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Andreas Joseph

Personal Details

First Name:Andreas
Middle Name:
Last Name:Joseph
Suffix:
RePEc Short-ID:pjo331
[This author has chosen not to make the email address public]
https://sites.google.com/site/datatoolx/

Affiliation

Bank of England

London, United Kingdom
http://www.bankofengland.co.uk/
RePEc:edi:boegvuk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.
  2. Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.
  3. Joseph, Andreas & Kneer, Christiane & van Horen, Neeltje & Saleheen, Jumana, 2019. "All you need is cash: corporate cash holdings and investment after the financial crisis," Bank of England working papers 843, Bank of England.
  4. Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.
  5. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  6. Cielinska, Olga & Joseph, Andreas & Shreyas, Ujwal & Tanner, John & Vasios, Michalis, 2017. "Gauging market dynamics using trade repository data: the case of the Swiss franc de-pegging," Bank of England Financial Stability Papers 41, Bank of England.
  7. Joseph, Andreas & Osbat, Chiara, 2016. "How you export matters: the disassortative structure of international trade," Working Paper Series 1958, European Central Bank.
  8. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between financial and environmental networks in OECD countries," Papers 1501.04992, arXiv.org, revised Apr 2015.
  9. Osbat, Chiara & Zollino, Francesco & Aiello, Giovanni & Bluhm, Benjamin & Buelens, Christian & Cavallini, Flavia & Joseph, Andreas & Leonte, Alexandru & Lommatzsch, Kirsten & Momchilov, Georgi & Giord, 2015. "Compendium on the diagnostic toolkit for competitiveness," Occasional Paper Series 163, European Central Bank.
  10. Andreas Joseph & Irena Vodenska & Eugene Stanley & Guanrong Chen, 2014. "Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics," Papers 1403.0848, arXiv.org.
  11. Andreas Joseph & Stephan Joseph & Guanrong Chen, 2013. "Cross-border Portfolio Investment Networks and Indicators for Financial Crises," Papers 1306.0215, arXiv.org, revised Jan 2014.

Articles

  1. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between Financial and Environmental Networks in OECD Countries," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.

Chapters

  1. Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017. "Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.

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. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.

    Cited by:

    1. Potjagailo, Galina & Wolters, Maik H., 2019. "Global financial cycles since 1880," IMFS Working Paper Series 132, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    2. Lloyd, S. & Manuel, E. & Panchev, K., 2021. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," Cambridge Working Papers in Economics 2156, Faculty of Economics, University of Cambridge.
    3. Rey, Hélène & FOULIARD, Jeremy & Howell, Michael, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, C.E.P.R. Discussion Papers.
    4. Hurley, James & Karmakar, Sudipto & Markoska, Elena & Walczak, Eryk & Walker, Danny, 2021. "Impacts of the Covid-19 crisis: evidence from 2 million UK SMEs," Bank of England working papers 924, Bank of England.
    5. Hyeongwoo Kim & Wen Shi, 2020. "Forecasting Financial Vulnerability in the US: A Factor Model Approach," Auburn Economics Working Paper Series auwp2020-04, Department of Economics, Auburn University.
    6. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    7. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised Mar 2024.
    8. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    9. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    10. Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Seismonomics: Listening to the heartbeat of the economy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 288-309, December.
    11. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    12. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
    13. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    14. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    15. du Plessis, Emile & Fritsche, Ulrich, 2022. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," WiSo-HH Working Paper Series 67, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    16. Peter Breyer & Stefan Girsch & Jakob Hanzl & Mario Hübler & Sophie Steininger & Elisabeth Wittig, 2023. "An analysis of Austrian banks during the high inflation period of the 1970s," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 45, pages 45-59.
    17. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "On the efficient synthesis of short financial time series: A Dynamic Factor Model approach," Finance Research Letters, Elsevier, vol. 53(C).
    18. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    19. Simona Malovaná & Josef Bajzík & Dominika Ehrenbergerová & Jan Janků, 2023. "A prolonged period of low interest rates in Europe: Unintended consequences," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 526-572, April.
    20. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    21. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    22. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
    23. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.

