Faulty Risk Model
Interesting Quotes from the book * Explaination from the book how each model work and why it is not quite a good idea too rely too much on them
* “When the model is too simplistic it might not explain what happened, but if it is too complicated, the model contains too much noise—just as reality contains too much noise.We all know that we have a financial crisis, but we do not immediately see why we have it.”
* “The problemis not in themathematics itself. Themodels are inherently logical. The problem lies in the way mathematics is applied in concrete situations.”
* “Financial innovation is clearly not a reason for the fall in interest rates, or the rise in real estate prices. But it might be an explanation of how the subprime crisis got so easily out of hand.”
* “Each of the participants, the banks, the credit rating agencies, the buyers of these products, and even professors of finance, who specialize in securitization, did not see it coming. And the reason they did not see it coming is clearly related to the way the industry persistently misjudged risk. And they misjudged risk because they all believed in models that universally failed to predict risk.”
* “there is no question that a breakdown in risk management was more than just a factor of this crisis. It caused a credit bubble to get out of hand. Without it, the crisis would have been much less severe. But alas, it is not a plausible cause for the crisis itself.”
* A modern financial mathematics, Robert Merton said, “The attempt to quantify risk has led to the existence of more overall risk in the system,” because everyone feels safer than before and therefore takes greater risks. This important feedback loop is not taken into account in many risk models. It results in risk being permanently underestimated, and this is what fueled the credit boom even further.
More research on
1) Why is the model fault? Modeling: “Normal distribution is not always the norm”
* “When the model is too simplistic it might not explain what happened, but if it is too complicated, the model contains too much noise—just as reality contains too much noise.We all know that we have a financial crisis, but we do not immediately see why we have it.”
* “The problemis not in themathematics itself. Themodels are inherently logical. The problem lies in the way mathematics is applied in concrete situations.”
* “Financial innovation is clearly not a reason for the fall in interest rates, or the rise in real estate prices. But it might be an explanation of how the subprime crisis got so easily out of hand.”
* “Each of the participants, the banks, the credit rating agencies, the buyers of these products, and even professors of finance, who specialize in securitization, did not see it coming. And the reason they did not see it coming is clearly related to the way the industry persistently misjudged risk. And they misjudged risk because they all believed in models that universally failed to predict risk.”
* “there is no question that a breakdown in risk management was more than just a factor of this crisis. It caused a credit bubble to get out of hand. Without it, the crisis would have been much less severe. But alas, it is not a plausible cause for the crisis itself.”
* A modern financial mathematics, Robert Merton said, “The attempt to quantify risk has led to the existence of more overall risk in the system,” because everyone feels safer than before and therefore takes greater risks. This important feedback loop is not taken into account in many risk models. It results in risk being permanently underestimated, and this is what fueled the credit boom even further.
More research on
1) Why is the model fault? Modeling: “Normal distribution is not always the norm”