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Predicting Sovereign Debt Crises Using Artificial Neural Networks

Author : Marco Fioramanti

Publisher :

Page : 32 pages

File Size : 32,33 MB

Release : 2006

Category :

ISBN :

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Book Description

Recent episodes of financial crises have revived the interest in developing models that are able to timely signal their occurrence. The literature has developed both parametric and non parametric models to predict these crises, the so called Early Warning Systems. Using data related to sovereign debt crises occurred in developing countries from 1980 to 2004, this paper shows that a further progress can be done applying a less developed non-parametric method, i.e. Artificial Neural Networks (ANN). Thanks to the high flexibility of neural networks and to the Universal Approximation Theorem an ANN based early warning system can, under certain conditions, outperform more consolidated methods.



Predicting Sovereign Debt Crises

Author : Paolo Manasse

Publisher : International Monetary Fund

Page : 42 pages

File Size : 32,50 MB

Release : 2003-11-01

Category : Business & Economics

ISBN : 1451875258

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Book Description

We develop an early-warning model of sovereign debt crises. A country is defined to be in a debt crisis if it is classified as being in default by Standard & Poor's, or if it has access to nonconcessional IMF financing in excess of 100 percent of quota. By means of logit and binary recursive tree analysis, we identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt-crisis episode one year in advance. The logit model predicts 74 percent of all crises entries while sending few false alarms, and the recursive tree 89 percent while sending more false alarms.



Predicting Sovereign Debt Crises

Author : Paolo Manasse

Publisher :

Page : 41 pages

File Size : 35,32 MB

Release : 2006

Category :

ISBN :

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Book Description

We develop an early-warning model of sovereign debt crises. A country is defined to be in a debt crisis if it is classified as being in default by Standard amp; Poor's, or if it has access to nonconcessional IMF financing in excess of 100 percent of quota. By means of logit and binary recursive tree analysis, we identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt-crisis episode one year in advance. The logit model predicts 74 percent of all crises entries while sending few false alarms, and the recursive tree 89 percent while sending more false alarms.



Predicting Fiscal Crises: A Machine Learning Approach

Author : Klaus-Peter Hellwig

Publisher : International Monetary Fund

Page : 66 pages

File Size : 21,91 MB

Release : 2021-05-27

Category : Business & Economics

ISBN : 1513573586

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Book Description

In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.





“Rules of Thumb” for Sovereign Debt Crises

Author : Paolo Manasse

Publisher : INTERNATIONAL MONETARY FUND

Page : 32 pages

File Size : 39,75 MB

Release : 2005-03-01

Category : Business & Economics

ISBN : 9781451860610

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Book Description

This paper contains an empirical investigation of the set of economic and political conditions that are associated with a likely occurrence of a sovereign debt crisis. We use a new statistical approach (Binary Recursive Tree) that allows us to derive a collection of "rules of thumb" that help identify the typical characteristics of defaulters. We find that not all crises are equal: they differ depending on whether the government faces insolvency, illiquidity, or various macroeconomic risks. We also characterize the set of fundamentals that can be associated with a relatively "risk free" zone. This classification is important for discussing appropriate policy options to prevent crises and improve response time and prediction.



Modeling Sovereign Debt Crises Using Panels

Author : Ana-Maria Fuertes

Publisher :

Page : 41 pages

File Size : 26,68 MB

Release : 2009

Category :

ISBN :

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Book Description

This paper compares rival sovereign default models that differ in how country-, region- and time-specific effects are treated. The quality of the models is gauged using inference-based criteria and the plausibility of estimates. An out-of-sample forecast evaluation framework is deployed based on statistical- and economic-loss functions, naive benchmarks and equal-predictive-ability tests. The inference metrics overwhelmingly favor more complex models that allow for time-varying country heterogeneity. However, simplicity beats complexity in terms of forecasting. Pooled logit models that simply control either for regional heterogeneity or for time effects produce the most accurate forecasts and outperform the naive models.



Handbook of Research on Computational Science and Engineering: Theory and Practice

Author : Leng, J.

Publisher : IGI Global

Page : 701 pages

File Size : 22,96 MB

Release : 2011-10-31

Category : Technology & Engineering

ISBN : 161350117X

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Book Description

By using computer simulations in research and development, computational science and engineering (CSE) allows empirical inquiry where traditional experimentation and methods of inquiry are difficult, inefficient, or prohibitively expensive. The Handbook of Research on Computational Science and Engineering: Theory and Practice is a reference for interested researchers and decision-makers who want a timely introduction to the possibilities in CSE to advance their ongoing research and applications or to discover new resources and cutting edge developments. Rather than reporting results obtained using CSE models, this comprehensive survey captures the architecture of the cross-disciplinary field, explores the long term implications of technology choices, alerts readers to the hurdles facing CSE, and identifies trends in future development.



Data Science for Economics and Finance

Author : Sergio Consoli

Publisher : Springer Nature

Page : 357 pages

File Size : 30,67 MB

Release : 2021

Category : Application software

ISBN : 3030668916

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Book Description

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.



New Dynamics in Banking and Finance

Author : Nesrin Özataç

Publisher : Springer Nature

Page : 209 pages

File Size : 44,94 MB

Release : 2022-04-27

Category : Business & Economics

ISBN : 3030937259

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Book Description

This volume presents current developments in the fields of banking and finance from an international perspective. Featuring contributions from the 5th International Conference on Banking and Finance Perspectives (ICBFP), this volume serves as a valuable forum for discussing current issues and trends in the banking and financial sectors, especially in light of the global economic challenges triggered by financial institutions. Using the latest theoretical models, new perspectives are brought to topics such as the global financial markets, international banking and finance, microfinance, fintech, and corporate finance. Offering an opportunity to explore the challenges of a rapidly changing industry, this volume will be of interest to academics, policy makers, and scholars in the fields of banking, insurance, and finance.