie de la Sorbonne 14031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    58. Shu-Heng Chen & Umberto Gostoli, 2017. "Coordination in the El Farol Bar problem: The role of social preferences and social networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(1), pages 59-93, April.

  25. Thomas Lux, 2001. "The Multi-Fractal Model of Asset Returns: Simple Moment and GMM Estimation," Computing in Economics and Finance 2001 62, Society for Computational Economics.

    Cited by:

    1. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    2. Lisa Borland & Jean-Philippe Bouchaud, 2005. "On a multi-timescale statistical feedback model for volatility fluctuations," Science & Finance (CFM) working paper archive 500059, Science & Finance, Capital Fund Management.

  26. Thomas Lux & D. Sornette, 1999. "On Rational Bubbles and Fat Tails," Papers cond-mat/9910141, arXiv.org.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Dave, Chetan & Sorge, Marco M., 2021. "Equilibrium indeterminacy and sunspot tales," European Economic Review, Elsevier, vol. 140(C).
    3. Sornette, D & Malevergne, Y, 2001. "From rational bubbles to crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 40-59.
    4. Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Papers 1003.5926, arXiv.org, revised Mar 2011.
    5. Sergio Da Silva, 2004. "International Finance, Levy Distributions, and the Econophysics of Exchange Rates," International Finance 0405018, University Library of Munich, Germany.
    6. Jannati, Sima & Korniotis, George & Kumar, Alok, 2020. "Big fish in a small pond: Locally dominant firms and the business cycle," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 219-240.
    7. Cristian Valeriu STANCIU & Cristi SPULBAR & Sabin RIZESCU, 2012. "Econophysics - related Remarks in Considering the Necessity of a Distribution Adjustment in the Eurozone Real Economy and Re-modeling its Financial System and Markets. Thermodynamics and Statistical Physics Approach," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 2(1), pages 1-9, February.
    8. Sabiou Inoua, 2015. "The Intrinsic Instability of Financial Markets," Papers 1508.02203, arXiv.org.
    9. Cruz-Aké, Salvador & Venegas-Martínez, Francisco & Cabrera-Ramos, Agustín, 2013. "Irracionalidad En Los Mercados Inmobiliarios De Los Eu," Sección de Estudios de Posgrado e Investigación de la Escuela Superios de Economía del Instituto Politécnico Nacional, in: Benemerita Universidad Autónoma de Puebla (ed.), Política Económica: Análisis Monetario, Regional e Institucional, volume 1, chapter 2, pages 49-82, Escuela Superior de Economía, Instituto Politécnico Nacional.
    10. De Grauwe, Paul & Grimaldi, Marianna, 2004. "Bubbles and Crashes in a Behavioural Finance Model," Working Paper Series 164, Sveriges Riksbank (Central Bank of Sweden).
    11. Andreas Husler & Didier Sornette & Cars H. Hommes, 2012. "Super-exponential bubbles in lab experiments: evidence for anchoring over-optimistic expectations on price," Papers 1205.0635, arXiv.org.
    12. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    13. Makoto Nirei & John Stachurski & Koichiro Takaoka & Tsutomu Watanabe, 2018. "Herding and Power Laws in Financial Markets," CARF F-Series CARF-F-434, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. Levy, Moshe, 2008. "Stock market crashes as social phase transitions," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 137-155, January.
    15. Belhoula, Mohamed Malek & Mensi, Walid & Al-Yahyaee, Khamis Hamed, 2024. "Dynamic speculation and efficiency in European natural gas markets during the COVID-19 and Russia-Ukraine crises," Resources Policy, Elsevier, vol. 98(C).
    16. Nirei, Makoto & Stachurski, John & Watanabe, Tsutomu, 2020. "Trade clustering and power laws in financial markets," Theoretical Economics, Econometric Society, vol. 15(4), November.
    17. Paul de Grauwe & Roberto Dieci & Marianna Grimaldi & Paul De Grauwe, 2005. "Fundamental and Non-Fundamental Equilibria in the Foreign Exchange Market. A Behavioural Finance Framework," CESifo Working Paper Series 1431, CESifo.
    18. Yoon, Gawon, 2012. "Some properties of periodically collapsing bubbles," Economic Modelling, Elsevier, vol. 29(2), pages 299-302.
    19. Li Lin & Didier Sornette, 2009. "Diagnostics of Rational Expectation Financial Bubbles with Stochastic Mean-Reverting Termination Times," Papers 0911.1921, arXiv.org.
    20. Sheri Markose & Amadeo Alentorn, 2005. "Option Pricing and the Implied Tail Index with the Generalized Extreme Value (GEV) Distribution," Computing in Economics and Finance 2005 397, Society for Computational Economics.
    21. B. M. Roehner & D. Sornette, 2000. ""Thermometers" of Speculative Frenzy," Papers cond-mat/0001353, arXiv.org.
    22. Makoto Nirei & John Stachurski & Tsutomu Watanabe, 2018. "Trade Clustering and Power Laws in Financial Markets (Published in Theoretical Economics, 15:1365?1398, 2020)," CARF F-Series CARF-F-450, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    23. W. -X. Zhou & D. Sornette, 2002. "Evidence of a Worldwide Stock Market Log-Periodic Anti-Bubble Since Mid-2000," Papers cond-mat/0212010, arXiv.org, revised Aug 2003.
    24. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2020. "Market-crash forecasting based on the dynamics of the alpha-stable distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    25. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    26. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    27. Jan-Christian Gerlach & Jerome Kreuser & Didier Sornette, 2020. "Awareness of crash risk improves Kelly strategies in simulated financial time series," Papers 2004.09368, arXiv.org.
    28. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2021. "Financial Return Distributions: Past, Present, and COVID-19," Papers 2107.06659, arXiv.org.
    29. Chetan Dave & Scott J. Dressler & Samreen Malik, 2022. "A Cautionary Tale of Fat Tails," Villanova School of Business Department of Economics and Statistics Working Paper Series 53, Villanova School of Business Department of Economics and Statistics.
    30. Xavier Gabaix, 2008. "Power Laws in Economics and Finance," NBER Working Papers 14299, National Bureau of Economic Research, Inc.
    31. Markose, Sheri M & Alentorn, Amadeo, 2005. "The Generalized Extreme Value (GEV) Distribution, Implied Tail Index and Option Pricing," Economics Discussion Papers 3726, University of Essex, Department of Economics.
    32. Sornette, D., 2002. "“Slimming” of power-law tails by increasing market returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 309(3), pages 403-418.
    33. Li Lin & Didier Sornette, 2023. "The inverse Cox-Ingersoll-Ross process for parsimonious financial price modeling," Papers 2302.11423, arXiv.org, revised Jun 2023.
    34. Alexander Smirnov, 2016. "A Simple Model of Credit Expansion," Papers 1609.05055, arXiv.org.
    35. Leipus, Remigijus & Paulauskas, Vygantas & Surgailis, Donatas, 2005. "Renewal regime switching and stable limit laws," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 299-327.
    36. Sabiou Inoua & Vernon Smith, 2023. "A Classical Model of Speculative Asset Price Dynamics," Papers 2307.00410, arXiv.org.
    37. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2005. "Institutional Investors and Stock Market Volatility," NBER Working Papers 11722, National Bureau of Economic Research, Inc.
    38. Johansen, Søren & Lange, Theis, 2013. "Least squares estimation in a simple random coefficient autoregressive model," Journal of Econometrics, Elsevier, vol. 177(2), pages 285-288.
    39. Sabiou Inoua, 2016. "Speculation and Power Law," Papers 1612.08705, arXiv.org.
    40. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    41. Y. Malevergne & D. Sornette, 2001. "Multi-dimensional Rational Bubbles and fat tails: application of stochastic regression equations to financial speculation," Papers cond-mat/0101371, arXiv.org.
    42. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    43. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.
    44. Li, Hui & Wu, Min & Wang, Xiao-Tian, 2009. "Fractional-moment Capital Asset Pricing model," Chaos, Solitons & Fractals, Elsevier, vol. 42(1), pages 412-421.
    45. Douglas R. Emery, 2022. "Negative bubbles and the market for “dreams”: “Lemons” in the looking glass," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 45(1), pages 5-16, March.
    46. Yan, Wanfeng & Woodard, Ryan & Sornette, Didier, 2012. "Diagnosis and prediction of rebounds in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1361-1380.
    47. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    48. Kwangwon Ahn & Hanwool Jang & Jinu Kim & Inug Ryu, 2024. "COVID-19 and REITs Crash: Predictability and Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1159-1172, March.
    49. Jerome L Kreuser & Didier Sornette, 2017. "Super-Exponential RE Bubble Model with Efficient Crashes," Swiss Finance Institute Research Paper Series 17-33, Swiss Finance Institute.
    50. Sabiou Inoua, 2023. "News-driven Expectations and Volatility Clustering," Papers 2309.04876, arXiv.org.
    51. Deev, Oleg & Kajurova, Veronika & Stavarek, Daniel, 2013. "Testing rational speculative bubbles in Central European stock markets," MPRA Paper 46582, University Library of Munich, Germany.
    52. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    53. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    54. Devendran Indiran & Nurfadhlina Abdul Halim & Wan Muhamad Amir W. Ahmad, 2015. "Estimating Fundamental Value and the Size of Rational Speculative Bubbles of Hong Kong Stock Market during the Year 2008," International Journal of Financial Economics, Research Academy of Social Sciences, vol. 4(1), pages 1-7.
    55. Sabiou M. Inoua, 2020. "News-Driven Expectations and Volatility Clustering," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    56. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for rational bubbles in banking indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 365-376.
    57. Luis Goncalves de Faria, 2022. "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers 2206.09772, arXiv.org.
    58. Nikolas L. Albertson & Heather L. R. Tierney & Jeffrey W. Cline & Daniel Boylan, 2026. "Abnormal returns and stock performance prior and after federal holidays," Review of Financial Economics, John Wiley & Sons, vol. 44(1), January.
    59. D. Sornette & R. Woodard, "undated". "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Working Papers CCSS-09-003, ETH Zurich, Chair of Systems Design.
    60. D. Sornette, 2000. ""Slimming" of power law tails by increasing market returns," Papers cond-mat/0010112, arXiv.org, revised Sep 2001.
    61. Y. Malevergne & V. F. Pisarenko & D. Sornette, 2003. "Empirical Distributions of Log-Returns: between the Stretched Exponential and the Power Law?," Papers physics/0305089, arXiv.org.
    62. Andrea Morone, 2005. "Financial Market in the Laboratory, an Experimental Analysis of some Stylized Facts," Papers on Strategic Interaction 2005-27, Max Planck Institute of Economics, Strategic Interaction Group.
    63. Marianna Grimaldi & Paul De Grauwe, 2003. "Bubbling and Crashing Exchange Rates," CESifo Working Paper Series 1045, CESifo.
    64. Li, Y., 2006. "On microscopic simulation models of financial markets," Other publications TiSEM ec2f852d-4a7f-47b1-99b8-4, Tilburg University, School of Economics and Management.
    65. Didier Sornette & Ryan Woodard, 2009. "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Papers 0905.0220, arXiv.org.
    66. Namun Cho & Tae-Seok Jang, 2019. "Asset Market Volatility and New Keynesian Macroeconomics: A Game-Theoretic Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 245-266, June.
    67. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.
    68. Vogel, Harold L. & Werner, Richard A., 2015. "An analytical review of volatility metrics for bubbles and crashes," International Review of Financial Analysis, Elsevier, vol. 38(C), pages 15-28.
    69. Makoto Nirei & Theodoros Stamatiou & Vladyslav Sushko, 2012. "Stochastic Herding in Financial Markets Evidence from Institutional Investor Equity Portfolios," BIS Working Papers 371, Bank for International Settlements.

