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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 29 items for :

  • Type: Journal Issue x
  • Monetary Policy, Central Banking, and the Supply of Money and Credit: General x
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Cover Global Financial Stability Report, October 2024
Global Financial Stability Report, October 2024
Steadying the Course: Uncertainty, Artificial Intelligence, and Financial Stability
International Monetary Fund. Monetary and Capital Markets Department

Abstract

Chapter 1 shows that although near-term financial stability risks have remained contained, mounting vulnerabilities could worsen future downside risks by amplifying shocks, which have become more probable because of the widening disconnect between elevated economic uncertainty and low financial volatility. Chapter 2 presents evidence that high macroeconomic uncertainty can threaten macrofinancial stability by exacerbating downside tail risks to markets, credit supply, and GDP growth. These relationships are stronger when debt vulnerabilities are elevated, or financial market volatility is low (during episodes of a macro-market disconnect). Chapter 3 assesses recent developments in AI and Generative AI and their implications for capital markets. It presents new analytical work and results from a global outreach to market participants and regulators, delineates potential benefits and risks that may arise from the widespread adoption of these new technologies, and makes suggestions for policy responses.

Keywords:
financial stability; vulnerabilities; uncertainty; risk; artificial intelligence; capital markets; tail risk; uncertainty index; monetary policy; mortgage rate; market liquidity risk; fiscal policy uncertainty; Emerging and frontier financial markets; Financial sector stability; Sovereign bonds; Credit; Global; Middle East; Europe; Central and Eastern Europe; South America
Cover IMF Staff Country Reports
Republic of Latvia: Selected Issues
International Monetary Fund. African Dept.
Selected Issues
Keywords:
term premium; premium in the Baltic; staff team of the International Monetary Fund; nominal NPLS; Latvia's lending rate; Loans; Credit; Big data; Nonperforming loans; Bank credit; Baltics
Cover Departmental Papers
Assessing Banking Sector Vulnerabilities in the Gulf Cooperation Council in the Wake of COVID-19
Abdullah Al-Hassan
,
Imen Benmohamed
,
Aidyn Bibolov
,
Giovanni Ugazio
, and
Ms. Tian Zhang
The Gulf Cooperation Council region faced a significant economic toll from the COVID-19 pandemic and oil price shocks in 2020. Policymakers responded to the pandemic with decisive and broad measures to support households and businesses and mitigate the long-term impact on the economy. Financial vulnerabilities have been generally contained, reflecting ongoing policy support and the rebound in economic activity and oil prices, as well as banks entering the COVID-19 crisis with strong capital, liquidity, and profitability. The banking systems remained well-capitalized, but profitability and asset quality were adversely affected. Ongoing COVID-19 policy support could also obscure deterioration in asset quality. Policymakers need to continue to strike a balance between supporting recovery and mitigating risks to financial stability, including ensuring that banks’ buffers are adequate to withstand prolonged pandemic and withdrawal of COVID-related policy support measures. Addressing data gaps would help policymakers to further assess vulnerabilities and mitigate sectoral risks.
Keywords:
COVID-19; banking performance; corporates performance; sovereign-banks-economy nexus; bank asset quality; oil price shock; policy support; assessing banking sector vulnerability; C. bank assets quality; Financial statements; Commercial banks; Loans; Credit; Global; Middle East
Cover IMF Working Papers
Fintech Credit Risk Assessment for SMEs: Evidence from China
Yiping Huang
,
Ms. Longmei Zhang
,
Zhenhua Li
,
Han Qiu
,
Tao Sun
, and
Xue Wang
Promoting credit services to small and medium-size enterprises (SMEs) has been a perennial challenge for policy makers globally due to high information costs. Recent fintech developments may be able to mitigate this problem. By leveraging big data or digital footprints on existing platforms, some big technology (BigTech) firms have extended short-term loans to millions of small firms. By analyzing 1.8 million loan transactions of a leading Chinese online bank, this paper compares the fintech approach to assessing credit risk using big data and machine learning models with the bank approach using traditional financial data and scorecard models. The study shows that the fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages. BigTech’s proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow. Furthermore, the fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers. With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.
