On Jan 14, 7:13*am, JoeTaxpayer <JoeTaxpa...[at]comcast.net> wrote:
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It's a shirt-sleeve calculation based on the average debt to book
ratio and average price to book ratio of the S&P 500 stocks.

Much financial debt comes from government obligations and home loans
so I am not sure what the entire investing universe looks like.

--
Ron

Ron Peterson wrote:

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Can you explain this? Why 25%? Where'd that number come from?
Joe

On Jan 3, 8:29*am, beliav...[at]aol.com wrote:
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http://www.fooledbyrandomness.com/ is referenced in the article and
gives a better insight into the problems of risk analysis.

Risk is relative because we don't know when the very unusual things
will occur.

Conventionally, investors are asked to split their investment between
stocks and bonds. If this were to be done using only S&P 500 stocks
and bonds, the average investor would be only 25% into bonds since
those companies aren't highly leveraged.

--
Ron

On Jan 5, 12:49*am, Igor Chudov <ichu...[at]algebra.com> wrote:
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Value at Risk and Expected Shortfall can be estimated for return
distributions other than the normal distribution, as discussed in a
paper "Measuring financial risk : comparison of alternative procedures
to estimate VaR and ES" at http://e-archivo.uc3m.es/dspace/bits...1/ws087326.pdf
. Taleb specializes in attacking straw men.

The article that beliavsky cited is, in fact, very interesting and
well researched, as are most NY Times articles these days.

At the core of the issue is that Value at Risk metric assumes that
asset returns are normally distributed and their correlations are
knowable and do not change much. The article expounded quite a bit on
the former (normal distribution), citing Nassim Taleb and other
luminaries in debunking the idea of normally distributed
returns. Which is quite correct.

What I would like to do is to attract attention to the second issue,
which is correlations.

As a result of assuming that correlations are knowable and do not
change, the risk of a portfolio comprised of various such assets, was
estimated to be considerably lower than the risk of owning one such
asset, due to those correlations. For example, the returns of
Ukrainian state debt would be not too correlated with portfolios
comprized of Arizona Alt-A mortgages. So if you mix Arizona mortgages
with Ukrainian debt, you could get decent return but low value at
risk. So your VaR analysis told you that it would be a relatively safe
thing to do to borrow some money, using these assets as colateral.

As we now know, that was not the case and they all were subject to
credit risk. In the crisis, as we heard, "all correlations went to 1"
and all sorts of securities started losing money in tandem. Which was
a mathematically impossible event, under assumptions of wrong models used.

Unlike those giddy times, now everyone is afraid of everything, and it
is a great time to invest prudently (not using too much leverage).

i

On Jan 3, 9:29*am, beliav...[at]aol.com wrote:
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Asking your indulgences, with an eye to a happy start to a new year, a
little humor from the farm ....

The other day, I saw a duck crossing the road. Curious about this,
because I thought only chickens crossed roads, I ambled over and got a
rare exclusive interview with the duck. "Chickens are talked about,"
he explained, "because they're hit by speeding tricycles more often.
Us ducks hardly ever get hit." "Are chickens dumber than ducks?" I
asked. "Oh, no!" the duck said, "They're much smarter, and have PhD's
in mathematics!" Puzzled, I asked, "Well, if chickens are smarter, how
come they get hit more often?" The duck smiled. "They use probability
theory and virtual risk management models. Then they strut around in
circles, and never see the actual tricycle that creams 'em. We ducks
are taught to look both ways before making our next move, and walk in
a straight line."

Prompted by the financial crisis, the latest New York Times magazine
has a long article "Risk Mismanagement" by Joe Nocera
http://www.nytimes.com/2009/01/04/ma.../04risk-t.html about the use
(some argue misuse) of the Value at Risk (VaR) measure by financial
institutions. A 99% VaR of $100 K for a 1 month time horizon is a
forecast that 99% of the time, the loss on a portfolio will be less
than or equal to $100,000. The article notes a defect of VaR -- it
says the losses in the worst 1% of scenarios will be worse than a
certain amount (in this example $100 K), but it ignores how much worse
they could be.

A risk measure called "expected shortfall" is defined to be the
expected loss for a specified time horizon and quantile (such as 1
month and 1%). Unlike VaR, it does depend on the magnitude of the
losses in the left tail. The Wikipedia article http://en.wikipedia.org/wiki/Expected_shortfall
defines it and presents examples of its calculation. Researchers in
risk management generally consider it a better risk measure than VaR,
and I wonder why the NYT article does not mention it in a ten-page
article.

Risk measure are relevant to individual investors as well as
investment banks. Given a method of simulating returns (either drawing
from historical returns or using a parametric distribution such as the
normal), one can estimate the expected shortfall of an investment
strategy in meeting some liability. For example, one could estimate
the expected shortfall from investing a $50K lump sum in the stock
market for 10 years and selling 25% of the portfolio over each of the
next 4 years in order to pay annual college expenses of $25 K. One
would need a distribution of stock market returns, perhaps normally
distributed with annualized mean of 8% and standard deviation of 16%.
Currently, financial planning software usually quantifies risk by
forecasting the probability of an investment strategy succeeding, for
example the probability of not running out of money over 20 years
given an initial withdrawal of 4% of the portfolio, increased annually
at the rate of inflation. This risk measure, like VaR, ignores the
magnitude of the failure when the failure occurs. It treats running
out of money in year 5 and year 19 as the same. An expected shortfall
measure may be better.