Understanding Mortgage-backed Securities
by Michiel Bertsch
A research project on the mathematical modeling of fixed income markets has recently begun at the CNR institute for applied mathematics in Rome (Istituto per le Applicazioni del Calcolo IAC-CNR). The aim is to combine real world problems with high quality research in mathematical finance, in order to obtain a better and more efficient understanding of the correct pricing of complicated fixed income products such as mortgage-backed securities. The project is intrinsically interdisciplinary, and uses techniques varying from the statistical analysis of financial data to the development of basic models and their numerical simulation.
IAC has started a project on financial mathematics in collaboration with INA SIM S.P.A. (INA is a major insurance company in Italy). The aim of the project is both to study existing mathematical and statistical models for the correct pricing of fixed income financial products, and to develop new ones. In the early stage we focus on one hand on the analysis of the relevant statistical data and, on the other, on the study of existing advanced models in the academic literature. In a second stage, these two activities are intended to meet in order to develop accurate models for the pricing of complicated financial products and their numerical implementation.
A particular example of such products are the so-called mortgage-backed securities (MBSs). Roughly speaking, the US fixed income market is divided in three areas: treasury bills, corporate bonds and MBSs, but nowadays the latter area is the bigger one. MBSs are liquid and they are securitized for default risk. Their only disadvantage is the prepayment risk, and it is exactly this point which makes MBSs difficult to price and creates a challenge to financial modelers. Someone with a mortgage usually does not optimize the moment at which he exercises the prepayment option of the mortgage, and even pooling several mortgages together does not average out this effect. In the academic literature only very few advanced pricing models have been proposed; however, after more than 30 years of experience, the US market is a source of considerable data. This means that the necessary ingredients are present to improve the methods of quantitative analysis of MBSs. In this context, we observe that quantitative analysis becomes a particularly powerful tool in the case of new emerging markets, in which even aggressive traders may lack the necessary experience to be as efficient as usual. In the future, in the new European context, MBSs could very well form such an emerging market.
A closing remark regards the dramatic problem of the almost complete absence of good research in applied mathematics in Italian industry. The project on MBSs is attracting first rate students and postdocs. Some of them will become academic researchers, but I am convinced that others will find a job in Italian financial institutes. Having researchers with a PhD degree in mathematics in strategic positions in private companies would be an important step towards further high-quality collaboration with Italian industry.
Michiel Bertsch - University of Rome Tor Vergata and CNR-IAC
Tel: +39 06 440 2627