Das Kontrahentenrisiko und CCDSs unter Korrelation
Das Kontrahentenrisiko unter Korrelation ist in der Finanzliteratur relativ unerforscht. Im vorliegenden Artikel führen Damiano Brigo und Andrea Pallavicini die bestehenden Analysen über einfache Swap-Portfolios hinaus. Das Ausfallereignis wird im Rahmen eines stochastischen Intensitäts-Sprung-Diffusionsmodells betrachtet. Außerdem werden relevante und strukturierte Auswirkungen der Zinssatz/Kredit-Korrelation auf die Kontrahentenrisiko-Anpassung nachgewiesen, die in natürlicher Weise mit bedingten Kreditausfallswaps (CCDSs) zusammenhängen
In diesem Artikel befassen wir uns mit dem Kontra-hentenrisiko für Zinsauszahlungen bei Korrelation zwischen den Kredit- und Zinssätzen. Wir untersuchen dabei insbesondere das Kontrahentenrisiko (bzw. Ausfallrisiko) bei Zinsswaps (IRSs) und schließen damit an die Arbeiten von Sorensen & Bollier (1994) und Brigo & Masetti (2006) an, die die Korrelation nicht berücksichtigen, sowie an die Arbeit von Brigo & Pallavicini (2007), die den Intensitätsprozess als reinen Diffusionsprozess betrachten.
Auße
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Credit risk
Finding the investment management ‘one analytics view’
This paper outlines the benefits accruing to buy-side practitioners on the back of generating a single analytics view of their risk and performance metrics across funds, regions and asset classes
Revolutionising liquidity management: harnessing operational intelligence for real‑time insights and risk mitigation
Pierre Gaudin, head of business development at ActiveViam, explains the importance of fast, in-memory data analysis functions in allowing firms to consistently provide senior decision-makers with actionable insights
Sec-lending haircuts and indemnification pricing
A pricing method for borrowed securities that includes haircut and indemnification is introduced
XVAs and counterparty credit risk for energy markets: addressing the challenges and unravelling complexity
In this webinar, a panel of quantitative researchers and risk practitioners from banks, energy firms and a software vendor discuss practical challenges in the modelling and risk management of XVAs and CCR in the energy markets, and how to overcome them.
Credit risk & modelling – Special report 2021
This Risk special report provides an insight on the challenges facing banks in measuring and mitigating credit risk in the current environment, and the strategies they are deploying to adapt to a more stringent regulatory approach.
The wild world of credit models
The Covid-19 pandemic has induced a kind of schizophrenia in loan-loss models. When the pandemic hit, banks overprovisioned for credit losses on the assumption that the economy would head south. But when government stimulus packages put wads of cash in…
Driving greater value in credit risk and modelling
A forum of industry leaders discusses the challenges facing banks in measuring and mitigating credit risk in the current environment, and strategies to adapt to a more stringent regulatory framework in the future
Accelerating the evolution of credit decisioning and modelling
Anthony Mancuso, director, global head of risk modelling and decisioning at SAS, explains the importance of developing a fully capable credit modelling lifecycle to empower non-specialist personnel, and offers insight into its own solutions to this end,…
Most read
- Top 10 operational risks for 2024
- Japanese megabanks shun internal models as FRTB bites
- Top 10 op risks: third parties stoke cyber risk