Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257980 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 1 [Article No.:] 25 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-36
Publisher: 
MDPI, Basel
Abstract: 
This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that loss given default (LGD) is non-random. In practice, however, LGD is impossible to predict and tends to be positively correlated with the default rate and the latter phenomenon is typically referred to as PD-LGD correlation (here PD refers to probability of default, which is often used synonymously with default rate). There is a large literature on modelling stochastic LGD and PD-LGD correlation, but there is a dearth of literature on using importance sampling to estimate large deviation probabilities in those models. Numerical evidence indicates that the proposed algorithms are extremely effective at reducing the computational burden associated with obtaining accurate estimates of large deviation probabilities across a wide variety of PD-LGD correlation models that have been proposed in the literature.
Subjects: 
acceptance-rejection sampling
credit risk
importance sampling
large deviation probabilities
loss probabilities
PD-LGD correlation
portfolio credit risk
stochastic recovery
tail probabilities
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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