  2. Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.

    Cited by:

    1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    2. Filippos Petroulakis, 2023. "Task Content and Job Losses in the Great Lockdown," ILR Review, Cornell University, ILR School, vol. 76(3), pages 586-613, May.
    3. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    4. Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Papers 2107.02512, arXiv.org, revised Sep 2022.
    5. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    6. Michael Puglia & Adam Tucker, 2020. "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series 2020-038, Board of Governors of the Federal Reserve System (U.S.).
    7. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.

  3. Joseph, Andreas & Kneer, Christiane & van Horen, Neeltje & Saleheen, Jumana, 2019. "All you need is cash: corporate cash holdings and investment after the financial crisis," Bank of England working papers 843, Bank of England.

    Cited by:

    1. Giorgio Calcagnini & Laura Gardini & Germana Giombini & Edgar S. Carrera, 2022. "Does too much liquidity generate instability?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(1), pages 191-208, January.
    2. Masami Imai & Michiru Sawada, 2022. "Does a Financial Crisis Impair Corporate Innovation?," Wesleyan Economics Working Papers 2022-002, Wesleyan University, Department of Economics.
    3. Yacine Belghitar & Andrea Moro & Nemanja Radić, 2022. "When the rainy day is the worst hurricane ever: the effects of governmental policies on SMEs during COVID-19," Small Business Economics, Springer, vol. 58(2), pages 943-961, February.
    4. Ahmad, Muhammad Farooq & Kowalewski, Oskar, 2021. "Collective bargaining power and corporate cash policy," International Review of Law and Economics, Elsevier, vol. 68(C).
    5. Tatiana Didier & Federico Huneeus & Mauricio Larrain & Sergio L. Schmukler, 2020. "Financing Firms in Hibernation During the COVID-19 Pandemic," World Bank Publications - Reports 33611, The World Bank Group.
    6. Van Dijcke, David & Buckmann, Marcus & Turrell, Arthur & Key, Tomas, 2023. "Vacancy posting, firm balance sheets, and pandemic policy," Bank of England working papers 1033, Bank of England.
    7. Aikman, David & Beale, Daniel & Brinley-Codd, Adam & Covi, Giovanni & Hüser, Anne‑Caroline & Lepore, Caterina, 2023. "Macroprudential stress‑test models: a survey," Bank of England working papers 1037, Bank of England.
    8. Diekhof, Josefine & Krieger, Bastian & Licht, Georg & Rammer, Christian & Schmitt, Johannes & Stenke, Gero, 2021. "The impact of the Covid-19 crisis on innovation: First in-sights from the German business sector," ZEW Expert Briefs 21-06, ZEW - Leibniz Centre for European Economic Research.
    9. Carolina Correa-Caro & Leandro Medina & Marcos Poplawski-Ribeiro & Bennett Sutton, 2021. "Fiscal Stimulus and Firms’ Sales and Capital Expenditure During the Global Financial Crisis," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 63(3), pages 489-535, September.
    10. Albuquerque, Bruno, 2021. "Corporate debt booms, financial constraints and the investment nexus," Bank of England working papers 935, Bank of England.

  4. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.