Articles

  1. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Dong, Xin & Gong, Jinguo & Wang, Qin, 2025. "Environmental attention and the predictability of crude oil volatility: Evidence from a new MIDAS multifractal model," Energy Economics, Elsevier, vol. 143(C).
    3. Liu, Ruipeng & Lux, Thomas, 2017. "Generalized Method of Moment estimation of multivariate multifractal models," Economic Modelling, Elsevier, vol. 67(C), pages 136-148.
    4. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    5. Andria, Joseph & di Tollo, Giacomo & Kalda, Jaan, 2022. "The predictive power of power-laws: An empirical time-arrow based investigation," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    7. Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
    8. Mawuli Segnon & Mark Trede, 2017. "Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach," CQE Working Papers 6617, Center for Quantitative Economics (CQE), University of Muenster.
    9. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, September.
    10. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    11. Kwan, Wilson & Li, Wai Keung & Li, Guodong, 2012. "On the estimation and diagnostic checking of the ARFIMA–HYGARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3632-3644.
    12. Janczura, Joanna & Weron, Rafal, 2011. "Goodness-of-fit testing for the marginal distribution of regime-switching models," MPRA Paper 32532, University Library of Munich, Germany.
    13. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    14. Diniz, Ana & Barreiros, João & Crato, Nuno, 2012. "A new model for explaining long-range correlations in human time interval production," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1908-1919.
    15. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models," FinMaP-Working Papers 46, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    16. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    17. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    18. Mohamed CHIKHI & Ali BENDOB & Ahmed Ramzi SIAGH, 2019. "Day-of-the-week and month-of-the-year effects on French Small-Cap Volatility: the role of asymmetry and long memory," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 221-248, December.
    19. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    20. Dimitrios I. Vortelinos, 2015. "Out‐of‐sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini‐futures markets," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 58-67, November.
    21. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    22. Gorka Muñoz-Gil & Harshith Bachimanchi & Jesús Pineda & Benjamin Midtvedt & Gabriel Fernández-Fernández & Borja Requena & Yusef Ahsini & Solomon Asghar & Jaeyong Bae & Francisco J. Barrantes & Steen W, 2025. "Quantitative evaluation of methods to analyze motion changes in single-particle experiments," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    23. Liu, Yufang & Zhang, Weiguo & Fu, Junhui, 2016. "Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 56-66.
    24. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
    25. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy.
    26. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    27. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
    28. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    29. De Angelis Luca & Viroli Cinzia, 2017. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-22, April.

  2. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    See citations under working paper version above.
  3. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    See citations under working paper version above.
  4. Farmer, J. Doyne & Lux, Thomas, 2008. "Introduction to special issue on `Applications of Statistical Physics in Economics and Finance'," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 1-6, January.

    Cited by:

    1. Schinckus, Christophe, 2018. "Ising model, econophysics and analogies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 95-103.
    2. Vladik Kreinovich & Monchaya Chiangpradit & Wararit Panichkitkosolkul, 2012. "Efficient algorithms for heavy-tail analysis under interval uncertainty," Annals of Operations Research, Springer, vol. 195(1), pages 73-96, May.
    3. Jovanovic, Franck & Schinckus, Christophe, 2016. "Breaking down the barriers between econophysics and financial economics," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 256-266.
    4. Marcel Ausloos & Franck Jovanovic & Christophe Schinckus, 2016. "On the "usual" misunderstandings between econophysics and finance: some clarifications on modelling approaches and efficient market hypothesis," Papers 1606.02045, arXiv.org.
    5. Shu-Heng Chen & Sai-Ping Li, 2011. "Econophysics: Bridges over a Turbulent Current," Papers 1107.5373, arXiv.org.
    6. Sandro Claudio Lera & Didier Sornette, 2015. "Currency target zone modeling: An interplay between physics and economics," Papers 1508.04754, arXiv.org, revised Oct 2015.
    7. Gregor Semieniuk & Ellis Scharfenaker, 2014. "A Bayesian Latent Variable Mixture Model for Filtering Firm Profit Rate," SCEPA working paper series. 2014-1, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    8. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

  5. Lux, Thomas, 2008. "The Markov-Switching Multifractal Model of Asset Returns: GMM Estimation and Linear Forecasting of Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 194-210, April.
    See citations under working paper version above.
  6. Silvano Cincotti & Laura Gardini & Thomas Lux, 2008. "New Advances in Financial Economics: Heterogeneity and Simulation," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 1-2, September.

