دانلود مقاله توزیع تمرکز ریسک اعتباری در بخش خاص اقتصاد و سرمایه اقتصادی قبل و حین کرونا
خلاصه
1. مقدمه
2 اندازه گیری ریسک تمرکز بخش
3 بررسی تحقیقات در مورد ریسک بخشی و تمرکز بخش تجاری
4 روش شناسی: مدل ساختاری چند عاملی
5 داده
6 نتایج: ترکیب پورتفولیو
7 نتیجه گیری و پیامدهای سیاست
منابع
Abstract
1 Introduction
2 Measurement of sector concentration risk
3 Review of research on sectoral risk and business‑sector concentration
4 Methodology: the multi‑factor structural model
5 Data
6 Results: portfolio composition
7 Conclusion and policy implications
References
چکیده
هدف از کار زیربنای این مقاله، ردیابی تکامل ریسک دنباله در پرتفوی NPL بانک‌ها در شرایط عادی و بدترین شرایط (قبل و در طول همه‌گیری کووید-19) و برآورد تأثیر ریسک تمرکز بخش بر روی است. مقادیر سرمایه اقتصادی نتایج بیشتر به تجزیه و تحلیل بخش‌های مختلف با هدف تعیین ریسک‌ترین بخش‌ها اجازه می‌دهد. این مطالعه از یک مدل ساختاری چند عاملی استفاده می‌کند، با توجه به اینکه هر بخش تحت تأثیر یک عامل ریسک سیستماتیک متفاوت است، به طوری که دارایی‌های وام گیرندگان از همان بخش به طور قابل توجهی همبستگی دارند، حتی اگر همبستگی بین بخش‌ها کم باشد. این تحقیق در واقع به دنبال توسعه بیشتر روش ارائه شده توسط دولمان و ماشلین در سال 2006 بوده است - در جهت بهبود دقت برآوردهای اقتصادی-سرمایه، به لطف ابزارهای جایگزین برای ترسیم ماتریس همبستگی عوامل بخشی. تجزیه و تحلیل تجربی بر اساس داده های فردی از گزارش احتیاطی تحت بانک ملی لهستان و همچنین داده های بازار بود. نتایج نشان می‌دهد که در دوره 2015-2017، خطر دم افزایش یافته و به دنبال آن شروع کاهش می‌یابد. در مورد هدف دوم مقاله، حمایت از این ایده وجود دارد که سرمایه اقتصادی ممکن است در جایی که تمرکز بخش در پرتفوی یک بانک به حساب می‌آید افزایش یابد. مشخص شده است که خطر دم در بخش‌های ساخت و ساز و املاک متمرکز است و خدمات اقامتی و غذایی در طول همه‌گیری ناپایدارتر می‌شوند. بنابراین کانالی برای انتقال ریسک بین بخش های مالی و شرکتی وجود دارد. با تشکر از کار انجام شده، ما درک بهتری از تأثیر تمرکز بخشی فعالیت‌های وام دهی بانک‌ها بر سطح ریسک داریم، با امکان استفاده از این برنامه در زمان انجام تست‌های استرس، و توصیه‌های نظارتی از سوی سازمان نظارت مالی لهستان. فرموله شده است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The aim of the work underpinning this paper has been to track the evolution of tail risk in banks’ NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Düllmann and Masschelein in 2006—in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015–2017 period, as followed by the onset of a decline. Where the paper’s second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks’ lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland’s Financial Supervision Authority are formulated.
Introduction
Concentration risk is one of the specific types of risk in banking whose inappropriate management, non-subjection to appropriate policies and regulations, or incomplete measurement may all give banks financial problems. Examples here might be the concentration of bank lending in the energy sector in Texas and Oklahoma in the 1980s; as well as over-exposure to the construction and development sector in Sweden in the early 1990s, and in Spain and Ireland in 2000. The materialisation of concentration risk during the global financial crisis of 2008–2009 was in turn a source of huge losses for European and global banks that left them weakened economically, financially, and as regards operational security. Such circumstances ensure the huge importance from a macroprudential point of view of concentration risk being measured.
The effects of the COVID-19 pandemic will hit home just as soon as we see restriction of the emergency measures that governments, central banks and regulators across Europe have introduced. That said, it is reasonable to expect that banks struggling currently with declining interest margins and low profitability, among other things, will prove to be most affected by the crisis, where there is marked concentration in sectors more affected by the pandemic, i.e. hospitality, transport and some manufacturing sub-sectors; as well as in those other sectors already characterised by a high level of non-performing loans on account of COVID. The pandemic may emerge as concentrating bank exposures to the domestic government sector to an excessive degree. This is to say nothing of Poland’s energy transformation, with its requirement for a very considerable amount of investment (given costs at the level of 1.6 trillion PLN estimated for 2021–2040.
Conclusion and policy implications
This article examines the extent to which sector concentration contributes to growth in economic capital, as well as assessing the effectiveness of analytical methods in measuring sector concentration risk. Unexpected losses were estimated using the multivariate model described in the articles by Düllmann and Masschelein (2006), (2007), and Düllmann and Puzanova (2011), as derived indirectly from the model after Merton (1974). Additionally, this article has focused on the development of the methodology proposed by Düllmann and Masschelein, in the direction of improved accuracy of economic-capital estimation, thanks to alternate ways of mapping the sector factor correlation matrix. Moreover, the article contains a detailed description of the methodology applied.
It is worth noting that the study used a unique database, inter alia with data concerning bank exposures to non-financial sector enterprises, corporate PDs and model LGD estimates (based on historical data). Portfolios were characterised by reference to their degree of diversification, with aggregate, relative exposures of domestic banking represented sector by sector. Exposures from the financial sector were not included, in recognition of that sector’s specific nature. In addition, it was possible to note the high degree of heterogeneity characterising sectors of industry, hence the study’s more-precise division into industrial groups.