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Middle East and Central Asia > Qatar

You are looking at 1 - 3 of 3 items for :

  • Type: Journal Issue x
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Cover IMF Working Papers
Completing the Market: Generating Shadow CDS Spreads by Machine Learning
Nan Hu
,
Jian Li
, and
Alexis Meyer-Cirkel
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.
Keywords:
WP; firm; default probability; failure intensity; firm size proxy; Credit default swaps; Prediction; Machine Learning methods; input variable; contracts data; recovery rate; importance probability matrix; credit measure Distance to default; coverage ratio; equity market variable; macroeconomic variable; market perception; machine learning method; Credit default swap; Machine learning; Credit risk; Credit ratings; Stock markets; North America
Cover IMF Working Papers
FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk
Majid Bazarbash
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Keywords:
WP; ML model; bears risk; machine learning technique; ML analysis; ML evaluation; FinTech Credit; Financial Inclusion; Machine Learning; Credit Risk Assessment; ML analyst; credit risk driver; FinTech credit company; credit scoring; supervised machine learning model; capital structure; borrower default; neural network; Credit risk; Credit; Credit ratings; Loans; Global
Cover IMF Staff Country Reports
Qatar: 2010 Article IV Consultation-Staff Report; Staff Statement and Supplement; Public Information Notice on the Executive Board Discussion; and Statement by the Executive Director for Qatar
International Monetary Fund
Qatar has weathered the global economic crisis well. Enhancement of liquefied natural gas capacity (LNG), government support to the banking system, and increase in public spending sustain the high growth rates. There is a need to monitor aggregate demand to prevent the resurgence of inflation pressures. The creation of a financial stability unit and the results of recent stress tests show the banking system’s resilience to market and credit risks. The coordination among data providers and intention to seek technical assistance in consumer price index are noted.
Keywords:
ISCR; CR; headline CPI inflation; QCB shares staffs assessment; staff appraisal; headline inflation; Headline consumer price inflation; bank credit to the private sector; Commercial banks; Credit; Credit bureaus; Global