TY - JOUR
T1 - Deep learning for CVA computations of large portfolios of financial derivatives
AU - Andersson, Kristoffer
AU - Oosterlee, Cornelis W.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, e.g., a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for different levels of WWR and for different levels of credit quality of the counterparty. We show that the CVA is overestimated with up to 25% by using the standard procedure of not adjusting the exercise strategy for the default-risk of the counterparty. For the Expected Shortfall of the CVA dynamics, the overestimation was found to be more than 100% in some non-extreme cases.
AB - In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, e.g., a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for different levels of WWR and for different levels of credit quality of the counterparty. We show that the CVA is overestimated with up to 25% by using the standard procedure of not adjusting the exercise strategy for the default-risk of the counterparty. For the Expected Shortfall of the CVA dynamics, the overestimation was found to be more than 100% in some non-extreme cases.
KW - Bermudan options
KW - Deep learning
KW - Expected shortfall
KW - Portfolio CVA
KW - WWR
UR - http://www.scopus.com/inward/record.url?scp=85107148110&partnerID=8YFLogxK
U2 - 10.1016/j.amc.2021.126399
DO - 10.1016/j.amc.2021.126399
M3 - Article
AN - SCOPUS:85107148110
SN - 0096-3003
VL - 409
SP - 1
EP - 21
JO - Applied Mathematics and Computation
JF - Applied Mathematics and Computation
M1 - 126399
ER -