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Marco Bee

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. Marco Bee & Julien Hambuckers & Luca Trapin, 2018. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," DEM Working Papers 2018/08, Department of Economics and Management.

    Cited by:

    1. Marco Bee & Julien Hambuckers & Luca Trapin, 2019. "An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution," DEM Working Papers 2019/11, Department of Economics and Management.
    2. Marco Bee, 2022. "The truncated g-and-h distribution: estimation and application to loss modeling," Computational Statistics, Springer, vol. 37(4), pages 1771-1794, September.

  2. Marco Bee & Maria Michela Dickson & Flavio Santi, 2017. "Likelihood-based Risk Estimation for Variance-Gamma Models," DEM Working Papers 2017/03, Department of Economics and Management.

    Cited by:

    1. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Naderi, Mehrdad & Hashemi, Farzane & Bekker, Andriette & Jamalizadeh, Ahad, 2020. "Modeling right-skewed financial data streams: A likelihood inference based on the generalized Birnbaum–Saunders mixture model," Applied Mathematics and Computation, Elsevier, vol. 376(C).

  3. Marco Bee & Giulio Gatti, 2015. "An improved pairs trading strategy based on switching regime volatility," DEM Discussion Papers 2015/13, Department of Economics and Management.

    Cited by:

    1. Jeff Stephenson & Bruce Vanstone & Tobias Hahn, 2021. "A Unifying Model for Statistical Arbitrage: Model Assumptions and Empirical Failure," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 943-964, December.
    2. Endres, Sylvia & Stübinger, Johannes, 2018. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," FAU Discussion Papers in Economics 07/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

  4. Marco Bee & Stefano Schiavo, 2015. "Powerless : gains from trade when firm productivity is not Pareto distributed," Documents de Travail de l'OFCE 2015-19, Observatoire Francais des Conjonctures Economiques (OFCE).

    Cited by:

    1. Bawa, Siraj, 2017. "Corporate Taxation in the Open Economy without Pareto," MPRA Paper 80857, University Library of Munich, Germany, revised Aug 2017.
    2. Ruben Dewitte & Michel Dumont & Glenn Rayp & Peter Willemé, 2022. "Unobserved heterogeneity in the productivity distribution and gains from trade," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(3), pages 1566-1597, August.
    3. Letizia Montinari & Massimo Riccaboni & Stefano Schiavo, 2021. "Innovation, trade and multi‐product firms," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(1), pages 311-337, February.
    4. Anders Rosenstand Laugesen, 2015. "Asymmetric Monotone Comparative Statics for the Industry Compositions," Economics Working Papers 2015-22, Department of Economics and Business Economics, Aarhus University.
    5. Neary, Peter & Mrázová, Monika, 2017. "Sales and Markup Dispersion: Theory and Empirics," CEPR Discussion Papers 12044, C.E.P.R. Discussion Papers.
    6. Peter Neary & Monika Mrázová, 2019. "Io For Export(S)," Economics Series Working Papers 868, University of Oxford, Department of Economics.
    7. Peter Neary & Monika MrázováMathieu Parenti, 2015. "Technology, Demand, And The Size Distribution Of Firms," Economics Series Working Papers 774, University of Oxford, Department of Economics.
    8. Ruben Dewitte & Bruno Merlevede & Glenn Rayp, 2024. "Gains from trade: Demand, supply, and idiosyncratic shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 870-886, August.
    9. di Mauro, Filippo & Demian, Calin-Vlad, 2015. "The exchange rate, asymmetric shocks and asymmetric distributions," Working Paper Series 1801, European Central Bank.
    10. Polanec Sašo & Smith Paul A. & Bavdaž Mojca, 2022. "Determination of the Threshold in Cutoff Sampling Using Response Burden with an Application to Intrastat," Journal of Official Statistics, Sciendo, vol. 38(4), pages 1205-1234, December.
    11. Dewitte, Ruben, 2020. "From Heavy-Tailed Micro to Macro: on the characterization of firm-level heterogeneity and its aggregation properties," MPRA Paper 103170, University Library of Munich, Germany.

  5. Marco Bee & Giuseppe Espa & Diego Giuliani & Flavio Santi, 2015. "A Cross-Entropy approach to the estimation of Generalised Linear Multilevel Models," DEM Working Papers 2015/04, Department of Economics and Management.

