Default correlation, cluster dynamics and single names: The GPCL dynamical loss model

Economy – Quantitative Finance – Pricing of Securities

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We extend the common Poisson shock framework reviewed for example in Lindskog and McNeil (2003) to a formulation avoiding repeated defaults, thus obtaining a model that can account consistently for single name default dynamics, cluster default dynamics and default counting process. This approach allows one to introduce significant dynamics, improving on the standard "bottom-up" approaches, and to achieve true consistency with single names, improving on most "top-down" loss models. Furthermore, the resulting GPCL model has important links with the previous GPL dynamical loss model in Brigo, Pallavicini and Torresetti (2006a,b), which we point out. Model extensions allowing for more articulated spread and recovery dynamics are hinted at. Calibration to both DJi-TRAXX and CDX index and tranche data across attachments and maturities shows that the GPCL model has the same calibration power as the GPL model while allowing for consistency with single names

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Default correlation, cluster dynamics and single names: The GPCL dynamical loss model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Default correlation, cluster dynamics and single names: The GPCL dynamical loss model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Default correlation, cluster dynamics and single names: The GPCL dynamical loss model will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-538838

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.