    Cited by:

    1. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    2. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024, arXiv.org, revised May 2015.
    4. Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
    5. George Halkos & Mike G. Tsionas, 2019. "Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1005-1025, October.

  7. S. Alfarano & T. Lux & F. Wagner, 2007. "Empirical validation of stochastic models of interacting agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 183-187, January.

    Cited by:

    1. Donovan Platt, 2022. "Bayesian Estimation of Economic Simulation Models Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 599-650, February.
    2. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    3. Argentiero, Amedeo & Bovi, Maurizio & Cerqueti, Roy, 2016. "Bayesian estimation and entropy for economic dynamic stochastic models: An exploration of overconsumption," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 143-157.
    4. Alexandru Stan, 2015. "A Price Crash Alerting Strategy for Agent-based Artificial Financial Markets," MIC 2015: Managing Sustainable Growth; Proceedings of the Joint International Conference, Portorož, Slovenia, 28–30 May 2015,, University of Primorska, Faculty of Management Koper.
    5. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    6. Sylvain Mignot & Annick Vignes, 2020. "The Many Faces of Agent-Based Computational Economics: Ecology of Agents, Bottom-Up Approaches and Paradigm Shift [Les modèles multi-agents en économie, entre agents hétérogènes, approches bottom-up et changement de paradigme]," Post-Print hal-02956172, HAL.
    7. Colasante, Annarita, 2017. "Selection of the distributional rule as an alternative tool to foster cooperation in a Public Good Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 482-492.
    8. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    9. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    10. Platt, Donovan, 2020. "A comparison of economic agent-based model calibration methods," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    11. Donovan Platt & Tim Gebbie, 2016. "The Problem of Calibrating an Agent-Based Model of High-Frequency Trading," Papers 1606.01495, arXiv.org, revised Mar 2017.
    12. Mattia Guerini & Alessio Moneta, 2016. "A Method for Agent-Based Models Validation," LEM Papers Series 2016/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.
    14. Francesco Lamperti, 2015. "An Information Theoretic Criterion for Empirical Validation of Time Series Models," LEM Papers Series 2015/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Colasante, Annarita, 2016. "Evolution of Cooperation in Public Good Game," MPRA Paper 72577, University Library of Munich, Germany.
    16. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    17. Lamperti, Francesco, 2018. "An information theoretic criterion for empirical validation of simulation models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 83-106.
    18. J. Doyne Farmer & John Geanakoplos, 2008. "The Virtues and Vices of Equilibrium and the Future of Financial Economics," Cowles Foundation Discussion Papers 1647, Cowles Foundation for Research in Economics, Yale University.
    19. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.

  8. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 80-101, November.
    See citations under working paper version above.
  9. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    See citations under working paper version above.
  10. Akira Namatame & Thomas Lux & Robert Axtell, 2006. "Welcome to JEIC," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(1), pages 1-3, May.

    Cited by:

    1. Matthias Meyer & Iris Lorscheid & Klaus G. Troitzsch, 2009. "The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-12.

  11. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Sylvain Barde & Ofce Observatoire Français Des Conjonctures Économiques, 2016. "Direct comparison of agent-based models of herding in financial markets," Post-Print hal-03604749, HAL.
    3. S. Alfarano & T. Lux & F. Wagner, 2007. "Empirical validation of stochastic models of interacting agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 183-187, January.
    4. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2017. "Market entry waves and volatility outbursts in stock markets," BERG Working Paper Series 128, Bamberg University, Bamberg Economic Research Group.
    5. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01499344, HAL.
    6. Matthijs Lof, 2015. "Rational Speculators, Contrarians, and Excess Volatility," Management Science, INFORMS, vol. 61(8), pages 1889-1901, August.
    7. O. Hermsen, 2010. "Does Basel II destabilize financial markets? An agent-based financial market perspective," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 29-40, January.
    8. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    9. Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
    10. Eminente, Clara & Artime, Oriol & De Domenico, Manlio, 2022. "Interplay between exogenous triggers and endogenous behavioral changes in contagion processes on social networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    11. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    12. Alfarano, Simone & Milakovic, Mishael & Raddant, Matthias, 2011. "A Note on institutional hierarchy and volatility in financial markets," MPRA Paper 30902, University Library of Munich, Germany.
    13. Siyan Chen & Saul Desiderio, 2022. "Calibration of Agent-Based Models by Means of Meta-Modeling and Nonparametric Regression," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1457-1478, December.
    14. Grazzini, Jakob & Richiardi, Matteo, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201335, University of Turin.
    15. Donovan Platt, 2022. "Bayesian Estimation of Economic Simulation Models Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 599-650, February.
    16. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    17. Bharati Singh, 2021. "A Bibliometric Analysis of Behavioral Finance and Behavioral Accounting," American Business Review, Pompea College of Business, University of New Haven, vol. 24(2), pages 198-230.
    18. Di Guilmi, Corrado & He, Xue-Zhong & Li, Kai, 2014. "Herding, trend chasing and market volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 349-373.
    19. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    20. Kirill S. Glavatskiy & Mikhail Prokopenko & Adrian Carro & Paul Ormerod & Michael Harré, 2021. "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model," SN Business & Economics, Springer, vol. 1(6), pages 1-21, June.
    21. Annalisa Fabretti, 2013. "On the problem of calibrating an agent based model for financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 277-293, October.
    22. Domenico Delli Gatti & Tommaso Ferraresi & Filippo Gusella & Lilit Popoyan & Giorgio Ricchiuti & Andrea Roventini, 2024. "The complex interplay between exchange rate and real markets: an agent-based model exploration," LEM Papers Series 2024/24, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    23. Gallegati, Mauro & Kirman, Alan, 2019. "20 years of WEHIA: A journey in search of a safer road," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 5-14.
    24. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    25. Michael Wegener & Frank Westerhoff, 2012. "Evolutionary competition between prediction rules and the emergence of business cycles within Metzler’s inventory model," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 251-273, April.
    26. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Working Papers 07/2012, University of Verona, Department of Economics.
    27. Biondo, Alessio Emanuele & Mazzarino, Laura & Pluchino, Alessandro, 2024. "Trading strategies and Financial Performances: A simulation approach," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    28. Kononovicius, Aleksejus & Ruseckas, Julius, 2019. "Order book model with herd behavior exhibiting long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 171-191.
    29. Jacob Grazzini & Matteo Richiardi & Lisa Sella, 2012. "Indirect estimation of agent-based models.An application to a simple diffusion model," LABORatorio R. Revelli Working Papers Series 118, LABORatorio R. Revelli, Centre for Employment Studies.
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    171. Yi-Fang Liu & Wei Zhang & Chao Xu & J{o}rgen Vitting Andersen & Hai-Chuan Xu, 2013. "Impact of information cost and switching of trading strategies in an artificial stock market," Papers 1311.4274, arXiv.org, revised Jul 2014.
    172. Luis Goncalves de Faria, 2022. "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers 2206.09772, arXiv.org.
    173. Aleksejus Kononovicius & Valentas Daniunas, 2013. "Agent-based and macroscopic modeling of the complex socio-economic systems," Papers 1303.3693, arXiv.org, revised Apr 2013.
    174. Qingfu Liu & Yiuman Tse & Kaixin Zheng, 2021. "The impact of trading behavioral biases on market liquidity under different volatility levels: Evidence from the Chinese commodity futures market," The Financial Review, Eastern Finance Association, vol. 56(4), pages 671-692, November.
    175. Daye Li & Rongrong Li & Qiankun Sun, 2017. "How the heterogeneity in investment horizons affects market trends," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1473-1482, March.
    176. David Allen & Stephen Satchell & Colin Lizieri, 2024. "Quantifying the non-Gaussian gain," Journal of Asset Management, Palgrave Macmillan, vol. 25(1), pages 1-18, February.
    177. Frank M. A. Klingert & Matthias Meyer, 2018. "Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-7.
    178. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    179. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.
    180. Ha Che-Ngoc & Nga Do-Thi & Thao Nguyen-Trang, 2023. "Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1781-1799, December.
    181. Hohnisch, Martin & Westerhoff, Frank, 2008. "Business cycle synchronization in a simple Keynesian macro-model with socially transmitted economic sentiment and international sentiment spill-over," Structural Change and Economic Dynamics, Elsevier, vol. 19(3), pages 249-259, September.
    182. He, Xue-Zhong & Li, Youwei & Zheng, Min, 2019. "Heterogeneous agent models in financial markets: A nonlinear dynamics approach," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 135-149.
    183. Hidayet Beyhan & Burç Ülengin, 2021. "Modelling an Artificial Financial Market: Agent Based Approach," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., vol. 36(Special2), pages 71-96, January.
    184. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.
    185. Ron Bird & Lorenzo Casavecchia & Paul Woolley, 2008. "Insights into the Market Impact of Different Investment Styles," Working Paper Series 1, The Paul Woolley Centre for Capital Market Dysfunctionality, University of Technology, Sydney.
    186. Maria Elvira Mancino & Maria Cristina Recchioni, 2015. "Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-33, September.
    187. Vygintas Gontis & Aleksejus Kononovicius, 2017. "Spurious memory in non-equilibrium stochastic models of imitative behavior," Papers 1707.09801, arXiv.org.
    188. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    189. Lamperti, Francesco, 2018. "An information theoretic criterion for empirical validation of simulation models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 83-106.
    190. Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
    191. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    192. Markus Demary, 2011. "Transaction taxes, greed and risk aversion in an agent-based financial market model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(1), pages 1-28, May.
    193. Alfarano Simone & Milakovic Mishael, 2012. "Identification of Interaction Effects in Survey Expectations: A Cautionary Note," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-23, October.
    194. Tai Vo-Van & Ha Che-Ngoc & Nghiep Le-Dai & Thao Nguyen-Trang, 2022. "A New Strategy for Short-Term Stock Investment Using Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 887-911, February.
    195. Guy Maugis, Pierre-André, 2019. "Paradigm shifts," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 13, pages 1-9.
    196. Hisakado, Masato & Mori, Shintaro, 2015. "Information cascade, Kirman’s ant colony model, and kinetic Ising model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 63-75.
    197. Aleksejus Kononovicius & Julius Ruseckas, 2018. "Order book model with herd behavior exhibiting long-range memory," Papers 1809.02772, arXiv.org, revised Apr 2019.
    198. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Documents de travail du Centre d'Economie de la Sorbonne 14031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    199. Xiong, Hang & Payne, Diane & Kinsella, Stephen, 2016. "Peer effects in the diffusion of innovations: Theory and simulation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 1-13.
    200. Simone Landini & Mauro Gallegati & Joseph Stiglitz, 2015. "Economies with heterogeneous interacting learning agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 91-118, April.
    201. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.
    202. Benjamin Patrick Evans & Mikhail Prokopenko, 2022. "Bounded strategic reasoning explains crisis emergence in multi-agent market games," Papers 2206.05568, arXiv.org.
    203. Vygintas Gontis & Aleksejus Kononovicius & Stefan Reimann, 2012. "The class of nonlinear stochastic models as a background for the bursty behavior in financial markets," Papers 1201.3083, arXiv.org, revised May 2012.
    204. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2013. "Signal amplification in an agent-based herding model," Papers 1302.6477, arXiv.org, revised Sep 2015.
    205. Makoto Nirei & Theodoros Stamatiou & Vladyslav Sushko, 2012. "Stochastic Herding in Financial Markets Evidence from Institutional Investor Equity Portfolios," BIS Working Papers 371, Bank for International Settlements.
    206. Aleksejus Kononovicius & Vygintas Gontis, 2013. "Control of the socio-economic systems using herding interactions," Papers 1309.6105, arXiv.org, revised Feb 2014.
    207. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.