    Cited by:

    1. Marco Bee & Maria Michela Dickson & Diego Giuliani & Davide Piacentino & Flavio Santi & Emanuele Taufer, 2016. "La sopravvivenza immediata delle start-up italiane del settore manifatturiero sanitario: un?analisi multilevel," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2016(3), pages 49-59.

  6. MArco Bee & Massimo Riccaboni & Stefano Schiavo, 2014. "Where Gibrat meets Zipf: Scale and Scope of French Firms," DEM Discussion Papers 2014/03, Department of Economics and Management.

    Cited by:

    1. Mercedes Campi & Marco Dueñas & Le Li & Huabin Wu, 2021. "Diversification, economies of scope, and exports growth of Chinese firms," Working Papers 65, Red Nacional de Investigadores en Economía (RedNIE).
    2. Shouji Fujimoto & Takayuki Mizuno & Atushi Ishikawa, 2022. "Interpolation of non-random missing values in financial statements’ big data using CatBoost," Journal of Computational Social Science, Springer, vol. 5(2), pages 1281-1301, November.
    3. Marco Bee & Stefano Schiavo, 2015. "Powerless : gains from trade when firm productivity is not Pareto distributed," Documents de Travail de l'OFCE 2015-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    4. Lyócsa, Štefan & Výrost, Tomáš, 2018. "Scale-free distribution of firm-size distribution in emerging economies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 501-505.
    5. Alessia Lo Turco & Daniela Maggioni & Federico Trionfetti, 2024. "Immigration and the skill premium," AMSE Working Papers 2414, Aix-Marseille School of Economics, France.
    6. Letizia Montinari & Massimo Riccaboni & Stefano Schiavo, 2021. "Innovation, trade and multi‐product firms," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(1), pages 311-337, February.
    7. Flavio Bazzana & Anna Zadorozhnaya & Roberto Gabriele, 2014. "The role of covenants in bond issue and investment policy. The case of Russian companies," DEM Discussion Papers 2014/05, Department of Economics and Management.
    8. Anna Maria Fiori, 2020. "On firm size distribution: statistical models, mechanisms, and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 447-482, September.
    9. Mariasole Bann√ö & Diego Giuliani & Enrico Zaninotto, 2014. "Going abroad on regional shoulders: The role of spillovers on the composition of regional exports," DEM Discussion Papers 2014/06, Department of Economics and Management.
    10. Arturo Ramos & Till Massing & Atushi Ishikawa & Shouji Fujimoto & Takayuki Mizuno, 2023. "Composite distributions in the social sciences: A comparative empirical study of firms' sales distribution for France, Germany, Italy, Japan, South Korea, and Spain," Papers 2301.09438, arXiv.org.
    11. Cebreros Alfonso, 2019. "The Micro-economics of Export Supply: Firm-Level Evidence from Mexico," Working Papers 2019-02, Banco de México.
    12. Massimo, Riccaboni & Jakub, Growiec & Fabio, Pammolli, 2011. "Innovation and Corporate Dynamics: A Theoretical Framework," MPRA Paper 30046, University Library of Munich, Germany.
    13. Montebruno, Piero & Bennett, Robert J. & van Lieshout, Carry & Smith, Harry, 2019. "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 858-875.
    14. Gala, Kaushik & Schwab, Andreas & Mueller, Brandon A., 2024. "Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms," Journal of Business Venturing, Elsevier, vol. 39(1).
    15. Musa, Hussam & Krištofík, Peter & Medzihorský, Juraj & Klieštik, Tomáš, 2024. "The development of firm size distribution – Evidence from four Central European countries," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 98-110.
    16. Jiangteng Zhou, 2023. "The impacts of highways on firm size distribution: Evidence from China," Growth and Change, Wiley Blackwell, vol. 54(2), pages 482-506, June.
    17. Michael Irlacher, 2022. "Multi-Product Firms in International Economics," CESifo Working Paper Series 9589, CESifo.
    18. Asif, Muhammad & Hussain, Zawar & Asghar, Zahid & Hussain, Muhammad Irfan & Raftab, Mariya & Shah, Said Farooq & Khan, Akbar Ali, 2021. "A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    19. Mariasole Banno' & Elisa Pozza & Sandro Trento, 2015. "La famiglia fa male all'internazionalizzazione dell'impresa?," DEM Discussion Papers 2015/03, Department of Economics and Management.
    20. Rashidisabet, Homa & Ajilore, Olusola & Leow, Alex & Demos, Alexander P., 2022. "Revisiting power-law estimation with applications to real-world human typing dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    21. Marco Bee, 2024. "On discriminating between lognormal and Pareto tail: an unsupervised mixture-based approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 251-269, June.
    22. Giuseppe Arbia & Marco Bee & Giuseppe Espa & Flavio Santi, 2014. "Fitting Spatial Econometric Models through the Unilateral Approximation," DEM Discussion Papers 2014/08, Department of Economics and Management.
    23. Ricardo González-López & Javier B. Gómez & Amalio F. Pacheco, 2020. "A Minimal Agent-Based Model For The Size-Frequency Distribution Of Firms," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(01), pages 1-27, March.
    24. Ren'e Aid & Matteo Basei & Giorgio Ferrari, 2023. "A Stationary Mean-Field Equilibrium Model of Irreversible Investment in a Two-Regime Economy," Papers 2305.00541, arXiv.org.

