The state of New Jersey is insolvent. Bankrupt might be a better word. New Jersey is $60 billion in the hole on pension funding and the Governor is planning on skipping payments in a "pension payment holiday" until 2012 so as to not increase property taxes. To top it off, the ongoing plan assumptions are 8.25%. Sorry NJ, that simply is not going to happen.
[Reference: http://globaleconomicanalysis.blogspot.com/]
Earlier blog posts on the ongoing disaster with the NJ pension system:
June 15, 2007
April 12, 2007
April 6, 2007
March 16, 2007
Wednesday, November 26, 2008
Thursday, November 20, 2008
Bad bad bad news for US pensions
On the expense side...
Assets of the 100 biggest US company pension plans, which account for 70% of defined benefit pension assets at corporations, fell by an estimated $120bn in October - the largest monthly loss in at least eight years. In 2008, PPA cash requirements were an estimated $32bn, which will likely rise to about $93bn in 2009.
On the funding status side...
If the spread between Treasuries and high-grade corporate bond yields hadn't more than doubled to 3.3 points over the past 12 months, the combined $60 billion surplus for S&P's 1,500 companies at the end of 2007 would now be a deficit of more than $400 billion. With the drop in liabilities due to a higher discount rate, however, the deficit as of Sept. 30 was only $35 billion.
Assets of the 100 biggest US company pension plans, which account for 70% of defined benefit pension assets at corporations, fell by an estimated $120bn in October - the largest monthly loss in at least eight years. In 2008, PPA cash requirements were an estimated $32bn, which will likely rise to about $93bn in 2009.
On the funding status side...
If the spread between Treasuries and high-grade corporate bond yields hadn't more than doubled to 3.3 points over the past 12 months, the combined $60 billion surplus for S&P's 1,500 companies at the end of 2007 would now be a deficit of more than $400 billion. With the drop in liabilities due to a higher discount rate, however, the deficit as of Sept. 30 was only $35 billion.
Sunday, November 09, 2008
Actuaries versus quants
A different angle than the stuff you usually see, from Paul Wimott.
Those working in the fields of actuarial science and quantitative finance have not always been totally appreciative of each others’ skills. Actuaries have been dealing with randomness and risk in finance for centuries. Quants are the relative newcomers, with all their fancy stochastic mathematics. Rather annoyingly for actuaries, quants came along late in the game and thanks to one piece of insight in the early 1970s completely changed the face of the valuation of risk.
The insight I refer to is the concept of dynamic hedging, first published by Black, Scholes and Merton in 1973. Before 1973, derivatives were being valued using the ‘actuarial method’, in a sense relying, as actuaries always have, on the Central Limit Theorem. Since 1973 all that has been made redundant. Quants have ruled the financial roost. However, this might just be the time for actuaries to fight back.
I am putting the finishing touches to this article a few days after the first anniversary of the ‘day that quant died’. In early August 2007, a number of high-profile and previously successful quantitative hedge funds suffered large losses. People said that their models “just stopped working”. The year since has seen a lot of soul searching by quants — how could this happen when they’ve got such incredible models?
In my view, the main reason why quantitative finance is in a mess is because of complexity and obscurity. Quants are making their models increasingly complicated, in the belief they are making improvements. This is not the case. More often than not each ‘improvement’ is a step backwards. If this were a proper hard science then there would be a reason for trying to perfect models. But finance is not a hard science, one in which you can conduct experiments for which the results are repeatable. Finance, thanks to it being underpinned by human beings and their wonderfully irrational behaviour, is forever changing. It is, therefore, much better to focus attention on making the models robust and transparent rather than ever more intricate.
As I mentioned in a recent blog, there is a maths sweet spot in quant finance. The models should not be too elementary so as to make it impossible to invent new structured products, nor should they be so abstract as to be easily misunderstood by all except their inventor (and sometimes even by them), with the obvious and financially dangerous consequences. Our goal is to make quant finance practical, understandable and, above all, safe.