  12. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February.
    See citations under working paper version above.
  13. Lux, Thomas & Sornette, Didier, 2002. "On Rational Bubbles and Fat Tails," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(3), pages 589-610, August.
    See citations under working paper version above.
  14. Lux, Thomas & Marchesi, Michele, 2002. "Journal of economic behavior and organization: special issue on heterogeneous interacting agents in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 143-147, October.

    Cited by:

    1. Jozef Barunik & Jiri Kukacka, 2015. "Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 959-973, June.
    2. Frank Westerhoff & Cristian Wieland, "undated". "Spill-over dynamics of central bank interventions," Modeling, Computing, and Mastering Complexity 2003 21, Society for Computational Economics.
    3. J. Emeterio Navarro Barrientos & Frank E. Walter & Frank Schweitzer, 2008. "Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments," Papers 0801.4305, arXiv.org, revised Sep 2008.
    4. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    5. LeBaron Blake & Winker Peter, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 141-148, April.
    6. Wieland, Cristian & Westerhoff, Frank H., 2005. "Exchange rate dynamics, central bank interventions and chaos control methods," Journal of Economic Behavior & Organization, Elsevier, vol. 58(1), pages 117-132, September.

  15. T. Lux, 2001. "Power laws and long memory," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 560-562.

    Cited by:

    1. Shana M. Sundstrom & Craig R. Allen & David G. Angeler, 2020. "Scaling and discontinuities in the global economy," Journal of Evolutionary Economics, Springer, vol. 30(2), pages 319-345, April.
    2. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    3. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    4. Benoit B. Mandelbrot, 2005. "Parallel cartoons of fractal models of finance," Annals of Finance, Springer, vol. 1(2), pages 179-192, October.

  16. Thomas Lux, 2001. "The limiting extremal behaviour of speculative returns: an analysis of intra-daily data from the Frankfurt Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 299-315.

    Cited by:

    1. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-31, October.
    2. Carl Chiarella & Giulia Iori & Josep Perello, 2007. "The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows," Papers 0711.3581, arXiv.org.
    3. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    4. Upper, Christian & Werner, Thomas, 2002. "Time variation in the tail behaviour of bunds futures returns," Working Paper Series 199, European Central Bank.
    5. G. D. Gettinby & C. D. Sinclair & D. M. Power & R. A. Brown, 2004. "An Analysis of the Distribution of Extreme Share Returns in the UK from 1975 to 2000," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 607-646, June.
    6. Grobys, Klaus, 2023. "Correlation versus co-fractality: Evidence from foreign-exchange-rate variances," International Review of Financial Analysis, Elsevier, vol. 86(C).
    7. Westerhoff, Frank H. & Dieci, Roberto, 2006. "The effectiveness of Keynes-Tobin transaction taxes when heterogeneous agents can trade in different markets: A behavioral finance approach," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 293-322, February.
    8. Grilli, Ruggero & Tedeschi, Gabriele & Gallegati, Mauro, 2014. "Bank interlinkages and macroeconomic stability," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 72-88.
    9. Chao Huang & Jin-Guan Lin, 2014. "Modified maximum spacings method for generalized extreme value distribution and applications in real data analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 867-894, October.
    10. Giorgio Fagiolo & Mauro Napoletano & Andrea Roventini, 2008. "Are output growth-rate distributions fat-tailed? some evidence from OECD countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 639-669.
    11. Konstantinos Tolikas & Athanasios Koulakiotis & Richard A. Brown, 2007. "Extreme Risk and Value-at-Risk in the German Stock Market," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 373-395.
    12. Ruggero Grilli & Gabriele Tedeschi & Mauro Gallegati, 2015. "Markets connectivity and financial contagion," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 287-304, October.
    13. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    14. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy.
    15. Suárez-García, Pablo & Gómez-Ullate, David, 2013. "Scaling, stability and distribution of the high-frequency returns of the Ibex35 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1409-1417.
    16. Niklas Wagner & Terry Marsh, 2004. "Tail index estimation in small smaples Simulation results for independent and ARCH-type financial return models," Statistical Papers, Springer, vol. 45(4), pages 545-561, October.
    17. José L. B. Fernandes & Augusto Hasman & Juan Ignacio Peña, 2006. "Risk Premium: Insights Over The Threshold," Working Papers Series 126, Central Bank of Brazil, Research Department.
    18. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    19. Silverberg, Gerald & Verspagen, Bart, 2004. "The size distribution of innovations revisited: an application of extreme value statistics to citation and value measures of patent significance," Research Memorandum 021, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    20. Carolina Castaldi & Koen Frenken & Bart Los, 2015. "Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 767-781, May.
    21. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    22. Rota, Mauro & Schettino, Francesco & Spinesi, Luca, 2017. "Key inventors, teams and firm performance: The Italian case," Structural Change and Economic Dynamics, Elsevier, vol. 42(C), pages 13-25.
    23. Pan, Raj Kumar & Sinha, Sitabhra, 2008. "Inverse-cubic law of index fluctuation distribution in Indian markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2055-2065.
    24. Castaldi, Carolina & Los, Bart, 2017. "Geographical patterns in US inventive activity 1977–1998: The “regional inversion” was underestimated," Research Policy, Elsevier, vol. 46(7), pages 1187-1197.
    25. Castaldi, Carolina & Milakovic, Mishael, 2007. "Turnover activity in wealth portfolios," Journal of Economic Behavior & Organization, Elsevier, vol. 63(3), pages 537-552, July.

  17. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.

    Cited by:

    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Sylvain Mignot & Gabriele Tedeschi & Annick Vignes, 2012. "An Agent Based Model of Switching: The Case of Boulogne S/mer Fish Market," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-3.
    3. Philip Z. Maymin, 2010. "Schizophrenic Representative Investors," Papers 1004.4592, arXiv.org.
    4. Thomas Theobald, 2012. "Agent-based risk management - A regulatory approach to financial markets," IMK Working Paper 95-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    5. Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    6. Norman Ehrentreich, 2002. "The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections," Computational Economics 0209001, University Library of Munich, Germany.
    7. Feldman, Todd, 2010. "Portfolio manager behavior and global financial crises," Journal of Economic Behavior & Organization, Elsevier, vol. 75(2), pages 192-202, August.
    8. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.
    9. Silver, Steven D. & Raseta, Marko & Bazarova, Alina, 2023. "Stochastic resonance in the recovery of signal from agent price expectations," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    10. Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2013. "Asset Price Dynamics with Heterogeneous Beliefs and Local Network Interactions," Discussion Papers 2013-18, School of Economics, The University of New South Wales.
    11. Chen, Yanhua & Pantelous, Athanasios A., 2022. "The U.S.-China trade conflict impacts on the Chinese and U.S. stock markets: A network-based approach," Finance Research Letters, Elsevier, vol. 46(PB).
    12. Andrea Morone, 2004. "Financial Market in the Laboratory," Experimental 0401002, University Library of Munich, Germany.
    13. Boer-Sorban, K. & Kaymak, U. & de Bruin, A., 2005. "A Modular Agent-Based Environment for Studying Stock Markets," ERIM Report Series Research in Management ERS-2005-017-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. García Ruiz Reyna Susana & Cruz Aké Salvador & Venegas Martínez Francisco, 2014. "Una medida de eficiencia de mercado: Un enfoque de teoría de la información," Contaduría y Administración, Accounting and Management, vol. 59(4), pages 137-166, octubre-d.
    15. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    16. Xue-Zhong He & Youwei Li, 2005. "Long Memory, Heterogeneity and Trend Chasing," Research Paper Series 148, Quantitative Finance Research Centre, University of Technology, Sydney.
    17. Menkhoff, Lukas & Rebitzky, Rafael R. & Schröder, Michael, 2009. "Heterogeneity in exchange rate expectations: Evidence on the chartist-fundamentalist approach," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 241-252, May.
    18. Xue-Zhong He & Youwei Li, 2015. "Testing of a Market Fraction Model and Power-Law Behaviour in the Dax 30," Research Paper Series 354, Quantitative Finance Research Centre, University of Technology, Sydney.
    19. Henrik Amilon, 2003. "Estimation of an Adaptive Stock Market Model with Heterogeneous Agents," Research Paper Series 107, Quantitative Finance Research Centre, University of Technology, Sydney.
    20. Yeh, Chia-Hsuan, 2008. "The effects of intelligence on price discovery and market efficiency," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 613-625, December.
    21. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2002. "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 217-239, October.
    22. Lux, Thomas & Schornstein, Sascha, 2003. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Economics Working Papers 2003-12, Christian-Albrechts-University of Kiel, Department of Economics.
    23. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
    24. Mariano Matilla-García & Manuel Ruiz Marín & Mohammed Dore & Rina Ojeda, 2014. "Nonparametric correlation integral–based tests for linear and nonlinear stochastic processes," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(1), pages 181-193, April.
    25. Constantinos VORLOW & Antonios ANTONIOU & Catherine KYRTSOU, 2004. "Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series," Computing in Economics and Finance 2004 27, Society for Computational Economics.
    26. Li, Y. & Donkers, A.C.D. & Melenberg, B., 2006. "The Econometric Analysis of Microscopic Simulation Models," Discussion Paper 2006-99, Tilburg University, Center for Economic Research.
    27. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy.
    28. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    29. Westerhoff, Frank H., 2004. "Multiasset Market Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 8(5), pages 596-616, November.
    30. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    31. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    32. Shimokawa, Tetsuya & Suzuki, Kyoko & Misawa, Tadanobu, 2007. "An agent-based approach to financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 207-225.
    33. BRATIAN Vasile & BUCUR Amelia, 2017. "The Development And The Current Status Of The Capital Market Hypotheses: A Few Benchmarks," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(1), pages 22-28, April.
    34. Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2016. "Understanding Financial Market States Using an Artificial Double Auction Market," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    35. Jiang, Jiaqi & Gu, Rongbao, 2016. "Using Rényi parameter to improve the predictive power of singular value decomposition entropy on stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 254-264.
    36. Gerasymchuk, S. & Pavlov, O.V., 2010. "Asset Price Dynamics with Local Interactions under Heterogeneous Beliefs," CeNDEF Working Papers 10-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    37. Belaire-Franch, Jorge, 2004. "Testing for non-linearity in an artificial financial market: a recurrence quantification approach," Journal of Economic Behavior & Organization, Elsevier, vol. 54(4), pages 483-494, August.
    38. Hayashi, Katsuhiko & Kaizoji, Taisei & Pichl, Lukáš, 2007. "Correlation patterns of NIKKEI index constituents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 16-21.
    39. Andreas Krause, 2000. "Microstructure Effects on Daily Return Volatility in Financial Markets," Papers cond-mat/0011295, arXiv.org.
    40. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    41. Pichl, Lukáš & Kaizoji, Taisei & Yamano, Takuya, 2007. "Stylized facts in internal rates of return on stock index and its derivative transactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 219-227.
    42. Catherine Kyrtsou & Michel Terraza, 2008. "Seasonal Mackey-Glass-GARCH process and short-term dynamics," Discussion Paper Series 2008_09, Department of Economics, University of Macedonia, revised Sep 2008.
    43. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.
    44. Sergiy Gerasymchuk, 2008. "Asset return and wealth dynamics with reference dependent preferences and heterogeneous beliefs," Working Papers 160, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    45. Hwang, Keunho & Kang, Jangkoo & Ryu, Doojin, 2010. "Phase-transition behavior in the emerging market: Evidence from the KOSPI200 futures market," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 35-46, January.
    46. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy.
    47. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    48. Georges, Christophre, 2006. "Learning with misspecification in an artificial currency market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(1), pages 70-84, May.
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    1. Lux, Thomas, 2003. "The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting," Economics Working Papers 2003-13, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Trincado, Estrella & Vindel, José María, 2015. "An application of econophysics to the history of economic thought: The analysis of texts from the frequency of appearance of key words," Economics Discussion Papers 2015-51, Kiel Institute for the World Economy.
    3. Bacry, Emmanuel & Kozhemyak, Alexey & Muzy, Jean-François, 2006. "Are asset return tail estimations related to volatility long-range correlations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 119-126.
    4. Matthieu Wyart & Jean-Philippe Bouchaud, 2003. "Self-referential behaviour, overreaction and conventions in financial markets," Science & Finance (CFM) working paper archive 500020, Science & Finance, Capital Fund Management.
    5. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    6. A-Young Park & Gabjin Oh, 2023. "The contagion effect of heterogeneous investor groups," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-20, October.
    7. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    8. T. Di Matteo & T. Aste & Michel M. Dacorogna, 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Econometrics 0503004, University Library of Munich, Germany.
    9. Marcus Cordi & Damien Challet & Serge Kassibrakis, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Post-Print hal-01966419, HAL.
    10. Lisa Borland & Jean-Philippe Bouchaud, 2005. "On a multi-timescale statistical feedback model for volatility fluctuations," Science & Finance (CFM) working paper archive 500059, Science & Finance, Capital Fund Management.
    11. T. Di Matteo & T. Aste & M. M. Dacorogna, 2003. "Using the Scaling Analysis to Characterize Financial Markets," Papers cond-mat/0302434, arXiv.org.
    12. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy.
    13. Kartono, Agus & Febriyanti, Marina & Wahyudi, Setyanto Tri & Irmansyah,, 2020. "Predicting foreign currency exchange rates using the numerical solution of the incompressible Navier–Stokes equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    14. Wyart, Matthieu & Bouchaud, Jean-Philippe, 2007. "Self-referential behaviour, overreaction and conventions in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 63(1), pages 1-24, May.
    15. Miśkiewicz, Janusz, 2012. "Economy with the time delay of information flow—The stock market case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1388-1394.
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    17. Lisa Borland & Jean-Philippe Bouchaud & Jean-Francois Muzy & Gilles Zumbach, 2005. "The Dynamics of Financial Markets -- Mandelbrot's multifractal cascades, and beyond," Science & Finance (CFM) working paper archive 500061, Science & Finance, Capital Fund Management.
    18. L. Borland & J. -Ph. Bouchaud, 2005. "On a multi-timescale statistical feedback model for volatility fluctuations," Papers physics/0507073, arXiv.org.