  7. Marco Bee & Diego GIuliani & Giuseppe Espa, 2013. "Approximate Maximum Likelihood Estimation of the Autologistic Model," DEM Discussion Papers 2013/12, Department of Economics and Management.

    Cited by:

    1. Bee, Marco & Benedetti, Roberto & Espa, Giuseppe, 2017. "Approximate maximum likelihood estimation of the Bingham distribution," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 84-96.
    2. Marco Bee & Roberto Benedetti & Giuseppe Espa, 2015. "Approximate likelihood inference for the Bingham distribution," DEM Working Papers 2015/02, Department of Economics and Management.

  8. Marco Bee, 2012. "Statistical analysis of the Lognormal-Pareto distribution using Probability Weighted Moments and Maximum Likelihood," Department of Economics Working Papers 1208, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Rafael González-Val & Arturo Ramos & Fernando Sanz-Gracia, 2014. "A new framework for US city size distribution: Empirical evidence and theory," ERSA conference papers ersa14p633, European Regional Science Association.

  9. Marco Bee & Massimo Riccaboni & Stefano Schiavo, 2012. "A Trick of the (Pareto) Tail," Department of Economics Working Papers 1206, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Massimo Riccaboni & Stefano Schiavo, 2013. "Stochastic Trade Networks," Working Papers 1/2013, IMT School for Advanced Studies Lucca, revised Jan 2013.

  10. Marco Bee & Massimo Riccaboni & Stefano Schiavo, 2011. "Pareto versus lognormal: a maximum entropy test," Department of Economics Working Papers 1102, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Letizia Montinari & Massimo Riccaboni & Stefano Schiavo, 2021. "Innovation, trade and multi‐product firms," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(1), pages 311-337, February.
    2. Marco Bee, 2020. "On discriminating between lognormal and Pareto tail: A mixture-based approach," DEM Working Papers 2020/9, Department of Economics and Management.
    3. Giorgio Fazio & Marco Modica, 2015. "Pareto Or Log-Normal? Best Fit And Truncation In The Distribution Of All Cities," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 736-756, November.
    4. Arturo Ramos & Till Massing & Atushi Ishikawa & Shouji Fujimoto & Takayuki Mizuno, 2023. "Composite distributions in the social sciences: A comparative empirical study of firms' sales distribution for France, Germany, Italy, Japan, South Korea, and Spain," Papers 2301.09438, arXiv.org.
    5. Cebreros Alfonso, 2019. "The Micro-economics of Export Supply: Firm-Level Evidence from Mexico," Working Papers 2019-02, Banco de México.
    6. Massimo, Riccaboni & Jakub, Growiec & Fabio, Pammolli, 2011. "Innovation and Corporate Dynamics: A Theoretical Framework," MPRA Paper 30046, University Library of Munich, Germany.
    7. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2017. "Where Gibrat meets Zipf: Scale and scope of French firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 265-275.
    8. Asif, Muhammad & Hussain, Zawar & Asghar, Zahid & Hussain, Muhammad Irfan & Raftab, Mariya & Shah, Said Farooq & Khan, Akbar Ali, 2021. "A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    9. Campolieti, Michele & Ramos, Arturo, 2021. "The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    10. Josic Hrvoje & Bašić Maja, 2018. "Reconsidering Zipf’s law for regional development: The case of settlements and cities in Croatia," Miscellanea Geographica. Regional Studies on Development, Sciendo, vol. 22(1), pages 22-30, March.
    11. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2013. "The size distribution of US cities: Not Pareto, even in the tail," Economics Letters, Elsevier, vol. 120(2), pages 232-237.
    12. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    13. Giorgio Fazio & Marco Modica, 2012. "Pareto or log-normal? A recursive-truncation approach to the distribution of (all) cities," Working Papers 2012_10, Business School - Economics, University of Glasgow.

