IDEAS home Printed from https://ideas.repec.org/f/pre561.html
   My authors  Follow this author

Maria Cristina Recchioni

Personal Details

First Name:Maria
Middle Name:Cristina
Last Name:Recchioni
Suffix:
RePEc Short-ID:pre561
[This author has chosen not to make the email address public]

Affiliation

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

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

Research output

as
Jump to: Working papers Articles

Working papers

  1. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2016. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Working Papers 2016/23, Economics Department, Universitat Jaume I, Castellón (Spain).
  2. Stefano STAFFOLANI & Maria Cristina RECCHIONI, 2016. "Increasing Graduation and Calling for More Autonomy in Higher Education: Is It a Good Thing? A Theoretical Model," Working Papers 419, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  3. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).
  4. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2014. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," FinMaP-Working Papers 26, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  5. Recchioni, Maria Cristina & Tedeschi, Gabriele & Berardi, Simone, 2014. "Bank's strategies during the financial crisis," FinMaP-Working Papers 25, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  6. Miglierina Enrico & Molho Elena & Recchioni Maria Cristina, 2006. "Box-constrained vector optimization: a steepest descent method without “a priori” scalarization," Economics and Quantitative Methods qf0603, Department of Economics, University of Insubria.

Articles

  1. Mariani, Francesca & Recchioni, Maria Cristina & Ciommi, Mariateresa, 2019. "Merton’s portfolio problem including market frictions: A closed-form formula supporting the shadow price approach," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1178-1189.
  2. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
  3. Francesco M. Chelli & Mariateresa Ciommi & Francesca Mariani & Maria Cristina Recchioni, 2018. "Opportunity and discrimination in tertiary education: a proposal of aggregation for some European countries," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 72(2), pages 85-95, April-Jun.
  4. Curato, Imma Valentina & Mancino, Maria Elvira & Recchioni, Maria Cristina, 2018. "Spot volatility estimation using the Laplace transform," Econometrics and Statistics, Elsevier, vol. 6(C), pages 22-43.
  5. Francesco Maria Chelli & Francesca Mariani & Maria Cristina Recchioni & Andrea Rimondi, 2018. "Stock return comovements and economic wealth conditions," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 72(4), pages 5-16, October-D.
  6. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
  7. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2017. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1257-1275, August.
  8. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
  9. 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.
  10. Lorella Fatone & Francesca Mariani & Maria Cristina Recchioni & Francesco Zirilli, 2013. "The Analysis of Real Data Using a Multiscale Stochastic Volatility Model," European Financial Management, European Financial Management Association, vol. 19(1), pages 153-179, January.
  11. Lorella Fatone & Francesca Mariani & Maria Cristina Recchioni & Francesco Zirilli, 2009. "An explicitly solvable multi‐scale stochastic volatility model: Option pricing and calibration problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(9), pages 862-893, September.
  12. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.
  13. Fusai, Gianluca & Recchioni, Maria Cristina, 2007. "Analysis of quadrature methods for pricing discrete barrier options," Journal of Economic Dynamics and Control, Elsevier, vol. 31(3), pages 826-860, March.
  14. L. Fatone & M. C. Recchioni & F. Zirilli, 2004. "Furtivity and Masking Problems in Time-Dependent Electromagnetic Obstacle Scattering," Journal of Optimization Theory and Applications, Springer, vol. 121(2), pages 223-257, May.
  15. Maria Cristina Recchioni, 2003. "A path following method for box-constrained multiobjective optimization with applications to goal programming problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(1), pages 69-85, September.
  16. G. Pacelli & M. C. Recchioni, 2000. "Monotone Variable–Metric Algorithm for Linearly Constrained Nonlinear Programming," Journal of Optimization Theory and Applications, Springer, vol. 104(2), pages 255-279, February.
  17. Graziella Pacelli & Maria Cristina Recchioni & Francesco Zirilli, 1999. "A hybrid method for pricing European options based on multiple assets with transaction costs," Applied Mathematical Finance, Taylor & Francis Journals, vol. 6(2), pages 61-85.
  18. M. Mochi & G. Pacelli & M. C. Recchioni & F. Zirilli, 1999. "Inverse Problem for a Class of Two-Dimensional Diffusion Equations with Piecewise Constant Coefficients," Journal of Optimization Theory and Applications, Springer, vol. 100(1), pages 29-57, January.

