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Author: Nan Hu
Publisher: International Monetary Fund
ISBN: 1513524089
Category : Business & Economics
Languages : en
Pages : 37
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Book Description
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
Author: Nan Hu
Publisher: International Monetary Fund
ISBN: 1513524089
Category : Business & Economics
Languages : en
Pages : 37
Get Book
Book Description
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
Author: Nan Hu
Publisher: International Monetary Fund
ISBN: 1513525182
Category : Business & Economics
Languages : en
Pages : 37
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Book Description
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
Author:
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ISBN:
Category :
Languages : en
Pages : 140
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Book Description
Popular Mechanics inspires, instructs and influences readers to help them master the modern world. Whether it’s practical DIY home-improvement tips, gadgets and digital technology, information on the newest cars or the latest breakthroughs in science -- PM is the ultimate guide to our high-tech lifestyle.
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Languages : en
Pages : 104
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Book Description
The Advocate is a lesbian, gay, bisexual, transgender (LGBT) monthly newsmagazine. Established in 1967, it is the oldest continuing LGBT publication in the United States.
Author: Jiri Podpiera
Publisher: International Monetary Fund
ISBN: 1455201367
Category : Business & Economics
Languages : en
Pages : 33
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Book Description
This paper attempts to identify the fundamental variables that drive the credit default swaps during the initial phase of distress in selected European Large Complex Financial Institutions (LCFIs). It uses yearly data over 2004 - 08 for 29 European LCFIs. The results from a dynamic panel data estimator show that LCFIs’ business models, earnings potential, and economic uncertainty (represented by market expectations about the future risks of a particular LCFI and market views on prospects for economic growth) are among the most significant determinants of credit risk. The findings of the paper are broadly consistent with those of the literature on bank failure, where the determinants of the latter include the entire CAMELS structure - that is, Capital Adequacy, Asset Quality, Management Quality, Earnings Potential, Liquidity, and Sensitivity to Market Risk. By establishing a link between the financial and market fundamentals of LCFIs and their CDS spreads, the paper offers a potential tool for fundamentals-based vulnerability and early warning system for LCFIs.
Author: Mr.Andrew J Tiffin
Publisher: International Monetary Fund
ISBN: 1513518305
Category : Computers
Languages : en
Pages : 30
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Book Description
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Author: International Monetary Fund. Monetary and Capital Markets Department
Publisher: International Monetary Fund
ISBN: 1616352477
Category : Business & Economics
Languages : en
Pages : 94
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Book Description
The April 2012 Global Financial Stability Report assesses changes in risks to financial stability over the past six months, focusing on sovereign vulnerabilities, risks stemming from private sector deleveraging, and assessing the continued resilience of emerging markets. The report probes the implications of recent reforms in the financial system for market perception of safe assets, and investigates the growing public and private costs of increased longevity risk from aging populations.
Author: Bertrand Candelon
Publisher: International Monetary Fund
ISBN: 1455225061
Category : Business & Economics
Languages : en
Pages : 30
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Book Description
This paper examines the spillover effects of sovereign rating news on European financial markets during the period 2007-2010. Our main finding is that sovereign rating downgrades have statistically and economically significant spillover effects both across countries and financial markets. The sign and magnitude of the spillover effects depend both on the type of announcements, the source country experiencing the downgrade and the rating agency from which the announcements originates. However, we also find evidence that downgrades to near speculative grade ratings for relatively large economies such as Greece have a systematic spillover effects across Euro zone countries. Rating-based triggers used in banking regulation, CDS contracts, and investment mandates may help explain these results.
Author: Andreas Sudmann
Publisher: transcript Verlag
ISBN: 3839447194
Category : Social Science
Languages : en
Pages : 335
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Book Description
After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?
Author: M. Ayhan Kose
Publisher: World Bank Publications
ISBN: 1464815453
Category : Business & Economics
Languages : en
Pages : 403
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Book Description
The global economy has experienced four waves of rapid debt accumulation over the past 50 years. The first three debt waves ended with financial crises in many emerging market and developing economies. During the current wave, which started in 2010, the increase in debt in these economies has already been larger, faster, and broader-based than in the previous three waves. Current low interest rates mitigate some of the risks associated with high debt. However, emerging market and developing economies are also confronted by weak growth prospects, mounting vulnerabilities, and elevated global risks. A menu of policy options is available to reduce the likelihood that the current debt wave will end in crisis and, if crises do take place, will alleviate their impact.