  11. Marco Bee & Fabrizio Miorelli, 2010. "Dynamic VaR models and the Peaks over Threshold method for market risk measurement: an empirical investigation during a financial crisis," Department of Economics Working Papers 1009, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    2. Axel A. Araneda, 2021. "Asset volatility forecasting:The optimal decay parameter in the EWMA model," Papers 2105.14382, arXiv.org.
    3. Nikola Radivojević & Nikola V. Ćurčić & Djurdjica Dj. Vukajlović, 2017. "Hull-White’s value at risk model: case study of Baltic equities market," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1023-1041, September.
    4. Song, Wenjuan & Sun, Lixin, 2014. "The Measurement of the Long-Term and Short-Term Risks of Chinese Listed Banks," MPRA Paper 70007, University Library of Munich, Germany, revised Jul 2014.
    5. Mesut BALLIBEY & Serpil T RKYILMAZ, 2014. "Value-at-Risk Analysis in the Presence of Asymmetry and Long Memory: The Case of Turkish Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 836-848.

  12. Emanuele Taufer & Nikolai Leonenko & Marco Bee, 2009. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," DISA Working Papers 0907, Department of Computer and Management Sciences, University of Trento, Italy, revised 02 Dec 2009.

    Cited by:

    1. Nikolai Leonenko & EStuart Petherick & Emanuele Taufer, 2012. "Multifractal Scaling for Risky Asset Modelling," DISA Working Papers 2012/07, Department of Computer and Management Sciences, University of Trento, Italy, revised Jul 2012.
    2. Szczepocki Piotr, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Statistics Poland, vol. 21(2), pages 173-187, June.
    3. Kotchoni, Rachidi, 2014. "The indirect continuous-GMM estimation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 464-488.
    4. Leonenko, Nikolai & Petherick, Stuart & Taufer, Emanuele, 2013. "Multifractal models via products of geometric OU-processes: Review and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 7-16.
    5. Stelzer Robert & Tosstorff Thomas & Wittlinger Marc, 2015. "Moment based estimation of supOU processes and a related stochastic volatility model," Statistics & Risk Modeling, De Gruyter, vol. 32(1), pages 1-24, April.
    6. Stojanović, Vladica S. & Popović, Biljana Č. & Milovanović, Gradimir V., 2016. "The Split-SV model," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 560-581.
    7. Bruno Ebner & Bernhard Klar & Simos G. Meintanis, 2018. "Fourier inference for stochastic volatility models with heavy-tailed innovations," Statistical Papers, Springer, vol. 59(3), pages 1043-1060, September.

  13. Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data," Department of Economics Working Papers 0801, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Raffaella Calabrese & Johan A. Elkink, 2012. "Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study," Working Papers 201215, Geary Institute, University College Dublin.

  14. Giuseppe Arbia & Marco Bee & Giuseppe Espa, 2007. "Aggregation of regional economic time series with different spatial correlation structures," Department of Economics Working Papers 0720, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB-Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Bee, Marco & Espa, Giuseppe & Giuliani, Diego, 2015. "Approximate maximum likelihood estimation of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 14-26.

  15. Marco Bee & Roberto Benedetti & Giuseppe Espa, 2007. "A framework for cut-off sampling in business survey design," Department of Economics Working Papers 0709, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Orietta Luzi & Gianni Seri & Viviana De Giorgi & Giampiero Siesto, 2013. "Estimating Business Statistics by integrating administrative and survey data: an experimental study on small and medium enterprises," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(2-3), pages 31-50.
    2. Sandars, Patrick & Eleni, Starida & Nega, Stamatina & Casado, Antonio & Buzzi, Maria Rosaria & Stacchini, Massimiliano & Švedas, Tomas & Goes, Wim & Bartmann, Martin & Ciesla, Norbert & Maitland-Smith, 2013. "Quality measures in non-random sampling: MFI interest rate statistics," Statistics Paper Series 3, European Central Bank.
    3. Raymond Chaudron & Krit Carlier, 2015. "The advantages of random sampling versus cutting-of-the-tail: the application of a stratified sample design for the collection of data on special financial institutions in the Netherlands," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.

  16. Marco Bee & Roberto Benedetti & Giuseppe Espa, 2007. "Spatial models for flood risk assessment," Department of Economics Working Papers 0710, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Solaiman Afroughi & Soghrat Faghihzadeh & Majid Jafari Khaledi & Mehdi Ghandehari Motlagh & Ebrahim Hajizadeh, 2011. "Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3--5-year-old children," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2763-2774, February.

