The Basle II parameter called Loss Given Default (LGD) aims to estimate the expected losses on not yet defaulted accounts in the case of default. Banks firstly need to collect historical recovery data, discount the recovery income and cost cash flow to the time of default, and calculate...

The paper provides an overview of the Exposure at Default (EAD) definition, requirements, and estimation methods as set by the Basel II regulation. A new methodology connected to the intensity of default modeling is proposed. The numerical examples show that various estimation techniques may...

The paper proposes an application of the survival time analysis methodology to estimations of the Loss Given Default (LGD) parameter. The main advantage of the survival analysis approach compared to classical regression methods is that it allows exploiting partial recovery data. The model is...

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump-Diffusion (SVJD) models are being explained. Three versions of the SIR particle filter with adapted proposal distributions to the jump occurrences, jump sizes, and both are derived and...