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    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
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    3. Philip A. Stork & Milan Vidojevic & Remco C. J. Zwinkels, 2021. "Behavioral heterogeneity in return expectations across equity style portfolios," International Review of Finance, International Review of Finance Ltd., vol. 21(4), pages 1225-1250, December.
    4. Gunduz Caginalp & Vladimira Ilieva, 2006. "The dynamics of trader motivations in asset bubbles," Labsi Experimental Economics Laboratory University of Siena 008, University of Siena.
    5. Samanidou, Egle & Zschischang, Elmar & Stauffer, Dietrich & Lux, Thomas, 2006. "Microscopic models of financial markets," Economics Working Papers 2006-15, Christian-Albrechts-University of Kiel, Department of Economics.
    6. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    7. Paul De Grauwe & Marianna Grimaldi, 2003. "Intervention in the Foreign Exchange Market in a Model with Noise Traders," Working Papers 162003, Hong Kong Institute for Monetary Research.
    8. Rachida Hennani, 2015. "Can the Lasota(1977)’s model compete with the Mackey-Glass(1977)’s model in nonlinear modelling of financial time series?," Working Papers 15-09, LAMETA, Universtiy of Montpellier, revised Jun 2015.
    9. Wang, Wenzhao, 2018. "Investor sentiment and the mean-variance relationship: European evidence," Research in International Business and Finance, Elsevier, vol. 46(C), pages 227-239.
    10. Choijil, Enkhbayar & Méndez, Christian Espinosa & Wong, Wing-Keung & Vieito, João Paulo & Batmunkh, Munkh-Ulzii, 2022. "Thirty years of herd behavior in financial markets: A bibliometric analysis," Research in International Business and Finance, Elsevier, vol. 59(C).
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    62. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2005. "Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Economics Working Papers 2005-14, Christian-Albrechts-University of Kiel, Department of Economics.
    63. Henkel, Christof, 2017. "From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 447-458.
    64. Mignot, Sarah & Westerhoff, Frank, 2025. "Contagious popular stories, stock market participation, and boom–bust cycles," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 234(C), pages 459-471.
    65. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    66. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    67. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    68. Westerhoff, Frank H., 2003. "Expectations driven distortions in the foreign exchange market," Journal of Economic Behavior & Organization, Elsevier, vol. 51(3), pages 389-412, July.
    69. Carl Chiarella & Xue-Zhong He & Min Zheng, 2013. "Heterogeneous expectations and exchange rate dynamics," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 392-419, May.
    70. Jörn Dermietzel, 2008. "The Heterogeneous Agents Approach to Financial Markets – Development and Milestones," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 19, pages 443-464, Springer.
    71. Cars Hommes, 2005. "Heterogeneous Agent Models: Two Simple Case Studies," Tinbergen Institute Discussion Papers 05-055/1, Tinbergen Institute.
    72. Carl Chiarella & Xue-Zhong He & Duo Wang, 2004. "A Behavioural Asset Pricing Model with a Time-Varying Second Moment," Research Paper Series 141, Quantitative Finance Research Centre, University of Technology, Sydney.
    73. Dieci, Roberto & Westerhoff, Frank, 2010. "Heterogeneous speculators, endogenous fluctuations and interacting markets: A model of stock prices and exchange rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 743-764, April.
    74. Po-Keng Cheng, 2022. "Transitions among states behind interactive agent model," Computational and Mathematical Organization Theory, Springer, vol. 28(1), pages 27-51, March.
    75. Xue-Zhong He, 2003. "Asset Pricing, Volatility and Market Behaviour: A Market Fraction Approach," Research Paper Series 95, Quantitative Finance Research Centre, University of Technology, Sydney.
    76. Xue-Zhong He & Youwei Li, 2008. "Heterogeneity, convergence, and autocorrelations," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 59-79.
    77. Ghonghadze, Jaba & Lux, Thomas, 2009. "Modeling the dynamics of EU economic sentiment indicators: an interaction-based approach," Kiel Working Papers 1487, Kiel Institute for the World Economy.
    78. J.-H. Steffi Yang & Satchell, S.E., 2002. "The Impact of Technical Analysis on Asset Price Dynamics," Cambridge Working Papers in Economics 0219, Faculty of Economics, University of Cambridge.
    79. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    80. Thomas Lux & Jaba Ghonghadze, 2011. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Post-Print hal-00711445, HAL.
    81. Hommes, C.H., 2005. "Heterogeneous Agents Models: two simple examples, forthcoming In: Lines, M. (ed.) Nonlinear Dynamical Systems in Economics, CISM Courses and Lectures, Springer, 2005, pp.131-164," CeNDEF Working Papers 05-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    82. Xue-Zhong He & Carl Chiarella, 1999. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset-Pricing Model," Computing in Economics and Finance 1999 223, Society for Computational Economics.
    83. Palczewski, Jan & Schenk-Hoppé, Klaus Reiner, 2010. "From discrete to continuous time evolutionary finance models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(5), pages 913-931, May.
    84. Roberto Dieci & Frank Westerhoff, 2012. "A simple model of a speculative housing market," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 303-329, April.
    85. Li, Y., 2006. "On microscopic simulation models of financial markets," Other publications TiSEM ec2f852d-4a7f-47b1-99b8-4, Tilburg University, School of Economics and Management.
    86. 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, May.
    87. Hommes, C.H., 2005. "Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006, pp.1109-1186," CeNDEF Working Papers 05-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    88. Xue-Zhong He & Youwei Li, 2005. "Heterogeneity, Profitability and Autocorrelations," Research Paper Series 147, Quantitative Finance Research Centre, University of Technology, Sydney.
    89. J. Doyne Farmer & John Geanakoplos, 2008. "The Virtues and Vices of Equilibrium and the Future of Financial Economics," Cowles Foundation Discussion Papers 1647, Cowles Foundation for Research in Economics, Yale University.
    90. Tai, Chung-Ching & Chen, Shu-Heng & Yang, Lee-Xieng, 2018. "Cognitive ability and earnings performance: Evidence from double auction market experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 409-440.
    91. Courtois, Pierre & Tazdaït, Tarik, 2007. "Games of influence in climate change negotiations: Modelling interactions," Ecological Modelling, Elsevier, vol. 204(3), pages 301-314.
    92. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.
    93. Gabaix, Xavier & Gopikrishnan, Parameswaran & Plerou, Vasiliki & Eugene Stanley, H., 2008. "Quantifying and understanding the economics of large financial movements," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 303-319, January.

