Test of risk assessment
07 January 2011, 08:48 PM
#62
You're confirmed +ve.
1 in a 1000 die = 0.1 in a 100, so that's 0.1% chance of dying...
...But the test is 'only' 95% successful, so you have to apply that 95% to the 0.1% i.e. 0.95 x 0.1 = 0.095%.
So you have a 'guaranteed' 0.095% chance of dying - or 95 times in every 100,000.
So, reversing it, then that's a 99.905% chance of SURVIVING.
1 in a 1000 die = 0.1 in a 100, so that's 0.1% chance of dying...
...But the test is 'only' 95% successful, so you have to apply that 95% to the 0.1% i.e. 0.95 x 0.1 = 0.095%.
So you have a 'guaranteed' 0.095% chance of dying - or 95 times in every 100,000.
So, reversing it, then that's a 99.905% chance of SURVIVING.
Last edited by joz8968; 07 January 2011 at 08:54 PM.
07 January 2011, 09:05 PM
#66
I came across this as a simple test of the ability of bankers to assess risk - indeed not just bankers, any typical human!
There is a fatal disease that kills 1 in 1000
A test has been developed that is 95% accurate.
Your doctor tells you that your test is positive.
What are your chances of surviving?
There is a fatal disease that kills 1 in 1000
A test has been developed that is 95% accurate.
Your doctor tells you that your test is positive.
What are your chances of surviving?
0%
We all die sooner or later.
07 January 2011, 10:13 PM
#70
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In the run up to the crunch Goldman Sachs had five days in a row where their risk models experienced 10 standard deviation events.
Each event was the equivalent of winning the lottery 21 times in a row, five times.
And yet they still used the models and didn't think - hmmm - maybe there is something awry in the Kingdom of Denmark!
07 January 2011, 11:23 PM
#71
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Well for the reason I have outlined – the initial assumptions are skewed – garbage in, garbage out
However, I suspect the early risk models for sub-prime mortgage lending were both very good and very accurate in terms of risk calculation and early sub-prime providers had a valid and profitable business model
The problem is, and this to me is a fundamental flaw of capitalism (as practiced in the late 20th century) is that it just follows the line of least resistance, later iterations of the sub-prime risk model were just easy vehicles to allow “bankers” to keep the magic “money” roundabout turning, why bother checking to see if the risk models were valid, what possible advantage would that have given the banks/bankers – it is the power/allure of Market Share (apart from in the final denouement, where GS cleaned up)
Let’s face it what do bankers (generalisation alert) really know about risk, take James Dyson – he had to re-mortgage and charge his house (everything) to the banks quite a few times in the pursuit of his dream (and building a wealth creating business), how many bankers have to do the same i.e really risk their personal assets, very very few I would imagine.
However, I suspect the early risk models for sub-prime mortgage lending were both very good and very accurate in terms of risk calculation and early sub-prime providers had a valid and profitable business model
The problem is, and this to me is a fundamental flaw of capitalism (as practiced in the late 20th century) is that it just follows the line of least resistance, later iterations of the sub-prime risk model were just easy vehicles to allow “bankers” to keep the magic “money” roundabout turning, why bother checking to see if the risk models were valid, what possible advantage would that have given the banks/bankers – it is the power/allure of Market Share (apart from in the final denouement, where GS cleaned up)
Let’s face it what do bankers (generalisation alert) really know about risk, take James Dyson – he had to re-mortgage and charge his house (everything) to the banks quite a few times in the pursuit of his dream (and building a wealth creating business), how many bankers have to do the same i.e really risk their personal assets, very very few I would imagine.
07 January 2011, 11:43 PM
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I don't think initial assumptions are skewed. The test is designed to demonstrate that humans typically are not very good at risk assessment.
The observations I made are regarding the Gaussian Cupola correlation models and the VaR models that were used to evaluate the risk of CDOs. These were the models that were used for the creation of securitised subprime, mortgage backed securities.
The same models were used from day one - they were fundamental in enabling mortgage backed securities. People made a lot of money in the early days as the business model was founded on never ending and rapid property price rises (which of course will occur if you pump that much capital into the asset!).
I think your comment above refers to the credit scoring (FICO) models that were used in the US on the retail side of the business. These models are pretty good, but fell by the wayside for the very reason you state.
Mortgage brokers and the retail banks they worked for were betting someone else's money - so they didn't care. They became increasingly lax, and increasingly fraudulent in their origination.
The observations I made are regarding the Gaussian Cupola correlation models and the VaR models that were used to evaluate the risk of CDOs. These were the models that were used for the creation of securitised subprime, mortgage backed securities.
The same models were used from day one - they were fundamental in enabling mortgage backed securities. People made a lot of money in the early days as the business model was founded on never ending and rapid property price rises (which of course will occur if you pump that much capital into the asset!).
I think your comment above refers to the credit scoring (FICO) models that were used in the US on the retail side of the business. These models are pretty good, but fell by the wayside for the very reason you state.
Mortgage brokers and the retail banks they worked for were betting someone else's money - so they didn't care. They became increasingly lax, and increasingly fraudulent in their origination.
08 January 2011, 09:14 AM
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08 January 2011, 09:18 AM
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Let’s face it what do bankers (generalisation alert) really know about risk, take James Dyson – he had to re-mortgage and charge his house (everything) to the banks quite a few times in the pursuit of his dream (and building a wealth creating business), how many bankers have to do the same i.e really risk their personal assets, very very few I would imagine.
