Hungary’s Corporate Sector Risk: A Machine Learning Approach

August 13, 2024

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Format: Chicago

Jakree Koosakul, and Xuege Zhang. Hungary’s Corporate Sector Risk: A Machine Learning Approach, (USA: International Monetary Fund, 2024) accessed November 21, 2024

Summary

In recent years, Hungary’s non-financial corporations were confronted with multiple shocks, ranging from the pandemic and rising geopolitical tensions to the historic tightening of domestic monetary policy. Employing machine learning techniques, this paper examines the determinants of Hungarian listed firms’ credit risk evolution over this period. Our analysis shows that both firm-specific and macroeconomic factors played a role in explaining the observed rise in firms’ default probability at onset of the pandemic, although Hungarian corporates proved broadly resilient, with risk indicators quickly improving a year after. Firms’ credit risk rose again in 2022, however, as both long-term interest rates and sovereign risk premia sharply increased, despite continued improvements in firms’ financial ratios. This development merits continued monitoring, particularly since a significant portion of corporate loans are set to mature within the next few years and could be repriced at higher interest rates.

Subject: Business enterprises, Central bank policy rate, Corporate sector, COVID-19, Credit default swap, Credit risk, Economic sectors, Financial regulation and supervision, Financial services, Health, Long term interest rates, Machine learning, Market risk, Monetary policy, Monetary tightening, Money, Technology

Keywords: Business enterprises, Central bank policy rate, Corporate sector, Corporate sector, COVID-19, COVID-19 pandemic, Credit default swap, Credit risk, Credit risk, Geopolitical tension, Hungary, Liquidity requirements, Loans, Long term interest rates, Machine learning, Machine learning, Market risk, Monetary policy, Monetary tightening, Probability of default, Sovereign risk

Publication Details

Pages:

12
Volume:

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DOI:

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Issue:

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Series:

Selected Issues Paper No. 2024/038
Stock No:

SIPEA2024038
ISBN:

9798400287916
ISSN:

2958-7875

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