When banks sell a contract they do so assuming it is going to make a profit. They use complex models, with sophisticated numerical solutions, to come up with the perfect value. Having gone to all that effort they then throw it into the same pot as all the others and risk-manage en masse. The funny thing is they never know whether each individual contract has “washed its own face”. Sure they know whether the pot has made money, their bonus is tied to it. But each contract? It makes good sense to risk-manage all contracts together but not to go into such obsessive detail in valuation when ultimately it’s the portfolio that makes money, especially if the basic models are so dodgy. The theory of quant finance and the practice diverge. Money is made by portfolios, not by individual contracts. In other words, quants make money from the Central Limit Theorem, just like actuaries, it’s just that quants are loath to admit it! Ironic.
It’s about time that actuaries got more involved in quantitative finance and brought some common sense back into this field. We need models people can understand and a greater respect for risk. Actuaries and quants have complementary skill sets. What high finance needs now are precisely the skills that actuaries have, a deep understanding of statistics, an historical perspective, and a willingness to work with data.
Thanks to CP for the link.
Those working in the fields of actuarial science and quantitative finance have not always been totally appreciative of each others’ skills. Actuaries have been dealing with randomness and risk in finance for centuries. Quants are the relative newcomers, with all their fancy stochastic mathematics. Rather annoyingly for actuaries, quants came along late in the game and thanks to one piece of insight in the early 1970s completely changed the face of the valuation of risk.
The insight I refer to is the concept of dynamic hedging, first published by Black, Scholes and Merton in 1973. Before 1973, derivatives were being valued using the ‘actuarial method’, in a sense relying, as actuaries always have, on the Central Limit Theorem. Since 1973 all that has been made redundant. Quants have ruled the financial roost. However, this might just be the time for actuaries to fight back.
I am putting the finishing touches to this article a few days after the first anniversary of the ‘day that quant died’. In early August 2007, a number of high-profile and previously successful quantitative hedge funds suffered large losses. People said that their models “just stopped working”. The year since has seen a lot of soul searching by quants — how could this happen when they’ve got such incredible models?
In my view, the main reason why quantitative finance is in a mess is because of complexity and obscurity. Quants are making their models increasingly complicated, in the belief they are making improvements. This is not the case. More often than not each ‘improvement’ is a step backwards. If this were a proper hard science then there would be a reason for trying to perfect models. But finance is not a hard science, one in which you can conduct experiments for which the results are repeatable. Finance, thanks to it being underpinned by human beings and their wonderfully irrational behaviour, is forever changing. It is, therefore, much better to focus attention on making the models robust and transparent rather than ever more intricate.
As I mentioned in a recent blog, there is a maths sweet spot in quant finance. The models should not be too elementary so as to make it impossible to invent new structured products, nor should they be so abstract as to be easily misunderstood by all except their inventor (and sometimes even by them), with the obvious and financially dangerous consequences. Our goal is to make quant finance practical, understandable and, above all, safe.
When banks sell a contract they do so assuming it is going to make a profit. They use complex models, with sophisticated numerical solutions, to come up with the perfect value. Having gone to all that effort they then throw it into the same pot as all the others and risk-manage en masse. The funny thing is they never know whether each individual contract has “washed its own face”. Sure they know whether the pot has made money, their bonus is tied to it. But each contract? It makes good sense to risk-manage all contracts together but not to go into such obsessive detail in valuation when ultimately it’s the portfolio that makes money, especially if the basic models are so dodgy. The theory of quant finance and the practice diverge. Money is made by portfolios, not by individual contracts. In other words, quants make money from the Central Limit Theorem, just like actuaries, it’s just that quants are loath to admit it! Ironic.
It’s about time that actuaries got more involved in quantitative finance and brought some common sense back into this field. We need models people can understand and a greater respect for risk. Actuaries and quants have complementary skill sets. What high finance needs now are precisely the skills that actuaries have, a deep understanding of statistics, an historical perspective, and a willingness to work with data.
Thanks to CP for the link.
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