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. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2016. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Working Papers 2016/23, Economics Department, Universitat Jaume I, Castellón (Spain).

    Cited by:

    1. Riccardo Bramante & Gimmi Dallago & Silvia Facchinetti, 2022. "Black’s model in a negative interest rate environment, with application to OTC derivatives," Computational Management Science, Springer, vol. 19(1), pages 25-39, January.
    2. Deelstra, Griselda & Grasselli, Martino & Van Weverberg, Christopher, 2016. "The role of the dependence between mortality and interest rates when pricing Guaranteed Annuity Options," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 205-219.
    3. Jae‐Yun Jun & Victor Lebreton & Yves Rakotondratsimba, 2021. "Forecasting negative yield‐curve distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 367-386, April.

  2. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2014. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," FinMaP-Working Papers 26, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

    Cited by:

    1. Giuliana Passamani & Roberto Tamborini & Matteo Tomaselli, 2016. "Taxing financial transactions in fundamentally heterogeneous markets," DEM Working Papers 2016/10, Department of Economics and Management.
    2. Seri, Raffaello & Martinoli, Mario & Secchi, Davide & Centorrino, Samuele, 2021. "Model calibration and validation via confidence sets," Econometrics and Statistics, Elsevier, vol. 20(C), pages 62-86.
    3. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02312186, HAL.
    4. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Papers 1703.10639, arXiv.org, revised Apr 2017.
    5. 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.
    6. Marwil J. Davila-Fernandez & Serena Sordi, 2022. "The Green-MKS system: A baseline environmental macro-dynamic model," Department of Economics University of Siena 890, Department of Economics, University of Siena.
    7. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Alexandru Mandes & Peter Winker, 2015. "Complexity and Model Comparison in Agent Based Modeling of Financial Markets," MAGKS Papers on Economics 201528, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Domenico Delli Gatti & Jakob Grazzini, 2019. "Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models," CESifo Working Paper Series 7894, CESifo.
    10. Giorgio Fagiolo & Andrea Roventini, 2016. "Macroeconomic Policy in DGSE and Agent-Based Models Redux: New Developments and Challenges Ahead," Sciences Po publications info:hdl:2441/dcditnq6282, Sciences Po.
    11. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    12. 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.
    13. Özge Dilaver & Robert Jump & Paul Levine, 2016. "Agent-based Macroeconomics and Dynamic Stochastic General Equilibrium Models: Where do we go from here?," School of Economics Discussion Papers 0116, School of Economics, University of Surrey.
    14. Yi Zhang & Zhe Li & Yongchao Zhang, 2020. "Validation and Calibration of an Agent-Based Model: A Surrogate Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-9, January.
    15. Filippo Gusella, 2022. "Detecting And Measuring Financial Cycles In Heterogeneous Agents Models: An Empirical Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 25(02n03), pages 1-22, March.
    16. 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).
    17. Luigi Bonatti & Lorenza Lorenzetti, 2016. "The co-evolution of tax evasion, social capital and policy responses: A theoretical approach," DEM Working Papers 2016/08, Department of Economics and Management.
    18. Xu, Hai-Chuan & Zhang, Wei & Xiong, Xiong & Wang, Xue & Zhou, Wei-Xing, 2021. "The double-edged role of social learning: Flash crash and lower total volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 405-420.
    19. Platt, Donovan, 2020. "A comparison of economic agent-based model calibration methods," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    20. 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.
    21. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02000726, HAL.
    22. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    23. 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.
    24. 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.
    25. Dinghai Xu & Jingru Ji & Donghua Wang, 2018. "Modelling the spreading process of extreme risks via a simple agent-based model: Evidence from the China stock market," Working Papers 1806, University of Waterloo, Department of Economics, revised 09 Jan 2018.
    26. Gabriele Tedeschi & Fabio Caccioli & Maria Cristina Recchioni, 2020. "Taming financial systemic risk: models, instruments and early warning indicators," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 1-7, January.
    27. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
    28. Lamperti, Francesco, 2018. "An information theoretic criterion for empirical validation of simulation models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 83-106.
    29. 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.
    30. Kukacka, Jiri & Barunik, Jozef, 2016. "Estimation of financial agent-based models with simulated maximum likelihood," FinMaP-Working Papers 63, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    31. 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.
    32. G. Rigatos, 2021. "Statistical Validation of Multi-Agent Financial Models Using the H-Infinity Kalman Filter," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 777-798, October.
    33. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    34. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    35. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
    36. 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.
    37. Gaffeo, Edoardo & Molinari, Massimo, 2017. "Taxing financial transactions in fundamentally heterogeneous markets," Economic Modelling, Elsevier, vol. 64(C), pages 322-333.
    38. Francesco Lamperti, 2016. "Empirical Validation of Simulated Models through the GSL-div: an Illustrative Application," LEM Papers Series 2016/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    39. 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.
    40. Giorgio Fagiolo & Andrea Roventini, 2016. "Macroeconomic Policy in DGSE and Agent-Based Models Redux," Working Papers hal-03459348, HAL.
    41. Simone Berardi & Gabriele Tedeschi, 2016. "How banks’ strategies influence financial cycles: An approach to identifying micro behavior," Working Papers 2016/24, Economics Department, Universitat Jaume I, Castellón (Spain).
    42. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    43. Niu, Xiaojian & Niu, Xiaoli & Wu, Kexing, 2021. "Implicit government guarantees and the externality of portfolio diversification: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    44. Siyan Chen & Saul Desiderio, 2022. "A Regression-Based Calibration Method for Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 687-700, February.
    45. Grilli, Ruggero & Tedeschi, Gabriele & Gallegati, Mauro, 2020. "Business fluctuations in a behavioral switching model: Gridlock effects and credit crunch phenomena in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    46. Minh Tran & Thanh Duong & Duc Pham-Hi & Marc Bui, 2020. "Detecting the Proportion of Traders in the Stock Market: An Agent-Based Approach," Mathematics, MDPI, vol. 8(2), pages 1-14, February.
    47. Colasante, Annarita, 2016. "Evolution of Cooperation in Public Good Game," MPRA Paper 72577, University Library of Munich, Germany.
    48. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    49. Elizabeth Jane Casabianca & Alessia Lo Turco & Daniela Maggioni, 2021. "Migration And The Structure Of Manufacturing Production. A View From Italian Provinces," Working Papers 448, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    50. Francesco Lamperti, 2018. "Empirical validation of simulated models through the GSL-div: an illustrative application," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 143-171, April.
    51. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    52. Ciola, Emanuele & Gaffeo, Edoardo & Gallegati, Mauro, 2022. "Search for profits and business fluctuations: How does banks’ behaviour explain cycles?," Journal of Economic Dynamics and Control, Elsevier, vol. 135(C).
    53. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    54. 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.
    55. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