    Cited by:

    1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    2. Amarda Cano, 2020. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, January -.
    3. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
    4. Amadxarif, Zahid & Brookes, James & Garbarino, Nicola & Patel, Rajan & Walczak, Eryk, 2019. "The language of rules: textual complexity in banking reforms," Bank of England working papers 834, Bank of England.
    5. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    6. Ryan Defina, 2021. "Machine Learning Methods: Potential for Deposit Insurance," IADI Fintech Briefs 3, International Association of Deposit Insurers.
    7. Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
    8. Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
    9. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    10. Guerra, Pedro & Castelli, Mauro & Côrte-Real, Nadine, 2022. "Machine learning for liquidity risk modelling: A supervisory perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 175-187.
    11. Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    12. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    13. Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
    14. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    15. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
    16. Tamara, Novian & Dwi Muchisha, Nadya & Andriansyah, Andriansyah & Soleh, Agus M, 2020. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms," MPRA Paper 105235, University Library of Munich, Germany.
    17. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    18. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    19. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    20. Zahner, Johannes & Baumgärtner, Martin, 2022. "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics 264019, Verein für Socialpolitik / German Economic Association.
    21. Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
    22. Andrei Shevelev & Maria Kvaktun & Kristina Virovets, 2021. "Effect of Monetary Policy on Investment in Russian Regions," Russian Journal of Money and Finance, Bank of Russia, vol. 80(4), pages 31-49, December.
    23. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    24. Fabio Zambuto & Simona Arcuti & Roberto Sabatini & Daniele Zambuto, 2021. "Application of classification algorithms for the assessment of confirmation to quality remarks," Questioni di Economia e Finanza (Occasional Papers) 631, Bank of Italy, Economic Research and International Relations Area.
    25. Joe McLaughlin & Nathan Palmer & Adam Minson & Eric Parolin, 2018. "The OFR Financial System Vulnerabilities Monitor," Working Papers 18-01, Office of Financial Research, US Department of the Treasury.
    26. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    27. Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.
    28. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    29. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    30. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series DP2019/03, Reserve Bank of New Zealand.
    31. James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
    32. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series 2614, European Central Bank.
    33. Dmytro Krukovets, 2020. "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24.
    34. Fabio Zambuto, 2021. "Quality checks on granular banking data: an experimental approach based on machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Micro data for the macro world, volume 53, Bank for International Settlements.
    35. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
    36. Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
    37. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
    38. Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
    39. Rohan Arora & Chen Fan & Guillaume Ouellet Leblanc, 2019. "Liquidity Management of Canadian Corporate Bond Mutual Funds: A Machine Learning Approach," Staff Analytical Notes 2019-7, Bank of Canada.
    40. Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
    41. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
    42. Emanuel Kohlscheen, 2021. "What does machine learning say about the drivers of inflation?," BIS Working Papers 980, Bank for International Settlements.
    43. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
    44. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    45. Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    46. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    47. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    48. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    49. Romain Plassard, 2020. "Making a Breach: The Incorporation of Agent-Based Models into the Bank of England's Toolkit," GREDEG Working Papers 2020-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    50. Leonard Sabetti & Ronald Heijmans, 2020. "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers 681, DNB.
    51. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    52. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
    53. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    54. Bholat, David & Brookes, James & Cai, Chris & Grundy, Katy & Lund, Jakob, 2017. "Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate," Bank of England working papers 688, Bank of England.
    55. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
    56. Andrea Carboni & Alessandro Moro, 2018. "Imputation techniques for the nationality of foreign shareholders in Italian firms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), External sector statistics: current issues and new challenges, volume 48, Bank for International Settlements.
    57. Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
    58. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    59. Andrew Clark, 2020. "A Pound Centric look at the Pound vs. Krona Exchange Rate Movement from 1844 to 1965," Economics Discussion Papers em-dp2020-22, Department of Economics, University of Reading.
    60. Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
    61. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.
    62. Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
    63. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
    64. Anil Savio Kavuri & Alistair Milne, 2019. "FinTech and the future of financial services: What are the research gaps?," CAMA Working Papers 2019-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  5. Cielinska, Olga & Joseph, Andreas & Shreyas, Ujwal & Tanner, John & Vasios, Michalis, 2017. "Gauging market dynamics using trade repository data: the case of the Swiss franc de-pegging," Bank of England Financial Stability Papers 41, Bank of England.

