Abstract
This paper offers a methodology for optimal sizing, or subordination, and pricing of the credit-sensitive Mortgage Backed Securities (MBS), such as ABS and CDO deals backed by subprime mortgage loans. To that end, we perform a four-step numerical analysis: first, estimating scenario-specific credit losses from a given mortgage pool backed by different collateral types by using the multi-factor Monte Carlo simulation models developed by Lin, Cho, and Yang (2009); second, structuring the pool into a ?-pack?subordination structure based on statistically-determined stress economic scenarios; third, estimating IRR and other performance indicators of determined tranches to assess and compare risk-adjusted returns thereof; and, calculating risk-based capitals as additional and policy-relevant risk measures.