  3. Recchioni, Maria Cristina & Tedeschi, Gabriele & Berardi, Simone, 2014. "Bank's strategies during the financial crisis," FinMaP-Working Papers 25, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

    Cited by:

    1. Ilker Arslan & Eugenio Caverzasi & Mauro Gallegati & Alper Duman, 2016. "Long Term Impacts of Bank Behavior on Financial Stability. an Agent Based Modeling Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-11.

Articles

  1. Mariani, Francesca & Recchioni, Maria Cristina & Ciommi, Mariateresa, 2019. "Merton’s portfolio problem including market frictions: A closed-form formula supporting the shadow price approach," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1178-1189.

    Cited by:

    1. Feghhi Kashani , Mohammad & Mohebimajd , Ahmadreza, 2021. "Outperformance Testing of a Dynamic Assets Portfolio Selection Supplemented with a Continuous Paths Levy Process," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(2), pages 253-282, June.
    2. Nikolay Khabarov & Alexey Smirnov & Michael Obersteiner, 2022. "Shadow prices and optimal cost in economic applications," Papers 2211.03591, arXiv.org, revised Nov 2022.
    3. Francesca Mariani & Gloria Polinesi & Maria Cristina Recchioni, 2022. "A tail-revisited Markowitz mean-variance approach and a portfolio network centrality," Computational Management Science, Springer, vol. 19(3), pages 425-455, July.

  2. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.