    Cited by:

    1. Jingzhi Chen & Charlie X. Cai & Robert Faff & Yongcheol Shin, 2022. "Nonlinear limits to arbitrage," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1084-1113, June.
    2. Fiedor, Paweł & Lapschies, Sarah & Orszaghova, Lucia, 2017. "Networks of counterparties in the centrally cleared EU-wide interest rate derivatives market," ESRB Working Paper Series 54, European Systemic Risk Board.
    3. Iman van Lelyveld, 2017. "The use of derivatives trade repository data: possibilities and challenges," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    4. Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
    5. Cenedese, Gino & Ranaldo, Angelo & Vasios, Michalis, 2018. "OTC premia," Bank of England working papers 751, Bank of England.
    6. Della Corte, Pasquale & Cenedese, Gino & Wang, Tianyu, 2020. "Currency Mispricing and Dealer Balance Sheets," CEPR Discussion Papers 15569, C.E.P.R. Discussion Papers.
    7. Fullwood, Jonathan & Massacci, Daniele, 2018. "Liquidity resilience in the UK gilt futures market: evidence from the order book," Bank of England working papers 744, Bank of England.
    8. Fiedor, Paweł, 2018. "Clearinghouse-Five: determinants of voluntary clearing in European derivatives markets," ESRB Working Paper Series 72, European Systemic Risk Board.
    9. Bardoscia, Marco & Ferrara, Gerardo & Vause, Nicholas & Yoganayagam, Michael, 2019. "Simulating liquidity stress in the derivatives market," Bank of England working papers 838, Bank of England.
    10. Karvik, Geir-Are & Noss, Joseph & Worlidge, Jack & Beale, Daniel, 2018. "The deeds of speed: an agent-based model of market liquidity and flash episodes," Bank of England working papers 743, Bank of England.
    11. Ezgi Deryol & Duygu Konukçu & Robert Szemere & Bruno Tissot, 2019. "Mind the data gap: commercial property prices for policy," IFC Reports 8, Bank for International Settlements.
    12. Fontana, Silvia Dalla & Holz auf der Heide, Marco & Pelizzon, Loriana & Scheicher, Martin, 2019. "The anatomy of the euro area interest rate swap market," SAFE Working Paper Series 255, Leibniz Institute for Financial Research SAFE.
    13. Okiriza Wibisono & Hidayah Dhini Ari & Anggraini Widjanarti & Alvin Andhika Zulen & Bruno Tissot, 2019. "The use of big data analytics and artificial intelligence in central banking – An overview," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    14. Francis Breedon & Louisa Chen & Angelo Ranaldo & Nicholas Vause, 2018. "Judgement Day: Algorithmic Trading Around the Swiss Franc Cap Removal," Working Papers on Finance 1808, University of St. Gallen, School of Finance.
    15. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    16. Mario Ascolese & Annalisa Molino & Grzegorz Skrzypczynski & Julius Cerniauskas & Sébastien Pérez-Duarte, 2017. "Euro-area derivatives markets: structure, dynamics and challenges," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    17. Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.
    18. José María Serena Garralda & Bruno Tissot, 2018. "Central banks and trade repositories derivatives data," IFC Reports 7, Bank for International Settlements.

  6. Joseph, Andreas & Osbat, Chiara, 2016. "How you export matters: the disassortative structure of international trade," Working Paper Series 1958, European Central Bank.

    Cited by:

    1. Vera Pirimova, 2021. "Structural Convergence of Bulgarian Foreign Trade and Exports to the Euro Area," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 93-117.
    2. Konstantins Benkovskis & Benjamin Bluhm & Elena Bobeica & Chiara Osbat & Stefan Zeugner, 2017. "What drives export market shares? It depends! An empirical analysis using Bayesian Model Averaging," Working Papers 2017/02, Latvijas Banka.

  7. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between financial and environmental networks in OECD countries," Papers 1501.04992, arXiv.org, revised Apr 2015.

