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Structured finance modeling is a type of investment forecasting that is applicable for securitized products. These products might be backed or secured by mortgage assets, vehicle loans, and other types of assets that are traded in the markets. The modeling aspect involves creating a forecast for future cash flows that might be generated from securitized assets based on analysis, and placing monetary values on deals. When performed accurately, structured finance modeling should provide market participants with a sense of what to expect with investment portfolio performance. Faulty modeling, however, could have dire consequences for investors, financial institutions that trade these products, and the broader economy as a whole.
Features tied to mortgage products backing the financial securities might be used in the models that determine performance forecasts. These components could include the likelihood for early payments, potential default rates, and the interest rates that are attached to the mortgages. Structured finance modeling involves using all of these features to analyze the various stages of securitization deals to form a performance expectation.
Part of structured finance modeling involves giving forecasts about the economy and the housing market, given that many of the securities traded in this market are backed by mortgages. This might include using economic data to determine the direction in which housing prices might trend in a particular region, as well as the pace of economic growth as measured by gross domestic product (GDP). Additional economic criteria that might be used for structured finance modeling may be the jobless claims in a region as well as the direction of interest rates.
The process of structured finance modeling also involves the assignment of risk to mortgage and asset-backed securities. Typically, industry ratings' agencies use modeling to determine a security's likelihood for default. Subsequently, a particular rating is attached to the security, and investors can buy these financial instruments based on their tolerance for risk. In the event that the structured finance modeling techniques lead to inaccurate valuations, it could potentially lead to substantial loss in the financial markets.
Financial institutions that create structured products could use asset backed securities that are likely to be attractive based on the financial modeling that may be acceptable to ratings agencies. In doing so, loans that are least likely to default should command a high credit rating. If modeling used by rating's agencies is flawed, the structured products may be comprised leaving investors with little recourse.
@everetra - Well, if I understand the article correctly, housing prices, economic trends and likely default rates are part of the variables that the forecasting model takes into consideration.
So I would have to think that if any of these variables are inaccurate – regardless of the reason – then the model would be inaccurate. I know there are some so-called financial gurus out there who claimed that they saw the collapse of the housing bubble and the subsequent sub prime mortgage meltdown, but most industry insiders did not.
In my opinion, no loans should ever have been extended to anyone with sub prime mortgages. Bankers say that they were pressured by the government to extend these loans. I can’t say
one way or another if that’s true.
In either case, if you were an investor, you should never have purchased any mortgage backed securities that reflected sub prime credit, assuming you knew the credit rating on the financial product to begin with.
@MrMoody - I disagree. What caused the problem was the collapse of the housing bubble, which you stated at the outset.
Furthermore we entered a recession at that time, with high unemployment. Even if you had excellent credit, but you found yourself out of a job and your house was underwater relative to its initial purchase price, you would end up facing foreclosure and the holder of the mortgage backed security would be stuck with a net loss.
The forecasting model is meant to predict certain outcomes but I don’t think it can accurately predict crises like the kind that hit the U.S. economy around 2006 – 2008.
I think everyone knows by now that the collapse of the housing bubble market in 2006 and beyond was one of the triggers of the meltdown of the mortgage backed securities market.
Many of these mortgages were sub prime, and so there were high default rates when the housing bubble collapsed. Ironically, one of the things that caused bankers to place their seal of approval on these mortgage backed securities was the endorsements from the big credit agencies, agencies we look to today to tell us if a company – or country – is a credit worthy risk.
What I’m getting at is that this structured finance forecasting model becomes virtually worthless, in my opinion, if the risk assessments are not accurate.
A mortgage backed security doesn’t have to be a high risk investment. But bankers looked to the agencies to give their blessings on the mortgages they were selling, and that’s what I think caused the problem.
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