  17. Marco Bee, 2005. "On maximum likelihood estimation of operational loss distributions," Department of Economics Working Papers 0503, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Pavel V. Shevchenko, 2010. "Implementing loss distribution approach for operational risk," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 277-307, May.
    2. Anna Chernobai & Krzysztof Burnecki & Svetlozar Rachev & Stefan Trueck & Rafal Weron, 2005. "Modeling catastrophe claims with left-truncated severity distributions (extended version)," HSC Research Reports HSC/05/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Xiaolin Luo & Pavel V. Shevchenko & John B. Donnelly, 2009. "Addressing the Impact of Data Truncation and Parameter Uncertainty on Operational Risk Estimates," Papers 0904.2910, arXiv.org.
    4. Pavel V. Shevchenko, 2009. "Implementing Loss Distribution Approach for Operational Risk," Papers 0904.1805, arXiv.org, revised Jul 2009.

  18. Marco Bee, 2002. "Un modello per l'incorporazione del rischio specifico nel VaR," Alea Tech Reports 013, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.

    Cited by:

    1. Flavio Bazzana & Francesca Debortoli, 2002. "Il rischio sistemico in finanza: una rassegna dei recenti contributi in letteratura," Alea Tech Reports 017, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.

  19. Marco Bee & Giuseppe Espa & Roberto Tamborini, 2002. "Firms� bankruptcy and turnover in a macroeconomy," Department of Economics Working Papers 0203, Department of Economics, University of Trento, Italia.

    Cited by:

    1. Flora Bellone & Patrick Musso & Lionel Nesta & Stefano Schiavo, 2008. "Financial Constraints and Firm Export Behavior," Department of Economics Working Papers 0816, Department of Economics, University of Trento, Italia.
    2. Gianluca Grimalda & Lorenzo Sacconi, 2004. "The Constitution of the Not-For-Profit Organisation: Reciprocal Conformity to Morality," Department of Economics Working Papers 0413, Department of Economics, University of Trento, Italia.
    3. Julie Dana & Christopher L. Gilbert, 2008. "Managing Agricultural Price Risk in Developing Countries," Department of Economics Working Papers 0819, Department of Economics, University of Trento, Italia.
    4. Lorenzo Sacconi & Stefano Moretti, 2004. "A Fuzzy Logic and Default Reasoning Model of Social Norm and Equilibrium Selection in Games under Unforeseen Contingencies," Department of Economics Working Papers 0412, Department of Economics, University of Trento, Italia.
    5. Luciano Andreozzi, 2008. "Property Rights and Investments: An Evolutionary Approach," Department of Economics Working Papers 0822, Department of Economics, University of Trento, Italia.
    6. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Andrea Mazzitelli, 2009. "Clusters of firms in space and time," Department of Economics Working Papers 0902, Department of Economics, University of Trento, Italia.
    7. Christopher L. Gilbert, 2008. "How to Understand High Food Prices," Department of Economics Working Papers 0823, Department of Economics, University of Trento, Italia.
    8. Elisabetta De Antoni, 2008. "Minsky�s Upward Instability: the Not-Too-Keynesian Optimism of a Financial Cassandra," Department of Economics Working Papers 0812, Department of Economics, University of Trento, Italia.
    9. Christopher L. Gilbert, 2008. "Commodity Speculation and Commodity Investment," Department of Economics Working Papers 0820, Department of Economics, University of Trento, Italia.
    10. Lorenzo Sacconi, 2004. "A Social Contract Account for CSR as Extended Model of Corporate Governance (Part II): Compliance, Reputation and Reciprocity," Department of Economics Working Papers 0411, Department of Economics, University of Trento, Italia.
    11. Sandra Notaro & Alessandro Paletto & Roberta Raffaelli, 2008. "Does forest damage have an economic impact? A case study from the Italian Alps," Department of Economics Working Papers 0809, Department of Economics, University of Trento, Italia.
    12. K. Vela Velupillai, 2004. "The unreasonable ineffectiveness of mathematics in economics," Department of Economics Working Papers 0406, Department of Economics, University of Trento, Italia.
    13. Christopher L. Gilbert & Alexandra Tabova, 2004. "Commodity prices and debt sustainability," Department of Economics Working Papers 0404, Department of Economics, University of Trento, Italia.

  20. Marco Bee, 2001. "Mixture models for VaR and stress testing," Alea Tech Reports 012, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.

