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Early warning systems for sovereign debt crises: The role of heterogeneity

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Cited by:

  1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
  2. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
  3. Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
  4. Dieter Gerdesmeier & Hans‐Eggert Reimers & Barbara Roffia, 2010. "Asset Price Misalignments and the Role of Money and Credit," International Finance, Wiley Blackwell, vol. 13(3), pages 377-407, December.
  5. Bartolucci, Francesco & Nigro, Valentina, 2007. "Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3470-3483, April.
  6. Cheng, Xian & Zhao, Haichuan, 2019. "Modeling, analysis and mitigation of contagion in financial systems," Economic Modelling, Elsevier, vol. 76(C), pages 281-292.
  7. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
  8. Smith, Jonathan Acosta & Grill, Michael & Lang, Jan Hannes, 2017. "The leverage ratio, risk-taking and bank stability," Working Paper Series 2079, European Central Bank.
  9. Han-Hsing Lee & Kuanyu Shih & Kehluh Wang, 2016. "Measuring sovereign credit risk using a structural model approach," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1097-1128, November.
  10. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
  11. Bellini, Tiziano & Riani, Marco, 2012. "Robust analysis of default intensity," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3276-3285.
  12. Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
  13. Ms. Svetlana Cerovic & Mrs. Kerstin Gerling & Andrew Hodge & Mr. Paulo A Medas, 2018. "Predicting Fiscal Crises," IMF Working Papers 2018/181, International Monetary Fund.
  14. Quentin Bro de Comères, 2022. "Predicting European Banks Distress Events: Do Financial Information Producers Matter?," Working Papers hal-03752678, HAL.
  15. Chiaramonte, Laura & Casu, Barbara, 2017. "Capital and liquidity ratios and financial distress. Evidence from the European banking industry," The British Accounting Review, Elsevier, vol. 49(2), pages 138-161.
  16. Sarlin, Peter & Ramsay, Bruce A., 2014. "Ending over-lending : Assessing systemic risk with debt to cash flow," Research Discussion Papers 11/2014, Bank of Finland.
  17. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
  18. 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.
  19. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
  20. Arazmuradov, Annageldy, 2016. "Assessing sovereign debt default by efficiency," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 100-113.
  21. Roberto Savona & Marika Vezzoli, 2012. "Multidimensional Distance‐To‐Collapse Point And Sovereign Default Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 205-228, October.
  22. Tuomas Antero Peltonen & Michela Rancan & Peter Sarlin, 2019. "Interconnectedness of the banking sector as a vulnerability to crises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 963-990, April.
  23. Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
  24. Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
  25. Sarlin, Peter & Ramsay, Bruce A., 2014. "Ending over-lending: Assessing systemic risk with debt to cash flow," Bank of Finland Research Discussion Papers 11/2014, Bank of Finland.
  26. S Figini & P Giudici, 2011. "Statistical merging of rating models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1067-1074, June.
  27. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
  28. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
  29. Thangjam Rajeshwar Singh, 2011. "An ordered probit model of an early warning system for predicting financial crisis in India," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 185-201, Bank for International Settlements.
  30. repec:zbw:bofrdp:2014_011 is not listed on IDEAS
  31. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  32. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
  33. 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.
  34. Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
  35. repec:zbw:bofitp:2011_018 is not listed on IDEAS
  36. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  37. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
  38. Sarlin, Peter & Ramsay, Bruce A., 2015. "Ending over-lending: assessing systemic risk with debt to cash flow," Working Paper Series 1769, European Central Bank.
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