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Network science and contagion for private credit

Network science maps the links between private credit funds, banks, insurers, sponsors and borrowers. On this platform it shows shared loans, common lenders and funding dependencies through which stress could travel.

Ajukan pertanyaan Back to STEM foundation of Private Credit
01Daftar

Network science and contagion for private credit on the record.

Catatan metodeMetode STEM
  1. MetodeIlmu jaringan dan penularan
  3. Diterapkan diKredit Swasta
  6. Halaman kanonisSTEM Commons
02Permohonan

What network science and contagion does here.

Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial Sistem. In capital markets and investment and financial stability, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on STEM Commons.

Shared loan networks

Matching borrowers across fund filings reveals how many funds hold the same loans and how distress at one borrower spreads across lenders.

Funding dependencies

Bank facilities to funds and insurer allocations to managers are mapped to show which institutions finance which parts of the market.

Stress propagation

Simulated borrower defaults are traced through fund leverage and investor links to estimate second-round effects on banks and insurers.

03Uji tekanan

Bagaimana guncangan pada aset menjadi kerugian sistem.

Ekuitas yang hilang dari sistem setelah penularan antarbank, dibandingkan dengan pukulan awal, seiring berubahnya leverage, keterhubungan, dan tingkat pemulihan, yang merupakan inti dari Uji tekananmakroprudensial. Model pengajaran dengan nilai ilustratif yang dinyatakan, bukan prediksi.

Kendali

Penurunan aset eksternal
2.0%
Leverage (aset terhadap ekuitas)
10 kali
Porsi aset antarbank
10%
Pemulihan atas tagihan antarbank
40%
Bank dalam sistem
20
Bank inti
20% bank

Pembacaan

Ekuitas yang hilang akibat guncangan awal
18.0%
Ekuitas yang hilang setelah penularan
45.0%
Ditambahkan oleh penularan (DebtRank)
27.0 poin
Bank yang gagal bayar
0
Angka, metode, dan sumber
PutaranEkuitas sistem yang hilang (%)Bank yang gagal bayar
018.000
128.800
235.280
339.170
441.500
542.900
643.740
744.240
844.550
944.730
1044.840
1144.900
1244.940
1344.960
1444.980
1544.990
1644.990
1745.000
1845.000
1945.000
2045.000
2145.000
2245.000
2345.000
2445.000
2545.000
2645.000
2745.000
2845.000
2945.000
3045.000

Metode

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. DebtRank Diferensial Bardoscia and others (2015), PLOS ONE 10
  3. Penularan dalam jaringan keuangan Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Kliring dalam jaringan keuangan Eisenberg and Noe (2001), Management Science 47
  5. Kipas ketidakpastian (Monte Carlo parametrik) Spiegelhalter, Pearson dan Short (2011), Memvisualisasikan ketidakpastian tentang masa depan, Science 333
  6. Sensitivitas satu per satu (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

Bagaimana guncangan pada aset menjadi kerugian sistem. Model pengajaran ilustratif: model ini berjalan pada nilai yang ditetapkan oleh kontrol, bukan pada data terukur. Metode: DebtRank (Battiston dkk. (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia dkk. (2015), PLOS ONE 10); penularan dalam jaringan keuangan (Gai dan Kapadia (2010), Proc. R. Soc. A 466).

Di seluruh enam pilar, dipimpin oleh Nexus Consortium

04Data

Apa yang digunakan metode ini.

Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in register sumber.

DataPeranLabel
BDC holdingsLoan linksStatistik yang diterbitkan
Supervisory exposure dataFunding linksStatistik yang diterbitkan
Insurer statutory filingsInvestor linksStatistik yang diterbitkan
05Eksposur, kerugian, dan modal

What network science and contagion for private credit means for exposure, loss and capital.

Tampilan eksposur, kerugian, skenario, dan modal yang akan diminta oleh pejabat risiko, otoritas pengawas, atau kementerian keuangan, beserta kerangka pelaporan yang mereka gunakan. Strukturnya mengikuti praktik pengawasan dan aktuaria. Nilai bersifat ilustratif: tidak pernah merupakan harga, peringkat, atau saran.

Saluran transmisi

How a shock in network science and contagion for private credit reaches credit, market, underwriting, liquidity and operational risk, and feeds back.

Channel structure after the Basel Committee’s work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
Skenario iklim NGFS · Network for Greening the Financial SystemIFRS S2 Pengungkapan terkait Iklim · ISSBRekomendasi TCFD · Financial Stability Board TCFDPrinsip-prinsip pengelolaan efektif risiko keuangan terkait iklim · Basel Committee on Banking Supervision
06Validasi

Cara hasil diperiksa.

Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.

PemeriksaanCara pelaksanaannya
Entity matchingBorrower and fund names are matched with documented rules
Coverage statedUnobserved parts of the network are acknowledged
Scenario realismSimulated shocks reflect plausible events

Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.

07Keterkaitan

Topik yang bertumpu pada metode ini.

08Pengelolaan

Siapa yang memelihara metode ini.

Ilmu Sistem dan Pemodelan stewards network science and contagion for the Consortium and reviews how it is applied across platforms.

Canonical page: Network science and contagion

09Terhubung

Ke mana halaman ini mengarah di seluruh Konsorsium.

10Pertanyaan

Tanya jawab.

What are the limits of network science and contagion in this field?
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
Bagaimana hasil diperiksa?
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Apakah nilai di halaman ini nyata?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Di mana lagi metode ini diterapkan?
Observatory Halaman metode kanonis mencantumkan setiap platform yang menerapkannya.
LaporanBerkontribusi

Kontribusikan bukti dari halaman ini.

Laporan is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

Global Risks AllianceKredit SwastaKesiapan Pembiayaan dan AsuransiSkenario dan Uji Tekanan

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