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Posted by Beliavsky on May 3, 2009, 9:29 pm
by ELEANOR LAISE
Wall Street Journal
May 2, 2009
http://online.wsj.com/article/SB124121875397178921.html
There is no standard Monte Carlo approach, but the method is nothing
new. It was used during World War II to help develop the atomic bomb.
By the late 1990s some financial-services firms, like T. Rowe Price
Group Inc., had introduced Monte Carlo tools aimed at individuals.
If one had asked a financial adviser 18 months ago for retirement-
planning guidance, there is a good chance he would have run a "Monte
Carlo" simulation. This calculation method, as it is commonly used in
financial planning, estimates the odds of reaching retirement
financial goals.
But there is little chance your Monte Carlo simulation, named for the
gambling mecca, would have highlighted a scenario like the market
slide just seen. Though these tools typically run a portfolio through
hundreds or thousands of potential market scenarios, they often assign
minuscule odds to extreme market events. Yet these extreme events seem
to be happening more often.
Some industry participants and academics are pushing to improve the
Monte Carlo tools' ability to highlight the risk of major market
slides.
<rest of article at link>
My comment: the standard deviation of daily stock market returns
varies drastically over time, by perhaps a factor of 8 -- annualized
volatility of daily returns has ranged from 10% to 80% (experienced in
October and November 2008), as is reflected by the VIX index. To be
more realistic, Monte Carlo simulations should incorporate "stochastic
volatility".
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Posted by FranksPlace2 on May 4, 2009, 8:52 am
> Some industry participants and academics are pushing to improve the
> Monte Carlo tools' ability to highlight the risk of major market
> slides.
>
Monte Carlo simulates random events. The housing mess is the result
of a bunch of crooks selling homes to people who couldn't afford them
combined with "insurance" that had no reserve.
The outcome, in hindsite, is fully deterministic.
Frank
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Posted by Augustine on May 4, 2009, 11:29 am
>
> Monte Carlo simulates random events. The housing mess is the result
> of a bunch of crooks selling homes to people who couldn't afford them
> combined with "insurance" that had no reserve.
>
> The outcome, in hindsite, is fully deterministic.
And all that was necessary was a random trigger, even an unrelated
one, in this case, high oil and food prices, was enough to make the
castle of cards crumble down. Talk about a "hind" sight...
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Posted by Andrew Koenig on May 5, 2009, 1:41 pm
> My comment: the standard deviation of daily stock market returns
> varies drastically over time, by perhaps a factor of 8 -- annualized
> volatility of daily returns has ranged from 10% to 80% (experienced in
> October and November 2008), as is reflected by the VIX index. To be
> more realistic, Monte Carlo simulations should incorporate "stochastic
> volatility".
My comment is that it is a logical fallacy to apply the term "standard
deviation" to market behavior.
The argument is much more complicated than I can make in this small space,
but if you're intersted, you can learn more than you ever wanted to know
about it here:
http://www.amazon.com/Misbehavior-Markets-Fractal-Financial-Turbulence/dp/0465043577
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Posted by Tad Borek on May 5, 2009, 2:25 pm
Andrew Koenig wrote:
> My comment is that it is a logical fallacy to apply the term "standard
> deviation" to market behavior.
>
> The argument is much more complicated than I can make in this small space
Agree 100%. Or as I like to put it, any list of numbers has a standard
deviation, but that doesn't mean it's meaningful.
Coincidentally I was just reading a white paper by one of the MC
vendors, that speaks of a mean absolute error of 11.5% in predicting
returns in one of the stock indices (the Nasdaq 100 - a whole issue in
itself, but that's another post). In the world of science and
engineering the methodology would then be discarded, as unworkable -
imagine you were trying to use MCS to predict the failure rate for a
part on an aircraft to determine manufacturing processes and came up
with an error like that. In finance it becomes a "best practice" in some
circles because while it's so far out of bounds as to be arguably
useless, it's slightly better than using the more-invalid "project the
past few years forward to the next year."
That said I think the technique is helpful just to illustrate variable
returns - that visual, showing all those trajectories where sometimes it
goes to $0, sometimes sky-high, and mostly in the middle. But god forbid
you then pretend that the probabilities it spits out have meaning for
decision-making. Even a layperson should be able to see the problem with
running "tens of thousands or hundreds of thousands of scenarios, which
help gauge extreme events at the tail end of the distribution." You
could run it a billion times, you're still limited by the lack of data
and (more fundamentally) the fact that "hijacked jet flies into
building" and "laws change to facilitate bad lending" and "bad guy with
bad mustache takes over Europe" and "for no apparent reason stocks drop"
can't be described using probability theory.
-Tad
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