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Extending the Merton Model: A New Approach to Incorporating Forward-Looking Market Information into Credit Risk Modeling

Andrésdóttir Kjerúlf, Ásthildur LU and Holmkvist, Fanny LU (2025) NEKP01 20251
Department of Economics
Abstract
This thesis contributes to the credit risk literature by analyzing the Merton model’s ability to replicate credit default swap (CDS) spreads. Since defaults are rare events, resulting in low statistical power, the method is evaluated by comparing model implied CDS spreads to market ones. The data sample used includes quarterly observations from 2015 to 2024 for 44 firms included in the NASDAQ Composite Index. The primary contribution lies in proposing a new approach for incorporating forward-looking information into credit risk modeling in the form of a VIX weighted volatility measure. While using CDS spreads for the evaluation restricts the sample to firms with active CDS contracts, the approach is broadly applicable and can be used for... (More)
This thesis contributes to the credit risk literature by analyzing the Merton model’s ability to replicate credit default swap (CDS) spreads. Since defaults are rare events, resulting in low statistical power, the method is evaluated by comparing model implied CDS spreads to market ones. The data sample used includes quarterly observations from 2015 to 2024 for 44 firms included in the NASDAQ Composite Index. The primary contribution lies in proposing a new approach for incorporating forward-looking information into credit risk modeling in the form of a VIX weighted volatility measure. While using CDS spreads for the evaluation restricts the sample to firms with active CDS contracts, the approach is broadly applicable and can be used for any publicly listed company. The results suggest that the standard Merton model offers an adequate risk indicator but falls short in replicating the CDS market, since it only produces a limited number of positive spread estimates. The thesis finds no optimal default threshold that consistently improves the replication results. Incorporating VIX weighted volatility in the model increases the number of positive spread estimates, although it remains insufficient in capturing market CDS spreads. (Less)
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author
Andrésdóttir Kjerúlf, Ásthildur LU and Holmkvist, Fanny LU
supervisor
organization
course
NEKP01 20251
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Merton model, CDS spreads, Credit risk, VIX weighted volatility, Default probability
language
English
id
9199449
date added to LUP
2025-09-12 10:50:57
date last changed
2025-09-12 10:50:57
@misc{9199449,
  abstract     = {{This thesis contributes to the credit risk literature by analyzing the Merton model’s ability to replicate credit default swap (CDS) spreads. Since defaults are rare events, resulting in low statistical power, the method is evaluated by comparing model implied CDS spreads to market ones. The data sample used includes quarterly observations from 2015 to 2024 for 44 firms included in the NASDAQ Composite Index. The primary contribution lies in proposing a new approach for incorporating forward-looking information into credit risk modeling in the form of a VIX weighted volatility measure. While using CDS spreads for the evaluation restricts the sample to firms with active CDS contracts, the approach is broadly applicable and can be used for any publicly listed company. The results suggest that the standard Merton model offers an adequate risk indicator but falls short in replicating the CDS market, since it only produces a limited number of positive spread estimates. The thesis finds no optimal default threshold that consistently improves the replication results. Incorporating VIX weighted volatility in the model increases the number of positive spread estimates, although it remains insufficient in capturing market CDS spreads.}},
  author       = {{Andrésdóttir Kjerúlf, Ásthildur and Holmkvist, Fanny}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Extending the Merton Model: A New Approach to Incorporating Forward-Looking Market Information into Credit Risk Modeling}},
  year         = {{2025}},
}