  21. Thomas Lux, 1996. "Long-term stochastic dependence in financial prices: evidence from the German stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 3(11), pages 701-706.

    Cited by:

    1. Lux, Thomas, 2003. "The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting," Economics Working Papers 2003-13, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Dominique, C-Rene & Rivera-Solis, Luis Eduardo, 2012. "Short-term Dependence in Time Series as an Index of Complexity: Example from the S&P-500 Index," MPRA Paper 41408, University Library of Munich, Germany.
    3. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy.
    4. Mensi, Walid & Tiwari, Aviral Kumar & Al-Yahyaee, Khamis Hamed, 2019. "An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 168-177.
    5. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
    6. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    7. Alexander Subbotin, 2008. "A multi-horizon scale for volatility," Post-Print halshs-00261514, HAL.
    8. Sanjay Rajagopal & Patrick Hays, 2012. "Return Persistence in the Indian Real Estate Market," International Real Estate Review, Global Social Science Institute, vol. 15(3), pages 283-305.
    9. Siew Ann Cheong, 2013. "Econophysics: An Experimental Course for Advanced Undergraduates in the Nanyang Technological University," IIM Kozhikode Society & Management Review, , vol. 2(2), pages 79-99, July.
    10. Mejra Festic & Alenka Kavkler & Silvo Dajcman, 2012. "Long memory in the Croatian and Hungarian stock market returns," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 30(1), pages 115-139.
    11. Gomes, Luís M. P. & Soares, Vasco J. S. & Gama, Sílvio M. A. & Matos, José A. O., 2018. "Long-term memory in Euronext stock indexes returns: an econophysics approach," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(4), pages 862-881, August.
    12. Dominique, C-René & Rivera-Solis, Luis Eduardo, 2011. "Mixed fractional Brownian motion, short and long-term Dependence and economic conditions: the case of the S&P-500 Index," MPRA Paper 34860, University Library of Munich, Germany.
    13. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    14. Rivera-Castro, Miguel A. & Miranda, José G.V. & Cajueiro, Daniel O. & Andrade, Roberto F.S., 2012. "Detecting switching points using asymmetric detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 170-179.
    15. Ramashanti Naik & Y. V. Reddy, 2025. "Examination of Long Memory in Indian Stock Market: A Sectoral Juxtaposition," FIIB Business Review, , vol. 14(2), pages 184-202, March.
    16. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    17. Dominique, C-Rene & Rivera-Solis, Luis Eduardo, 2012. "The dynamics of market share’s growth and competition in quadratic mappings," MPRA Paper 43652, University Library of Munich, Germany.
    18. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.
    19. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.
    20. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    21. Benjamin Kohler & Anton J. Heckens & Thomas Guhr, 2026. "Extreme Value Analysis for Finite, Multivariate and Correlated Systems with Finance as an Example," Papers 2603.05260, arXiv.org.
    22. Carl Chiarella & Xue-Zhong He, 2001. "Asset Price and Wealth Dynamics Under Heterogeneous Expectations," Research Paper Series 56, Quantitative Finance Research Centre, University of Technology, Sydney.
    23. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
    24. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy.
    25. Rafal Weron, 2001. "Measuring long-range dependence in electricity prices," Papers cond-mat/0103621, arXiv.org.
    26. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.

  22. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.

    Cited by:

