Simulation-based pricing of mortgage-backed securities

Mortgage-backed-securities (MBS), as the largest investment class of fixed income securities, have always been hard to price. Because of the following reasons, normal numerical methods like lattice methods, or finite difference method for solving PDEs are hard to apply: 1) the path dependence of mortgage pool cash flows; 2) the embedded American call option to prepay; 3) the American put option to default; 4) the fact that mortgage borrower do not/cannot exercise these option optimally. And those reasons make Monte Carlo simulation the best approach to price MBS. A standard MBS pricing framework would consists the following parts: 1) interest rate model; 2) prepayment model, which consists house turnover model and refinance model; 3) OAS model, which captures risk factors from the market price. Those factors are not accounted for in the previous two models. In order to hedge MBS efficiently and effectively, we need to calculate hedging measures quickly and correct. Chen and Fu (2001,2002,2003) has developed some efficient hedging algorithm in the past to perform this task.