    Cited by:

    1. Jian Wang & Jin-Chun Huang & Shan-Lin Huang & Gwo-Hshiung Tzeng & Ting Zhu, 2021. "Improvement Path for Resource-Constrained Cities Identified Using an Environmental Co-Governance Assessment Framework Based on BWM-mV Model," IJERPH, MDPI, vol. 18(9), pages 1-30, May.
    2. Franco Ruzzenenti & Francesco Picciolo & Andreas Papandreou, 2015. "A network analysis of the global energy market: an insight on the entanglement between crude oil and the world economy," Papers 1509.05894, arXiv.org, revised Sep 2015.
    3. Pietro Vozzella & Franco Ruzzenenti & Giampaolo Gabbi, 2019. "Energy and Environmental Flows: Do Most Financialised Countries within the Mediterranean Area Export Unsustainability?," Sustainability, MDPI, vol. 11(13), pages 1-15, July.

  8. Osbat, Chiara & Zollino, Francesco & Aiello, Giovanni & Bluhm, Benjamin & Buelens, Christian & Cavallini, Flavia & Joseph, Andreas & Leonte, Alexandru & Lommatzsch, Kirsten & Momchilov, Georgi & Giord, 2015. "Compendium on the diagnostic toolkit for competitiveness," Occasional Paper Series 163, European Central Bank.

    Cited by:

    1. E. Dhyne & C. Duprez & C. Fuss, 2015. "Main CompNet research results," Economic Review, National Bank of Belgium, issue iii, pages 103-116, December.
    2. Konstantins Benkovskis & Benjamin Bluhm & Elena Bobeica & Chiara Osbat & Stefan Zeugner, 2017. "What drives export market shares? It depends! An empirical analysis using Bayesian Model Averaging," Working Papers 2017/02, Latvijas Banka.
    3. Epede, Mesumbe Bianca & Wang, Daoping, 2022. "Competitiveness and upgrading in global value chains: A multiple-country analysis of the wooden furniture industry," Forest Policy and Economics, Elsevier, vol. 140(C).
    4. Camille Logeay & Heike Joebges, 2018. "Could a national wage rule stabilize the current account and functional income distribution in the Euro area?," FMM Working Paper 23-2018, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    5. Elena Bobeica & Olegs Tkacevs & Styliani Christodoulopoulou, 2016. "The role of price and cost competitiveness for intra- and extra-euro area trade of euro area countries," Working Papers 2016/04, Latvijas Banka.
    6. Doris Ritzberger-Grünwald & Josef Schreiner & Julia Wörz, 2017. "Competitiveness of CESEE EU Member States: recent trends and prospects," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/17, pages 31-41.
    7. Alexander Herzog-Stein & Heike Joebges & Torsten Niechoj & Ulrike Stein & Rudolf Zwiener, 2015. "Nur moderater Anstieg der Arbeitskosten in Deutschland. Arbeits- und Lohnstückkostenentwicklung 2014 und 1. Halbjahr 2015 im europäischen Vergleich," IMK Report 109-2015, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

  9. Andreas Joseph & Irena Vodenska & Eugene Stanley & Guanrong Chen, 2014. "Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics," Papers 1403.0848, arXiv.org.

    Cited by:

    1. Vodenska, Irena & Aoyama, Hideaki & Becker, Alexander P. & Fujiwara, Yoshi & Iyetomi, Hiroshi & Lungu, Eliza, 2021. "From stress testing to systemic stress testing: The importance of macroprudential regulation," Journal of Financial Stability, Elsevier, vol. 52(C).
    2. Mr. Alexei P Kireyev & Andrei Leonidov, 2016. "A Network Model of Multilaterally Equilibrium Exchange Rates," IMF Working Papers 2016/130, International Monetary Fund.
    3. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.

