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Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Frodé, Isabelle LU and Sambergs, Viktor LU (2022) In Master’s Theses in Mathematical Sciences FMSM01 20221
Mathematical Statistics
Abstract
We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated using short rates from the Hull-White One-Factor model. The best performance was obtained from a GRU neural network with two recurrent layers, which with adequate accuracy could reproduce the exposure profile for an interest rate swap contract. Errors were comparable to those expected from a Monte Carlo simulation with 5K paths. With regards to computational efficiency, the proposed model showed great potential in outperforming traditional numerical... (More)
We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated using short rates from the Hull-White One-Factor model. The best performance was obtained from a GRU neural network with two recurrent layers, which with adequate accuracy could reproduce the exposure profile for an interest rate swap contract. Errors were comparable to those expected from a Monte Carlo simulation with 5K paths. With regards to computational efficiency, the proposed model showed great potential in outperforming traditional numerical methods. Further development and calibration to actual market data is required for the model to be applicable in the industry. The proposed architectures may then prove useful, especially for contracts with high-rated counterparties, traded in a normal and liquid market. (Less)
Please use this url to cite or link to this publication:
author
Frodé, Isabelle LU and Sambergs, Viktor LU
supervisor
organization
course
FMSM01 20221
year
type
H2 - Master's Degree (Two Years)
subject
keywords
xVA, CVA, OTC, Counterparty Credit Risk, Interest Rate Swaps, Hull-White Model, Machine Learning, Artificial Neural Networks, Gated Recurrent Units
publication/series
Master’s Theses in Mathematical Sciences
report number
LUTFMS-3441-2022
ISSN
1404-6342
other publication id
2022:E31
language
English
id
9087036
date added to LUP
2022-06-14 09:06:20
date last changed
2022-06-14 09:06:20
@misc{9087036,
  abstract     = {{We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated using short rates from the Hull-White One-Factor model. The best performance was obtained from a GRU neural network with two recurrent layers, which with adequate accuracy could reproduce the exposure profile for an interest rate swap contract. Errors were comparable to those expected from a Monte Carlo simulation with 5K paths. With regards to computational efficiency, the proposed model showed great potential in outperforming traditional numerical methods. Further development and calibration to actual market data is required for the model to be applicable in the industry. The proposed architectures may then prove useful, especially for contracts with high-rated counterparties, traded in a normal and liquid market.}},
  author       = {{Frodé, Isabelle and Sambergs, Viktor}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master’s Theses in Mathematical Sciences}},
  title        = {{Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment}},
  year         = {{2022}},
}