Keywords:
WP; credit history; Fintech firm; house ownership; internet company; real-time customer rating; Fintech lending; Credit risk assessment; Big data; Machine learning; micro firm; firms' access; medium-size enterprise; firm size; adverse selection; firm's real-time customer rating; fixed cost; information advantage; BigTech company; MYbank loan; Fintech; Credit risk; Loans; Bank credit; b. firm location; fintech approach; cash flows
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
Machine Learning and Causality: The Impact of Financial Crises on Growth
Mr. Andrew J Tiffin
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.
Keywords:
WP; machine-learning literature; instrumental-variables approach; treatment variable; confidence interval; ML technique; Supervised machine learning; causal inference; policy evaluation; counterfactual prediction; randomized experiments; treatment effects; banking crisis; financial crisis; B. machine learning; machine learning tool; machine-learning modification; RF algorithm; machine-learning model; Machine learning; Exchange rate flexibility; Global
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
Kenya: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper analyzes Kenya’s success in boosting financial inclusion. Kenya has become a regional and global leader in mobilizing new technologies to advance financial inclusion, poverty reduction, and growth. The rapid progress of financial inclusion in Kenya has been a result of a friendly environment for the absorption of information technology, dynamic local banks, and open and stable regulations. Advances in financial inclusion over the past 10 years have allowed Kenyans to reap many of the benefits of financial access at a much faster pace than the typical cycle of financial deepening in low- and middle-income countries. Mobile financial services have lowered the transaction cost of remittances, allowing Kenyan households to smooth consumption in the face of shocks and significantly reducing poverty.
Keywords:
ISCR; CR; Kenya; interest rate; interest rate control; money; bank; NFCS credit; lending rate; Kenya's NFCS; foreign currency; transaction cost; remittance transaction; Interest rate policy; Double taxation; Financial inclusion; Credit; Bank credit; Africa; Global
Cover IMF Staff Country Reports
Qatar: 2018 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Qatar
International Monetary Fund. Middle East and Central Asia Dept.
This 2018 Article IV Consultation highlights that Qatar’s growth performance remains resilient. The direct economic and financial impact of the diplomatic rift between Qatar and some countries in the region has been manageable. Nonhydrocarbon real GDP growth is estimated to have moderated to about 4 percent in 2017 owing to on-going fiscal consolidation and the effect of the diplomatic rift. Headline inflation remains subdued, primarily owing to lower rental prices. The near-term growth outlook is broadly positive. Overall, GDP growth of 2.6 percent is projected for 2018. Inflation is expected to peak at 3.9 percent in 2018 before easing to 2.2 percent in the medium term. The underlying fiscal position continues to improve.
Keywords:
ISCR; CR; GDP; near-term growth outlook; GDDS summary table II data coverage; growth; Non-hydrocarbon real GDP growth; private sector engagement; growth performance; real estate price index; liquidity profile; hydrocarbon price; Commercial banks; Credit; Fiscal stance; Global; Middle East; North Africa; East Africa
Cover IMF Staff Country Reports
Qatar: Selected Issues
International Monetary Fund. Middle East and Central Asia Dept.
This Selected Issues paper analyzes the performance and vulnerabilities of Qatar’s nonfinancial corporate (NFC) sector. Qatar’s NFC sector is sizable in terms of the overall share of economic activity. The total turnover of these companies was US$ 28 billion in 2016. Assets of listed and non-listed NFCs in Qatar were estimated at about 115 percent of non-hydrocarbon GDP in 2016. Although profitability of Qatari corporates, as measured by Return on Equity and Return on Assets, has declined, it is still high. Qatari companies remain resilient in the face of moderate to severe interest and earnings shocks, as median Interest Coverage Ratio of Qatari firms remains well above 1. The impact of these shocks on debt-at-risk and firms-at-risk is also limited.
Keywords:
ISCR; CR; private sector; earnings; credit growth; bank; NFC assets; earnings shock; IMF staff calculation; Qatari NFC sector; equity price; Qatar's Interest Coverage Ratio; Nonperforming loans; Oil prices; Credit; Stress testing; Commercial banks