Bankers should be THE experts in risk or else why does society see fit to reward them so? If they are not then they are nothing but old fashioned thieves.
08 January 2011, 09:25 AM
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The more you look at it the more you realise the (retail) banks didn't care.
Their agents were selling the mortgages and were being trusted to carry out the FICO credit rating and risk assessment.
The banks then passed this mortgage into the capital markets where the investment banks were providing them with AAA rated, cast iron, don't you worry your pretty little head mortgage securities.
The banks at large were merely passing the stuff through and taking a fee.
And probably half the securitisation of the market was carried out by a team of 20 people in some esoteric investment arm of a US based in London. One man judged the financial risks that were capable of bringing down the Western world's financial system.
Their agents were selling the mortgages and were being trusted to carry out the FICO credit rating and risk assessment.
The banks then passed this mortgage into the capital markets where the investment banks were providing them with AAA rated, cast iron, don't you worry your pretty little head mortgage securities.
The banks at large were merely passing the stuff through and taking a fee.
And probably half the securitisation of the market was carried out by a team of 20 people in some esoteric investment arm of a US based in London. One man judged the financial risks that were capable of bringing down the Western world's financial system.
08 January 2011, 10:21 AM
#77
The banks have a history of coming up with risk models that make the maths easy - they haven't traditionally worried about whether they are an accurate model of the real world. For example, the LTCM debacle (which involved at least one Nobel prize winning economist) was based on the assumption that stock volatility is normally distributed. If that assumption were true the kind of stock market crashes we see every ten years or so would only happen every 200 years.
LTCM came up with an algorithm for selling derivatives and then continuously hedging those derivatives in a way that guaranteed them a profit provided the price of the underlying security stayed within a certain bound either side of the strike price. Unfortunately the algorithm meant that if the price moved outside of those bounds the loses grew exponentially. Because the models assumed these movements would be very rare they massively underpriced the risk and when the stock market crashed in the late '90s they required a massive bailout.
Much of modern financial engineering follows a similar model, generating a high probability of a relatively small return (in percentage terms) in exchange for allowing a small probability of a much more significant loss. The banks then leverage up the trades to ensure the small percentage return is significant in actual cash terms. Because their models tell them the risk of significant loss is very small they kid themselves that they are not gambling. Unfortunately, as the last few years have shown, they are crap at modelling the risk - usually because they use inappropriate probability distributions or forget about whole risk scenarios altogether. CDOs is a good example of this - the risk modelling focused on mortgage defaults as independent events and almost ignored the possibility that a single underlying cause (such as widespread mis-selling or a housing market crash) could cause a large number of defaults.
If anyone is interested in this subject then I would recommend reading The Black Swan by Nassim Taleb.
LTCM came up with an algorithm for selling derivatives and then continuously hedging those derivatives in a way that guaranteed them a profit provided the price of the underlying security stayed within a certain bound either side of the strike price. Unfortunately the algorithm meant that if the price moved outside of those bounds the loses grew exponentially. Because the models assumed these movements would be very rare they massively underpriced the risk and when the stock market crashed in the late '90s they required a massive bailout.
Much of modern financial engineering follows a similar model, generating a high probability of a relatively small return (in percentage terms) in exchange for allowing a small probability of a much more significant loss. The banks then leverage up the trades to ensure the small percentage return is significant in actual cash terms. Because their models tell them the risk of significant loss is very small they kid themselves that they are not gambling. Unfortunately, as the last few years have shown, they are crap at modelling the risk - usually because they use inappropriate probability distributions or forget about whole risk scenarios altogether. CDOs is a good example of this - the risk modelling focused on mortgage defaults as independent events and almost ignored the possibility that a single underlying cause (such as widespread mis-selling or a housing market crash) could cause a large number of defaults.
If anyone is interested in this subject then I would recommend reading The Black Swan by Nassim Taleb.
Last edited by scud8; 08 January 2011 at 06:49 PM. Reason: Fixed name of book
08 January 2011, 11:09 AM
#78
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i think what we are saying is numbers without a context are useless in determining anything
more people get attacked by sharks in 3ft of water, than at any other depth
does that tell you you are in most danger of a shark attack in 3ft of water or most people are usually in 3ft of water when attacked by a shark
more people get attacked by sharks in 3ft of water, than at any other depth
does that tell you you are in most danger of a shark attack in 3ft of water or most people are usually in 3ft of water when attacked by a shark
08 January 2011, 12:39 PM
#79
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The correlation models that were used to price CDOs used exactly that logic.
They identified a correlation between 3ft of water and shark attacks.
When the boat capsized in the deep Indian Ocean and the Oceanic White Tips came en masse they had no clue what to do. As Scud says the models work as long as nothing untoward or unplanned happens.
The 'illusion of validity'.
For books I would also recommend Liar's Poker, Big Short, Fool's Gold and Whoops.
They identified a correlation between 3ft of water and shark attacks.
When the boat capsized in the deep Indian Ocean and the Oceanic White Tips came en masse they had no clue what to do. As Scud says the models work as long as nothing untoward or unplanned happens.
The 'illusion of validity'.
For books I would also recommend Liar's Poker, Big Short, Fool's Gold and Whoops.
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