The credit crisis is spreading more and more. Banks will tumble over without governmental support, companies like General Motors and Toyota have to layoff people because they are not selling enough new cars and pension funds have to apply serious cutbacks in order to keep a healthy balance between assets and liabilities. It has become commonly accepted that the cause of the credit crisis lies in the introduction of complex financial product that have been constructed in the backrooms of banks by financial mathematicians and econometricians. So it is all to blame on math? Math has become a black sheep, a scapegoat to blame the credit crisis on. But math doesn’t introduce new financial products, it is the bankers themselves. Moreover it was not the math that caused the prices of houses to drop, causing the sub prime mortgages to default. Bankers didn’t anticipate on a significant drop in real estate prices, a rare but disastrous event. They didn’t (want to) see that black swan.
Math is the mother of many sciences, it is very important in our profession, also in the financial markets. I have spent several years in applying math in modelling financial products in optimisation models for pension funds and insurers. Aim of the models was to analyse strategies in funding and investments against the uncertain future. In most cases Monte Carlo simulation was used, to generate as many as possible future scenarios and find the most robust investment and funding strategy. Math gives you the possibility to describe a financial product in a handy, accurate, objective and quantitative way, giving insight it its behaviour under different economic circumstances. Key is of course to think of the circumstances you want to analyse, as in any optimisation project. This requires an open mind, not one pre-occupied with making money.
The core of the credit crisis lies in supplying mortgages to families that normally wouldn’t get one. By adjusting the conditions of a mortgage (introducing the sub prime mortgage), banks in the US created a possibility to increase the number of mortgages sold. When the family no longer could fulfil the payments on the mortgage, the banks would simply sell the house. In selling the house they expected to make a profit, because of ever increasing prices of real estate. There is no math involved in that process, just a focus on making money, whatever you may think of that. To even more increase revenues (and profit) derivatives were created to split up the sub prime mortgage portfolios, selling it to other banks and investors, spreading the risk across the world. This created a setting compared to Domino day. Again no math involved here, the only driver here is money.
The first domino’s started to tumble when banks in the US were only able to sell the real estate of defaulted sub prime mortgages with substantial losses. A situation that was impossible according to the bankers. This is a typical example of a black swan as has been described by Nassim Nicholas Taleb. As he states: “Banks hire dull people and train them to be even more dull. If they look conservative, it's only because their loans go bust on rare, very rare occasions. But (...) bankers are not conservative at all. They are just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug”
So it is wrong to blame math for the credit crisis. Math is a powerful tool that supports us in many ways. It helps us in optimising logistic chains, schedule manpower, build telecom networks, search Mars for water, organise humanitarian support and much more. When the bankers had used it in a proper way, maybe they would have been able to identify the risks involved when introducing sub prime mortgages.
Math is the mother of many sciences, it is very important in our profession, also in the financial markets. I have spent several years in applying math in modelling financial products in optimisation models for pension funds and insurers. Aim of the models was to analyse strategies in funding and investments against the uncertain future. In most cases Monte Carlo simulation was used, to generate as many as possible future scenarios and find the most robust investment and funding strategy. Math gives you the possibility to describe a financial product in a handy, accurate, objective and quantitative way, giving insight it its behaviour under different economic circumstances. Key is of course to think of the circumstances you want to analyse, as in any optimisation project. This requires an open mind, not one pre-occupied with making money.
The core of the credit crisis lies in supplying mortgages to families that normally wouldn’t get one. By adjusting the conditions of a mortgage (introducing the sub prime mortgage), banks in the US created a possibility to increase the number of mortgages sold. When the family no longer could fulfil the payments on the mortgage, the banks would simply sell the house. In selling the house they expected to make a profit, because of ever increasing prices of real estate. There is no math involved in that process, just a focus on making money, whatever you may think of that. To even more increase revenues (and profit) derivatives were created to split up the sub prime mortgage portfolios, selling it to other banks and investors, spreading the risk across the world. This created a setting compared to Domino day. Again no math involved here, the only driver here is money.
The first domino’s started to tumble when banks in the US were only able to sell the real estate of defaulted sub prime mortgages with substantial losses. A situation that was impossible according to the bankers. This is a typical example of a black swan as has been described by Nassim Nicholas Taleb. As he states: “Banks hire dull people and train them to be even more dull. If they look conservative, it's only because their loans go bust on rare, very rare occasions. But (...) bankers are not conservative at all. They are just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug”
So it is wrong to blame math for the credit crisis. Math is a powerful tool that supports us in many ways. It helps us in optimising logistic chains, schedule manpower, build telecom networks, search Mars for water, organise humanitarian support and much more. When the bankers had used it in a proper way, maybe they would have been able to identify the risks involved when introducing sub prime mortgages.