  10. Andreas Joseph & Stephan Joseph & Guanrong Chen, 2013. "Cross-border Portfolio Investment Networks and Indicators for Financial Crises," Papers 1306.0215, arXiv.org, revised Jan 2014.

    Cited by:

    1. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between financial and environmental networks in OECD countries," Papers 1501.04992, arXiv.org, revised Apr 2015.
    2. Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017. "Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    3. Xinxin Xu & Sheng Ma & Ziqiang Zeng, 2019. "Complex network analysis of bilateral international investment under de-globalization: Structural properties and evolution," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    4. Zhang, Shuhong & Wang, Lin & Liu, Zhixin & Wang, Xiaofan, 2016. "Evolution of international trade and investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 752-763.
    5. John Evans & Neil Allan & Neil Cantle, 2017. "A New Insight into the World Economic Forum Global Risks," Economic Papers, The Economic Society of Australia, vol. 36(2), pages 185-197, June.
    6. Marcos Duenas & Rossana Mastrandrea & Matteo Barigozzi & Giorgio Fagiolo, 2017. "Spatio-Temporal Patterns of the International Merger and Acquisition Network," LEM Papers Series 2017/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Siranova, Maria & Tiruneh, Menbere Workie & Fisera, Boris, 2021. "Creating the illicit capital flows network in Europe – Do the net errors and omissions follow an economic pattern?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 955-973.
    8. Li, Jingwei & Li, Shouwei, 2023. "Immunization of systemic risk in trade–investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    9. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
    10. Ismail, Mohd Sabri & Noorani, Mohd Salmi Md & Ismail, Munira & Razak, Fatimah Abdul & Alias, Mohd Almie, 2022. "Early warning signals of financial crises using persistent homology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    11. Billio, Monica & Casarin, Roberto & Costola, Michele & Pasqualini, Andrea, 2016. "An entropy-based early warning indicator for systemic risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 42-59.
    12. Andreas Joseph & Irena Vodenska & Eugene Stanley & Guanrong Chen, 2014. "Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics," Papers 1403.0848, arXiv.org.
    13. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2016. "Short term prediction of extreme returns based on the recurrence interval analysis," Papers 1610.08230, arXiv.org.
    14. João Amador & João Falcão Silva, 2017. "Banks international asset portfolios: optimality, linkages and resilience," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    15. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.

Articles

  1. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between Financial and Environmental Networks in OECD Countries," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.
    See citations under working paper version above.Sorry, no citations of articles recorded.

Chapters

  1. Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017. "Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

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 10 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-BIG: Big Data (4) 2017-09-10 2019-03-18 2019-03-18 2020-02-03
  2. NEP-CMP: Computational Economics (4) 2017-09-10 2019-03-18 2019-03-18 2020-02-03
  3. NEP-MAC: Macroeconomics (3) 2017-09-10 2020-01-13 2020-02-03
  4. NEP-ECM: Econometrics (2) 2017-09-10 2019-03-18
  5. NEP-GTH: Game Theory (2) 2019-03-18 2020-02-03
  6. NEP-MON: Monetary Economics (2) 2017-09-10 2020-02-03
  7. NEP-MST: Market Microstructure (2) 2017-01-15 2019-11-18
  8. NEP-NET: Network Economics (2) 2013-06-09 2014-03-15
  9. NEP-BAN: Banking (1) 2013-06-09
  10. NEP-BEC: Business Economics (1) 2020-01-13
  11. NEP-CBA: Central Banking (1) 2017-09-10
  12. NEP-CFN: Corporate Finance (1) 2020-01-13
  13. NEP-ETS: Econometric Time Series (1) 2019-03-18
  14. NEP-FDG: Financial Development and Growth (1) 2020-02-03
  15. NEP-FMK: Financial Markets (1) 2017-01-15
  16. NEP-FOR: Forecasting (1) 2014-03-15
  17. NEP-INT: International Trade (1) 2016-10-02
  18. NEP-RMG: Risk Management (1) 2020-02-03

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