    1. Baumann, Michael Heinrich & Baumann, Michaela & Erler, Alexander, 2019. "Limitations of stabilizing effects of fundamentalists: Facing positive feedback traders," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 13, pages 1-26.
    2. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    3. Hott, C., 2011. "Lending behavior and real estate prices," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2429-2442, September.
    4. Sean Wever & David Aadland, 2012. "Herd behaviour and underdogs in the NFL," Applied Economics Letters, Taylor & Francis Journals, vol. 19(1), pages 93-97, January.
    5. Frédéric Hanin, 2003. "La place du Treatise on Money dans l’oeuvre de Keynes : une théorie de l’instabilité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 79(1), pages 71-86.
    6. Samanidou, Egle & Zschischang, Elmar & Stauffer, Dietrich & Lux, Thomas, 2006. "Microscopic models of financial markets," Economics Working Papers 2006-15, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Jozef Barunik & Jiri Kukacka, 2015. "Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 959-973, June.
    9. Marta Gómez-Puig & Simón Sosvilla-Rivero & María del Carmen Ramos-Herrera, 2014. "An update on EMU sovereign yield spread drivers in time of crisis: A panel data analysis," Working Papers 2014-04, Universitat de Barcelona, UB Riskcenter.
    10. He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
    11. Chong, Terence Tai-Leung & Liu, Xiaojin & Zhu, Chenqi, 2016. "What Explains Herd Behavior in the Chinese Stock Market?," MPRA Paper 72100, University Library of Munich, Germany.
    12. Choijil, Enkhbayar & Méndez, Christian Espinosa & Wong, Wing-Keung & Vieito, João Paulo & Batmunkh, Munkh-Ulzii, 2022. "Thirty years of herd behavior in financial markets: A bibliometric analysis," Research in International Business and Finance, Elsevier, vol. 59(C).
    13. Dávila-Fernández, Marwil J. & Sordi, Serena, 2020. "Attitudes towards climate policies in a macrodynamic model of the economy," Ecological Economics, Elsevier, vol. 169(C).
    14. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    15. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    16. Syed Hassan & Sarosh Shabi & Taufiq Choudhry, 2018. "US Economic Uncertainty, EU Business Cycles and the Global Financial Crisis," Working Papers 2018-05, Swansea University, School of Management.
    17. Schütz, Gunter M. & de Almeida Prado, Fernando Pigeard & Harris, Rosemary J. & Belitsky, Vladimir, 2009. "Short-time behaviour of demand and price viewed through an exactly solvable model for heterogeneous interacting market agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4126-4144.
    18. Paolo Pellizzari & Frank Westerhoff, 2009. "Some effects of transaction taxes under different microstructures," Post-Print hal-00727590, HAL.
    19. Kampouridis, Michael & Chen, Shu-Heng & Tsang, Edward, 2012. "Market fraction hypothesis: A proposed test," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 41-54.
    20. Baskozos, Giorgos & Galanis, Giorgos & Di Guilmi, Corrado, 2020. "Social distancing and contagion in a discrete choice model of COVID-19," CRETA Online Discussion Paper Series 57, Centre for Research in Economic Theory and its Applications CRETA.
    21. Zhentao Shi & Huanhuan Zheng, 2018. "Structural Estimation of Behavioral Heterogeneity," Papers 1802.03735, arXiv.org, revised Jun 2018.
    22. David Zimmer, 2015. "Asymmetric dependence in house prices: evidence from USA and international data," Empirical Economics, Springer, vol. 49(1), pages 161-183, August.
    23. Jaroslaw Klepacki, 2025. "The Fear of Missing Out (FOMO) Effect in Cryptocurrency Trading: Analyses of Mass Behavior and Its Consequences in the Era of Unpredictable Extreme Phenomena," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 938-949.
    24. Dumitriu, Ramona & Stefanescu, Răzvan, 2020. "Iluzii financiare, Partea întâi [Financial Illusions, Part 1]," MPRA Paper 101201, University Library of Munich, Germany, revised 17 Jun 2020.
    25. Hiroki Murakami, 2019. "A note on the “unique” business cycle in the Keynesian theory," Metroeconomica, Wiley Blackwell, vol. 70(3), pages 384-404, July.
    26. Xue-Zhong He & Huanhuan Zheng, 2016. "Trading Heterogeneity Under Information Uncertainty," Research Paper Series 373, Quantitative Finance Research Centre, University of Technology, Sydney.
    27. Ron Bird & Lorenzo Casavecchia & Paolo Pellizzari & Paul Woolley, 2011. "The impact on the pricing process of costly active management and performance chasing clients," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(1), pages 61-82, May.
    28. Damian Smug & Peter Ashwin & Didier Sornette, 2018. "Predicting financial market crashes using ghost singularities," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    29. Woodward, George & Marisetty, Vijaya B., 2005. "Introducing non-linear dynamics to the two-regime market model: Evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(4-5), pages 559-581, September.
    30. Alessandro Spelta & Nicolò Pecora & Andrea Flori & Paolo Giudici, 2023. "The impact of the SARS-CoV-2 pandemic on financial markets: a seismologic approach," Annals of Operations Research, Springer, vol. 330(1), pages 639-664, November.
    31. Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    32. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    33. Filippo Gusella & Eugenio Vicario, 2025. "Generative Agents and Expectations: Do LLMs Align with Heterogeneous Agent Models?," Papers 2511.08604, arXiv.org.
    34. Carl Chiarella & Xue-Zhong He & Duo Wang, 2004. "Statistical Properties of a Heterogeneous Asset Price Model with Time-Varying Second Moment," Research Paper Series 142, Quantitative Finance Research Centre, University of Technology, Sydney.
    35. Noemi Schmitt & Frank Westerhoff, 2017. "Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1041-1070, November.
    36. Di Guilmi, Corrado & He, Xue-Zhong & Li, Kai, 2014. "Herding, trend chasing and market volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 349-373.
    37. Schmitt, Noemi & Westerhoff, Frank, 2017. "On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations," BERG Working Paper Series 119, Bamberg University, Bamberg Economic Research Group.
    38. Goeree, Jacob K. & Hommes, Cars H., 2000. "Heterogeneous beliefs and the non-linear cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 761-798, June.
    39. Michele Berardi, 2016. "Endogenous time-varying risk aversion and asset returns," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 581-601, July.
    40. Thomas Lux, 2009. "Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey," Post-Print hal-00720175, HAL.
    41. Misha Perepelitsa & Ilya Timofeyev, 2022. "Self-sustained price bubbles driven by digital currency innovations and adaptive market behavior," SN Business & Economics, Springer, vol. 2(3), pages 1-15, March.
    42. Christian Hott, 2009. "Banks and Real Estate Prices," Working Papers 2009-08, Swiss National Bank.
    43. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    44. Dieci, Roberto & Gardini, Laura & Westerhoff, Frank, 2022. "On the destabilizing nature of capital gains taxes," International Review of Financial Analysis, Elsevier, vol. 83(C).
    45. Torsten Trimborn, 2018. "A Macroscopic Portfolio Model: From Rational Agents to Bounded Rationality," Papers 1805.11036, arXiv.org, revised Oct 2018.
    46. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    47. Mesly, Olivier & Chkir, Imed & Racicot, François-Éric, 2019. "Predatory cells and puzzling financial crises: Are toxic products good for the financial markets?," Economic Modelling, Elsevier, vol. 78(C), pages 11-31.
    48. Dieci, Roberto & Schmitt, Noemi & Westerhoff, Frank H., 2018. "Interactions between stock, bond and housing markets," BERG Working Paper Series 133, Bamberg University, Bamberg Economic Research Group.
    49. Xue-Zhong (Tony) He & Carl Chiarella, 2001. "Asset Price and Wealth Dynamics under Heterogeneous Expectations," CeNDEF Workshop Papers, January 2001 5A.2, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    50. Serena Sordi & Marwil J. Dávila-Fernández, 2020. "Investment behaviour and “bull & bear” dynamics: modelling real and stock market interactions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 867-897, October.
    51. Cars H. Hommes, 2001. "Financial Markets as Nonlinear Adaptive Evolutionary Systems," Tinbergen Institute Discussion Papers 01-014/1, Tinbergen Institute.
    52. Roberto Dieci & Ilaria Foroni & Laura Gardini & Xue-Zhong He, 2005. "Market Mood, Adaptive Beliefs and Asset Price Dynamics," Research Paper Series 162, Quantitative Finance Research Centre, University of Technology, Sydney.
    53. Weihong Huang & Yu Zhang, 2017. "Endogenous Fundamental and Stock Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 629-653, December.
    54. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    55. Liyun Zhou & Jiamin Zheng, 2026. "Stock-level sentiment contagion and stock price bubbles," Journal of Asset Management, Palgrave Macmillan, vol. 27(1), pages 1-21, March.
    56. Fredj Jawadi & Wael Louhichi & Abdoulkarim Idi Cheffou & Hachmi Ben Ameur, 2019. "Modeling time-varying beta in a sustainable stock market with a three-regime threshold GARCH model," Annals of Operations Research, Springer, vol. 281(1), pages 275-295, October.
    57. Yu, Tongkui & Li, Honggang, 2008. "Dynamic Regimes of a Multi-agent Stock Market Model," MPRA Paper 14339, University Library of Munich, Germany.
    58. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    59. Huang, Weihong & Zheng, Huanhuan & Chia, Wai-Mun, 2010. "Financial crises and interacting heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1105-1122, June.
    60. Marwil J. Dávila-Fernández & Serena Sordi, 2019. "From open economies to attitudes towards change. Growth and institutions in Latin America and Asia," Department of Economics University of Siena 809, Department of Economics, University of Siena.
    61. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    62. Yide Wang & Chao Yu & Xujie Zhao, 2023. "Does herding effect help forecast market volatility?—Evidence from the Chinese stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1275-1290, August.
    63. Andrey Kudryavtsev, 2019. "Short-Term Herding Effect On Market Index Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 1-16, March.
    64. Jørgen Vitting Andersen & Ioannis Vrontos & Petros Dellaportas & Serge Galam, 2014. "Communication impacting financial markets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01215750, HAL.
    65. Michele Catalano & Corrado Di Guilmi, 2016. "Uncertainty, Rationality and Complexity in a Multi Sectoral Dynamic Model: The Dynamic Stochastic Generalized Aggregation Approach," CAMA Working Papers 2016-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    66. Dieci, Roberto & Schmitt, Noemi & Westerhoff, Frank H., 2022. "Boom-bust cycles and asset market participation waves: Momentum, value, risk and herding," BERG Working Paper Series 177, Bamberg University, Bamberg Economic Research Group.
    67. Galimberti, Jaqueson & Suhadolnik, Nicolas & Da Silva, Sergio, 2016. "Cowboying Stock Market Herds with Robot Traders," MPRA Paper 71758, University Library of Munich, Germany.
    68. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    69. Yosra Mefteh Rekik & Younes Boujelbene, 2015. "Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 33-43, April.
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    Cited by:

    1. Peter Flaschel, 2001. "Viability and Corridor Stability in Keynesian Supply-Driven Growth," Metroeconomica, Wiley Blackwell, vol. 52(1), pages 26-48, February.
    2. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

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