    Cited by:

    1. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    2. Gabriele Tedeschi & Fabio Caccioli & Maria Cristina Recchioni, 2020. "Taming financial systemic risk: models, instruments and early warning indicators," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 1-7, January.
    3. Kapar, Burcu & Iori, Giulia & Gabbi, Giampaolo & Germano, Guido, 2020. "Market microstructure, banks' behaviour and interbank spreads: evidence after the crisis," LSE Research Online Documents on Economics 100467, London School of Economics and Political Science, LSE Library.
    4. Deborah Noguera & Gabriel Montes-Rojas, 2023. "Minskyan model with credit rationing in a network economy," SN Business & Economics, Springer, vol. 3(3), pages 1-26, March.
    5. Deborah Noguera & Gabriel Montes-Rojas, 2022. "Credit-constrained fluctuations and uncertainty in a network economy," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(80), pages 5-52, November.

  3. Curato, Imma Valentina & Mancino, Maria Elvira & Recchioni, Maria Cristina, 2018. "Spot volatility estimation using the Laplace transform," Econometrics and Statistics, Elsevier, vol. 6(C), pages 22-43.

    Cited by:

    1. Madan, Dilip B. & Wang, King, 2018. "Strike asymptotics for Laplace implied volatilities," Finance Research Letters, Elsevier, vol. 25(C), pages 183-189.
    2. 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.
    3. Curato, Imma Valentina, 2019. "Estimation of the stochastic leverage effect using the Fourier transform method," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3207-3238.

  4. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.

    Cited by:

    1. Silvio Contessi & Pierangelo De Pace & Massimo Guidolin, 2020. "Mildly Explosive Dynamics in U.S. Fixed Income Markets," Working Papers 667, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Roberto Antonietti & Giulia De Masi & Giorgio Ricchiuti, 2020. "Linking FDI Network Topology with the Covid-19 Pandemic," Papers in Evolutionary Economic Geography (PEEG) 2054, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Nov 2020.
    3. Maria Elvira Mancino & Simona Sanfelici, 2020. "Identifying financial instability conditions using high frequency data," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 221-242, January.
    4. Lioui, Abraham & Tarelli, Andrea, 2019. "Macroeconomic environment, money demand and portfolio choice," European Journal of Operational Research, Elsevier, vol. 274(1), pages 357-374.
    5. Francesco Campigli & Gabriele Tedeschi & Maria Cristina Recchioni, 2021. "The talkative variables of the hybrid Heston model: Yields’ maturity and economic (in)stability," Working Papers 2021/03, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. Mazzocchetti, Andrea & Lauretta, Eliana & Raberto, Marco & Teglio, Andrea & Cincotti, Silvano, 2018. "Systemic Financial Risk Indicators and Securitised Assets: an Agent-Based Framework," MPRA Paper 89779, University Library of Munich, Germany.
    7. Gabriele Tedeschi & Fabio Caccioli & Maria Cristina Recchioni, 2020. "Taming financial systemic risk: models, instruments and early warning indicators," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 1-7, January.
    8. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.
    9. Recchioni, Maria Cristina & Iori, Giulia & Tedeschi, Gabriele & Ouellette, Michelle S., 2021. "The complete Gaussian kernel in the multi-factor Heston model: Option pricing and implied volatility applications," European Journal of Operational Research, Elsevier, vol. 293(1), pages 336-360.
    10. Annarita Colasante & Aurora García-Gallego & Nikolaos Georgantzis & Andrea Morone, 2020. "Voluntary contributions in a system with uncertain returns: a case of systemic risk," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 111-132, January.

  5. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2017. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1257-1275, August.
    See citations under working paper version above.
  6. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.

