Credit Exposure Modelling Using Differential Machine Learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2023
Författare
Wagner, Samuel
Karp, Måns
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Exposure modelling is a critical aspect of managing counterparty credit risk, and banks
worldwide invest significant time and computational resources in this task. One approach
to modelling exposure involves pricing trades with a counterparty in numerous potential
future market scenarios. Suitable for this type of pricing is a framework presented in
2020 by Huge and Savine, which they call differential machine learning. It approximates
the pricing function with a neural network that trains on Monte Carlo paths and the
gradients along these paths. This thesis aims to demonstrate the application of differential machine learning in the context of exposure modelling. To better comply with this context,
training is done on market variables, rather than some hidden model state. Simulated
data is used from Heston type models to estimate the future exposure distribution of a
portfolio consisting of European options. The conducted experiments reveal that training
the machine learning model on market observables yields similar results to those obtained
when training on hidden model states. Furthermore, the exposure modelling approach
is subject to stress testing by evaluation of its performance under different levels of
compatibility between the pricing model and future market scenarios in which the portfolio
is priced. Results show that low compatibility leads to decreased accuracy of the predicted
exposure distributions.
Beskrivning
Ämne/nyckelord
Counterparty credit risk, Differential machine learning, Exposure modelling. Heston model, Option pricing