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Decomposing CDS Spreads: Fundamental Risk and Default Clustering: An Empirical Study of Theoreticalversus Market Spreads AcrossMarket Regimes
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Probability, Mathematical Physics and Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Probability, Mathematical Physics and Statistics.
2026 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Dekomponering av CDS-spreadar: fundamental risk och klustring av kredithändelser (Swedish)
Abstract [en]

This thesis studies the determinants of Credit Default Swap (CDS) index spreads by decomposing them into fundamental credit risk, default dependence, and default clustering. As a proxy for fundamental credit risk, rating-implied CDS spreads are used by mapping credit ratings to default probabilities and hazard rates. Dependence between defaults is then analyzed using a one-factor Gaussian copula model, while time-varying clustering in default arrivals is modeled using a Hawkes process. The empirical analysis is based on the CDS indices CDX US High Yield and iTraxx Crossover. The results show that rating-implied spreads explain a baseline level of credit risk, but do not capture the large market spread increases observed during periods of financial stress. Both the Gaussian copula framework and the Hawkes model indicate that dependence matters for index spread formation: implied correlation rises in stressed periods, and Hawkes-based clustering intensifies when defaults occur in clusters. Taken together, the results suggest that CDS index spreads reflect not only fundamental default risk, but also dependence and clustering effects that become particularly important during market turmoil.

Abstract [sv]

Denna uppsats studerar ingående faktorerna bakom Credit Default Swap (CDS)-indexspreadar genom att dela upp dem i fundamental kreditrisk, defaultberoende och defaultklustring. Som benchmark för fundamental kreditrisk konstrueras ratingbaserade CDS-spreadar genom att kreditbetyg kopplas till historiska default-sannolikheter och hazard rates. Beroendet mellan defaults analyseras därefter med en enfaktors Gaussisk copulamodell, medan tidsvarierande klustring i defaultankomster modelleras med en Hawkesprocess. Den empiriska analysen baseras på CDS-indexen CDX US High Yield och iTraxx Crossover. Resultaten visar att ratingbaserade spreadar förklarar en grundläggande nivå av kreditrisk, men inte fångar de kraftiga uppgångarna i marknadsspreadar under perioder av finansiell stress. Både den Gaussiska copulamodellen och Hawkesmodellen indikerar att beroende spelar en viktig roll för indexspreadars utveckling: den implicita korrelationen stiger i stressade perioder, samtidigt som Hawkesbaserad klustring ökar när defaults inträffar tätt i tiden. Sammantaget tyder resultaten på att CDS-indexspreadar inte bara reflekterar fundamental defaultrisk, utan även beroende- och klustringseffekter som blir särskilt viktiga under perioder av marknadsoro.

Place, publisher, year, edition, pages
2026.
Series
TRITA-SCI-GRU ; 2026:186
Keywords [en]
Credit Default Swaps, CDS Indices, CDS Spread, Credit Risk, Credit Event, Probability of Default, Credit Ratings, Default Correlation, Gaussian Copula, Hawkes Process, Default Clustering, Hazard Rates
Keywords [sv]
Credit Default Swap (CDS), CDS-index, CDS-spread, Kreditrisk, Kredithändelse, sannolikhet för kredithändelse, Kreditbetyg, Korrelation mellan kredithändelser, Gaussisk copula, Hawkesprocess, Klustring av kredithändelser, Hazard rate
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-384573OAI: oai:DiVA.org:kth-384573DiVA, id: diva2:2083026
External cooperation
Captor Fund Management AB
Subject / course
Applied Mathematics and Industrial Economics
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2026-07-01 Created: 2026-07-01 Last updated: 2026-07-01Bibliographically approved

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CiteExportLink to record
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