    Cited by:

    1. Chaoqun Ma & Shengjie Yue & Hui Wu & Yong Ma, 2020. "Pricing Vulnerable Options with Stochastic Volatility and Stochastic Interest Rate," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 391-429, August.
    2. Rehez Ahlip & Laurence A. F. Park & Ante Prodan, 2017. "Pricing currency options in the Heston/CIR double exponential jump-diffusion model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-30, March.
    3. Kirkby, J. Lars, 2023. "Hybrid equity swap, cap, and floor pricing under stochastic interest by Markov chain approximation," European Journal of Operational Research, Elsevier, vol. 305(2), pages 961-978.
    4. Francesco Campigli & Gabriele Tedeschi & Maria Cristina Recchioni, 2021. "The talkative variables of the hybrid Heston model: Yields’ maturity and economic (in)stability," Working Papers 2021/03, Economics Department, Universitat Jaume I, Castellón (Spain).
    5. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2016. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Working Papers 2016/23, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. Ewald, Christian & Zou, Yihan, 2021. "Analytic formulas for futures and options for a linear quadratic jump diffusion model with seasonal stochastic volatility and convenience yield: Do fish jump?," European Journal of Operational Research, Elsevier, vol. 294(2), pages 801-815.
    7. Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2017. "A general framework for discretely sampled realized variance derivatives in stochastic volatility models with jumps," European Journal of Operational Research, Elsevier, vol. 262(1), pages 381-400.
    8. M. Escobar & D. Neykova & R. Zagst, 2017. "HARA utility maximization in a Markov-switching bond–stock market," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1715-1733, November.
    9. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    10. Recchioni, Maria Cristina & Iori, Giulia & Tedeschi, Gabriele & Ouellette, Michelle S., 2021. "The complete Gaussian kernel in the multi-factor Heston model: Option pricing and implied volatility applications," European Journal of Operational Research, Elsevier, vol. 293(1), pages 336-360.
    11. Rémi Stellian & Gabriel I. Penagos & Jenny P. Danna-Buitrago, 2021. "Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 59-101, January.
    12. Tan, Ken Seng & Weng, Chengguo & Zhang, Jinggong, 2022. "Optimal dynamic longevity hedge with basis risk," European Journal of Operational Research, Elsevier, vol. 297(1), pages 325-337.
    13. Giuseppe Orlando & Michele Bufalo, 2022. "A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1608-1622, December.

  7. 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.
    See citations under working paper version above.
  8. Lorella Fatone & Francesca Mariani & Maria Cristina Recchioni & Francesco Zirilli, 2013. "The Analysis of Real Data Using a Multiscale Stochastic Volatility Model," European Financial Management, European Financial Management Association, vol. 19(1), pages 153-179, January.

    Cited by:

    1. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2014. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," FinMaP-Working Papers 26, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    2. Andreas Kaeck & Carol Alexander, 2013. "Stochastic Volatility Jump†Diffusions for European Equity Index Dynamics," European Financial Management, European Financial Management Association, vol. 19(3), pages 470-496, June.
    3. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2016. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Working Papers 2016/23, Economics Department, Universitat Jaume I, Castellón (Spain).
    4. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
    5. Recchioni, Maria Cristina & Iori, Giulia & Tedeschi, Gabriele & Ouellette, Michelle S., 2021. "The complete Gaussian kernel in the multi-factor Heston model: Option pricing and implied volatility applications," European Journal of Operational Research, Elsevier, vol. 293(1), pages 336-360.

  9. Lorella Fatone & Francesca Mariani & Maria Cristina Recchioni & Francesco Zirilli, 2009. "An explicitly solvable multi‐scale stochastic volatility model: Option pricing and calibration problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(9), pages 862-893, September.

    Cited by:

    1. Maria Cristina Recchioni & Yu Sun & Gabriele Tedeschi, 2016. "Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model," Working Papers 2016/23, Economics Department, Universitat Jaume I, Castellón (Spain).
    2. Gifty Malhotra & R. Srivastava & H. C. Taneja, 2018. "Quadratic approximation of the slow factor of volatility in a multifactor stochastic volatility model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(5), pages 607-624, May.
    3. Seungho Yang & Jaewook Lee, 2014. "Do affine jump-diffusion models require global calibration? Empirical studies from option markets," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 111-123, January.
    4. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
    5. Bianca Reichert & Adriano Mendon a Souza, 2022. "Can the Heston Model Forecast Energy Generation? A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 289-295.
    6. Jang, H. & Lee, J., 2019. "Machine learning versus econometric jump models in predictability and domain adaptability of index options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 74-86.
    7. Tao Pang & Katherine Varga, 2019. "Portfolio Optimization for Assets with Stochastic Yields and Stochastic Volatility," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 691-729, August.
    8. Issouf Soumaré & Ernest Tafolong, 2017. "Risk-based capital for credit insurers with business cycles and dynamic leverage," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 597-612, April.