    Cited by:

    1. Enrico Zaninotto & Alessandro Rossi & Loris Gaio, 1999. "Stochastic learning in coordination games: a simulation approach," Quaderni DISA 015, Department of Computer and Management Sciences, University of Trento, Italy, revised 29 Jun 2003.
    2. Marco Filagrana, 2002. "Il model risk nella gestione dei rischi di mercato," Alea Tech Reports 015, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    3. Flavio Bazzana & Francesca Debortoli, 2002. "Il rischio sistemico in finanza: una rassegna dei recenti contributi in letteratura," Alea Tech Reports 017, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.

Articles

  1. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).

    Cited by:

    1. Santosh KUMAR & Bharat Kumar MEHER & Ramona BIRAU & Abhishek ANAND & Mircea Laurentiu SIMION, 2023. "Investigating Volatility Dynamics of the Portugal Stock Market using FIGARCH Models," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 39-45.
    2. Batten, Jonathan A. & Mo, Di & Pourkhanali, Armin, 2024. "Can inflation predict energy price volatility?," Energy Economics, Elsevier, vol. 129(C).

  2. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.

    Cited by:

    1. Abdussalam Aljadani & Bshair Alharthi & Mohammed A. Farsi & Hossam Magdy Balaha & Mahmoud Badawy & Mostafa A. Elhosseini, 2023. "Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach," Mathematics, MDPI, vol. 11(19), pages 1-28, September.
    2. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    3. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    4. Luis J. Mena & Vicente García & Vanessa G. Félix & Rodolfo Ostos & Rafael Martínez-Peláez & Alberto Ochoa-Brust & Pablo Velarde-Alvarado, 2024. "Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

  3. M. Bee & J. Hambuckers & L. Trapin, 2021. "Estimating large losses in insurance analytics and operational risk using the g-and-h distribution," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1207-1221, July.

    Cited by:

    1. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages," Papers 2404.08665, arXiv.org.
    2. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    3. Marco Bee, 2022. "The truncated g-and-h distribution: estimation and application to loss modeling," Computational Statistics, Springer, vol. 37(4), pages 1771-1794, September.
    4. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.

  4. M. Bee & J. Hambuckers & L. Trapin, 2019. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1255-1266, August.
    See citations under working paper version above.
  5. M. Bee & L. Trapin, 2018. "A characteristic function-based approach to approximate maximum likelihood estimation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(13), pages 3138-3160, July.

    Cited by:

    1. Marco Bee, 2022. "The truncated g-and-h distribution: estimation and application to loss modeling," Computational Statistics, Springer, vol. 37(4), pages 1771-1794, September.
    2. Marcin Pitera & Aleksei Chechkin & Agnieszka Wyłomańska, 2022. "Goodness-of-fit test for $$\alpha$$ α -stable distribution based on the quantile conditional variance statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 387-424, June.

  6. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2018. "Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 398-415, April.

    Cited by:

    1. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    2. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
    3. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
    4. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    5. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

  7. Marco Bee & Stefano Schiavo, 2018. "Powerless: gains from trade when firm productivity is not Pareto distributed," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 154(1), pages 15-45, February.
    See citations under working paper version above.
  8. Marco Bee & Maria Michela Dickson & Flavio Santi, 2018. "Likelihood-based risk estimation for variance-gamma models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 69-89, March.
    See citations under working paper version above.
  9. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

    Cited by:

    1. Katleho Makatjane & Ntebogang Moroke, 2021. "Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index," IJFS, MDPI, vol. 9(2), pages 1-18, March.
    2. Hamed Tabasi & Vahidreza Yousefi & Jolanta Tamošaitienė & Foroogh Ghasemi, 2019. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models," Administrative Sciences, MDPI, vol. 9(2), pages 1-17, May.
    3. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

  10. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2017. "Where Gibrat meets Zipf: Scale and scope of French firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 265-275.
    See citations under working paper version above.
  11. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.

