Thesis etd-04142021-113103 |
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Thesis type
Tesi di laurea magistrale
URN
etd-04142021-113103
Thesis title
Credit Risk Modelling in Application: Catastrophe Swaps and Deep Learning Approaches
Department
ECONOMIA E MANAGEMENT
Course of study
ECONOMICS
Supervisors
.
relatore Radi, Davide
Keywords
- Black Cox model
- catastrophe swap
- credit default swap
- credit risk
- deep learning
- fuzzy logic
- machine learning
- Merton model
- som
- stochastic models
Graduation session start date
12/07/2021
Availability
None
Abstract (Inglese)
Abstract (Italiano)
This work begins with examining various structural models with machine learning extensions to analysing credit risk. This is followed by the applications of credit risk modelling in the form of CAT swaps and CD swaps. The study of catastrophe (CAT) swaps is relatively new in literature and has received very little scholarly attention despite its extensive usage in the financial market. Both pre and ex-ante pricing model for CAT swaps are thoroughly exhumed with a final culmination in fuzzy network modelled deep learning pricing strategies for CAT swaps is provided. Credit Default swaps are extensively analysed and thoroughly explained.
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