  10. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.

    Cited by:

    1. M. L. N. Gonçalves & F. S. Lima & L. F. Prudente, 2022. "Globally convergent Newton-type methods for multiobjective optimization," Computational Optimization and Applications, Springer, vol. 83(2), pages 403-434, November.
    2. Chen, Wang & Yang, Xinmin & Zhao, Yong, 2023. "Memory gradient method for multiobjective optimization," Applied Mathematics and Computation, Elsevier, vol. 443(C).
    3. Gonçalves, M.L.N. & Lima, F.S. & Prudente, L.F., 2022. "A study of Liu-Storey conjugate gradient methods for vector optimization," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    4. Glaydston Carvalho Bento & Joao Xavier Neto & Paulo Roberto Oliveira & Antoine Soubeyran, 2014. "The self regulation problem as an inexact steepest descent method for multicriteria optimization," Post-Print hal-01474415, HAL.
    5. M. L. N. Gonçalves & L. F. Prudente, 2020. "On the extension of the Hager–Zhang conjugate gradient method for vector optimization," Computational Optimization and Applications, Springer, vol. 76(3), pages 889-916, July.
    6. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    7. L. F. Prudente & D. R. Souza, 2022. "A Quasi-Newton Method with Wolfe Line Searches for Multiobjective Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1107-1140, September.
    8. Matteo Lapucci & Pierluigi Mansueto, 2023. "A limited memory Quasi-Newton approach for multi-objective optimization," Computational Optimization and Applications, Springer, vol. 85(1), pages 33-73, May.
    9. P. B. Assunção & O. P. Ferreira & L. F. Prudente, 2021. "Conditional gradient method for multiobjective optimization," Computational Optimization and Applications, Springer, vol. 78(3), pages 741-768, April.
    10. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).
    11. Qing-Rui He & Chun-Rong Chen & Sheng-Jie Li, 2023. "Spectral conjugate gradient methods for vector optimization problems," Computational Optimization and Applications, Springer, vol. 86(2), pages 457-489, November.

  11. Fusai, Gianluca & Recchioni, Maria Cristina, 2007. "Analysis of quadrature methods for pricing discrete barrier options," Journal of Economic Dynamics and Control, Elsevier, vol. 31(3), pages 826-860, March.

    Cited by:

    1. A. Golbabai & L. Ballestra & D. Ahmadian, 2014. "A Highly Accurate Finite Element Method to Price Discrete Double Barrier Options," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 153-173, August.
    2. Keegan Mendonca & Vasileios E. Kontosakos & Athanasios A. Pantelous & Konstantin M. Zuev, 2018. "Efficient Pricing of Barrier Options on High Volatility Assets using Subset Simulation," Papers 1803.03364, arXiv.org, revised Mar 2018.
    3. Jin, Ting & Ding, Hui & Xia, Hongxuan & Bao, Jinfeng, 2021. "Reliability index and Asian barrier option pricing formulas of the uncertain fractional first-hitting time model with Caputo type," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Wendong Zheng & Chi Hung Yuen & Yue Kuen Kwok, 2016. "Recursive Algorithms For Pricing Discrete Variance Options And Volatility Swaps Under Time-Changed Lévy Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 1-29, March.
    5. Saadet Eskiizmirliler & Korhan Günel & Refet Polat, 2021. "On the Solution of the Black–Scholes Equation Using Feed-Forward Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 915-941, October.
    6. Fusai, Gianluca & Germano, Guido & Marazzina, Daniele, 2016. "Spitzer identity, Wiener-Hopf factorization and pricing of discretely monitored exotic options," European Journal of Operational Research, Elsevier, vol. 251(1), pages 124-134.
    7. Kirkby, J. Lars, 2023. "Hybrid equity swap, cap, and floor pricing under stochastic interest by Markov chain approximation," European Journal of Operational Research, Elsevier, vol. 305(2), pages 961-978.
    8. Gongqiu Zhang & Lingfei Li, 2021. "A General Approach for Lookback Option Pricing under Markov Models," Papers 2112.00439, arXiv.org.
    9. Dai, Min & Li, Peifan & Zhang, Jin E., 2010. "A lattice algorithm for pricing moving average barrier options," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 542-554, March.
    10. Jie Chen & Liaoyuan Fan & Lingfei Li & Gongqiu Zhang, 2022. "A multidimensional Hilbert transform approach for barrier option pricing and survival probability calculation," Review of Derivatives Research, Springer, vol. 25(2), pages 189-232, July.
    11. Rahman Farnoosh & Hamidreza Rezazadeh & Amirhossein Sobhani & M. Hossein Beheshti, 2016. "A Numerical Method for Discrete Single Barrier Option Pricing with Time-Dependent Parameters," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 131-145, June.
    12. Sorawit Saengkyongam & Anthony Hayter & Seksan Kiatsupaibul & Wei Liu, 2020. "Efficient computation of the stochastic behavior of partial sum processes," Computational Statistics, Springer, vol. 35(1), pages 343-358, March.
    13. Sesana, Debora & Marazzina, Daniele & Fusai, Gianluca, 2014. "Pricing exotic derivatives exploiting structure," European Journal of Operational Research, Elsevier, vol. 236(1), pages 369-381.
    14. Lingfei Li & Vadim Linetsky, 2015. "Discretely monitored first passage problems and barrier options: an eigenfunction expansion approach," Finance and Stochastics, Springer, vol. 19(4), pages 941-977, October.
    15. Amirhossein Sobhani & Mariyan Milev, 2017. "A Numerical Method for Pricing Discrete Double Barrier Option by Lagrange Interpolation on Jacobi Node," Papers 1712.01060, arXiv.org, revised Feb 2018.
    16. Kontosakos, Vasileios E. & Mendonca, Keegan & Pantelous, Athanasios A. & Zuev, Konstantin M., 2021. "Pricing discretely-monitored double barrier options with small probabilities of execution," European Journal of Operational Research, Elsevier, vol. 290(1), pages 313-330.
    17. Amirhossein Sobhani & Mariyan Milev, 2017. "A Numerical Method for Pricing Discrete Double Barrier Option by Legendre Multiwavelet," Papers 1703.09129, arXiv.org, revised Mar 2017.
    18. Fusai, Gianluca & Meucci, Attilio, 2008. "Pricing discretely monitored Asian options under Levy processes," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2076-2088, October.
    19. Hideharu Funahashi & Masaaki Kijima, 2016. "Analytical pricing of single barrier options under local volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 16(6), pages 867-886, June.
    20. Lian, Guanghua & Zhu, Song-Ping & Elliott, Robert J. & Cui, Zhenyu, 2017. "Semi-analytical valuation for discrete barrier options under time-dependent Lévy processes," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 167-183.
    21. Christoph Becker & Uwe Wystup, 2009. "On the cost of delayed currency fixing announcements," Annals of Finance, Springer, vol. 5(2), pages 161-174, March.

  12. Maria Cristina Recchioni, 2003. "A path following method for box-constrained multiobjective optimization with applications to goal programming problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(1), pages 69-85, September.

    Cited by:

    1. Lourdes Uribe & Johan M Bogoya & Andrés Vargas & Adriana Lara & Günter Rudolph & Oliver Schütze, 2020. "A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems," Mathematics, MDPI, vol. 8(10), pages 1-29, October.
    2. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.

  13. G. Pacelli & M. C. Recchioni, 2000. "Monotone Variable–Metric Algorithm for Linearly Constrained Nonlinear Programming," Journal of Optimization Theory and Applications, Springer, vol. 104(2), pages 255-279, February.

    Cited by:

    1. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.
    2. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (3) 2007-01-23 2015-01-31 2015-01-31
  2. NEP-EDU: Education (1) 2016-06-25
  3. NEP-EEC: European Economics (1) 2016-05-28
  4. NEP-FOR: Forecasting (1) 2015-01-31
  5. NEP-LMA: Labor Markets - Supply, Demand, and Wages (1) 2016-06-25
  6. NEP-MAC: Macroeconomics (1) 2016-05-28
  7. NEP-MON: Monetary Economics (1) 2016-05-28
  8. NEP-NET: Network Economics (1) 2016-06-25

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Maria Cristina Recchioni should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.