    Cited by:

    1. Małgorzata Just & Krzysztof Echaust, 2021. "An Optimal Tail Selection in Risk Measurement," Risks, MDPI, vol. 9(4), pages 1-16, April.
    2. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    3. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    4. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    5. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
    6. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
    7. Wang, Yi-Chiuan & Wu, Jyh-Lin & Lai, Yi-Hao, 2018. "New evidence on asymmetric return–volume dependence and extreme movements," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 212-227.
    8. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    9. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    10. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    11. Łuczak, Aleksandra & Just, Małgorzata, 2021. "Sustainable development of territorial units: MCDM approach with optimal tail selection," Ecological Modelling, Elsevier, vol. 457(C).
    12. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    13. Just, Małgorzata & Echaust, Krzysztof, 2024. "Cryptocurrencies against stock market risk: New insights into hedging effectiveness," Research in International Business and Finance, Elsevier, vol. 67(PA).
    14. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
    15. Yi-Chiuan Wang & Yi-hao Lai & Jyh-Lin Wu, 2024. "Asymmetries in risk spillovers between currency and stock markets: Evidence from the CoVaR-copula approach," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 1083-1119, October.
    16. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    17. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    18. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    19. Song, Yuping & Huang, Jiefei & Zhang, Qichao & Xu, Yang, 2024. "Heterogeneity effect of positive and negative jumps on the realized volatility: Evidence from China," Economic Modelling, Elsevier, vol. 136(C).
    20. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.
    21. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

  12. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2016. "US stock returns: are there seasons of excesses?," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1453-1464, September.

    Cited by:

    1. Marco Bee & Massimo Riccaboni & Luca Trapin, 2016. "An extreme value analysis of the last century crises across industries in the U.S. economy," Working Papers 02/2016, IMT School for Advanced Studies Lucca, revised Feb 2016.
    2. Cordero, Arkangel M., 2023. "Community and aftershock: New venture founding in the wake of deadly natural disasters," Journal of Business Venturing, Elsevier, vol. 38(2).

  13. Bee, Marco & Espa, Giuseppe & Giuliani, Diego, 2015. "Approximate maximum likelihood estimation of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 14-26.
    See citations under working paper version above.
  14. Giuseppe Arbia & Marco Bee & Giuseppe Espa, 2013. "Testing Isotropy in Spatial Econometric Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 228-240, September.

    Cited by:

    1. Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.
    2. Bee, Marco & Espa, Giuseppe & Giuliani, Diego, 2015. "Approximate maximum likelihood estimation of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 14-26.
    3. Giuseppe Arbia & Marco Bee & Giuseppe Espa & Flavio Santi, 2014. "Fitting Spatial Econometric Models through the Unilateral Approximation," DEM Discussion Papers 2014/08, Department of Economics and Management.

  15. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2013. "The size distribution of US cities: Not Pareto, even in the tail," Economics Letters, Elsevier, vol. 120(2), pages 232-237.

    Cited by:

    1. Grachev, Gennady A., 2022. "Size distribution of states, counties, and cities in the USA: New inequality form information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    2. Huan Li & Yehua Dennis Wei & Yuemin Ning, 2016. "Spatial and Temporal Evolution of Urban Systems in China during Rapid Urbanization," Sustainability, MDPI, vol. 8(7), pages 1-17, July.
    3. Gualandi, Stefano & Toscani, Giuseppe, 2019. "Size distribution of cities: A kinetic explanation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 221-234.
    4. Cieślik Andrzej & Teresiński Jan, 2016. "Does Zipf’s law hold for Polish cities?," Miscellanea Geographica. Regional Studies on Development, Sciendo, vol. 20(4), pages 5-10, December.
    5. Giorgio Fazio & Marco Modica, 2015. "Pareto Or Log-Normal? Best Fit And Truncation In The Distribution Of All Cities," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 736-756, November.
    6. Arturo Ramos & Till Massing & Atushi Ishikawa & Shouji Fujimoto & Takayuki Mizuno, 2023. "Composite distributions in the social sciences: A comparative empirical study of firms' sales distribution for France, Germany, Italy, Japan, South Korea, and Spain," Papers 2301.09438, arXiv.org.
    7. Growiec, Jakub, 2015. "On the modeling of size distributions when technologies are complex," Journal of Mathematical Economics, Elsevier, vol. 60(C), pages 1-8.
    8. Massimo, Riccaboni & Jakub, Growiec & Fabio, Pammolli, 2011. "Innovation and Corporate Dynamics: A Theoretical Framework," MPRA Paper 30046, University Library of Munich, Germany.
    9. Puente-Ajovín, Miguel & Ramos, Arturo & Sanz-Gracia, Fernando & Arribas-Bel, Daniel, 2020. "How sensitive is city size distribution to the definition of city? The case of Spain," Economics Letters, Elsevier, vol. 197(C).
    10. Campolieti, Michele, 2020. "The distribution of union size: Canada, 1913–2014," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    11. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2017. "Where Gibrat meets Zipf: Scale and scope of French firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 265-275.
    12. Arshad, Sidra & Hu, Shougeng & Ashraf, Badar Nadeem, 2019. "Zipf’s law, the coherence of the urban system and city size distribution: Evidence from Pakistan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 87-103.
    13. Montebruno, Piero & Bennett, Robert J. & van Lieshout, Carry & Smith, Harry, 2019. "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 858-875.
    14. Asif, Muhammad & Hussain, Zawar & Asghar, Zahid & Hussain, Muhammad Irfan & Raftab, Mariya & Shah, Said Farooq & Khan, Akbar Ali, 2021. "A statistical evidence of power law distribution in the upper tail of world billionaires’ data 2010–20," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    15. Rashidisabet, Homa & Ajilore, Olusola & Leow, Alex & Demos, Alexander P., 2022. "Revisiting power-law estimation with applications to real-world human typing dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    16. Tomaschitz, Roman, 2020. "Multiply broken power-law densities as survival functions: An alternative to Pareto and lognormal fits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    17. Campolieti, Michele & Ramos, Arturo, 2021. "The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    18. Gualandi, Stefano & Toscani, Giuseppe, 2017. "Pareto tails in socio-economic phenomena: A kinetic description," Economics Discussion Papers 2017-111, Kiel Institute for the World Economy (IfW Kiel).
    19. Josic Hrvoje & Bašić Maja, 2018. "Reconsidering Zipf’s law for regional development: The case of settlements and cities in Croatia," Miscellanea Geographica. Regional Studies on Development, Sciendo, vol. 22(1), pages 22-30, March.
    20. Verginer, Luca & Riccaboni, Massimo, 2021. "Talent goes to global cities: The world network of scientists’ mobility," Research Policy, Elsevier, vol. 50(1).
    21. Ramos, Arturo & Sanz-Gracia, Fernando, 2015. "US city size distribution revisited: Theory and empirical evidence," MPRA Paper 64051, University Library of Munich, Germany.
    22. Wang, Yuanjun & You, Shibing, 2016. "An alternative method for modeling the size distribution of top wealth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 443-453.
    23. Hasan Engin Duran & Andrzej Cieślik, 2021. "The distribution of city sizes in Turkey: A failure of Zipf’s law due to concavity," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(5), pages 1702-1719, October.
    24. Gualandi, Stefano & Toscani, Giuseppe, 2018. "Pareto tails in socio-economic phenomena: A kinetic description," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-17.
    25. Inna Manaeva, 2019. "Distribution of Cities in Federal Districts of Russia: Testing of the Zipf Law," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(1), pages 84-98.
    26. Pengfei Li & Ming Lu, 2021. "Urban Systems: Understanding and Predicting the Spatial Distribution of China's Population," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(4), pages 35-62, July.

  16. Bee, Marco, 2011. "Adaptive Importance Sampling for simulating copula-based distributions," Insurance: Mathematics and Economics, Elsevier, vol. 48(2), pages 237-245, March.

    Cited by:

    1. Philipp Arbenz & Mathieu Cambou & Marius Hofert, 2014. "An importance sampling approach for copula models in insurance," Papers 1403.4291, arXiv.org, revised Apr 2015.

  17. Taufer, Emanuele & Leonenko, Nikolai & Bee, Marco, 2011. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2525-2539, August.
    See citations under working paper version above.
  18. Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data," Letters in Spatial and Resource Sciences, Springer, vol. 1(1), pages 45-54, July.
    See citations under working paper version above.
  19. Marco Bee, 2005. "Estimating rating transition probabilites with missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 127-141, February.

    Cited by:

    1. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.

  20. Marco Bee, 2004. "Modelling credit default swap spreads by means of normal mixtures and copulas," Applied Mathematical Finance, Taylor & Francis Journals, vol. 11(2), pages 125-146.

    Cited by:

    1. Hayette Gatfaoui, 2010. "Investigating the dependence structure between credit default swap spreads and the U.S. financial market," Annals of Finance, Springer, vol. 6(4), pages 511-535, October.
    2. Chen, Yi-Hsuan & Tu, Anthony H. & Wang, Kehluh, 2008. "Dependence structure between the credit default swap return and the kurtosis of the equity return distribution: Evidence from Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 259-271, July.
    3. Marco Bee, 2007. "The asymptotic loss distribution in a fat-tailed factor model of portfolio credit risk," Department of Economics Working Papers 0701, Department of Economics, University of Trento, Italia.

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