There are multiple methods of measuring investment risk. Several popular methods utilized by investment managers:

VAR (historic, variance-covariance, and Monte Carlo)

Sharpe Ratio

Modified Sharpe Ratio

Standard Deviation


Ulcer Index

Information Ratio

Intra-Portfolio Correlation


For investment capital attached to future outlays such as pension obligations and retirement account distributions, surely Shortfall should be included in the risk assessment tools mix. For those asset managers who are not tethered to an Index, Information Ratio is of little use and is clearly inferior at measuring absolute risk. Furthermore, for all but the most stoic of investment managers, Standard Deviation and Sharpe Ratio are deficient because they don’t differentiate pro-position and counter-position volatility.

Although each remaining method has strengths and drawbacks, all successfully measure risk. Maybe, a combination of methods throughout the investment decision-making process will permit a more comprehensive analysis of risk. Consider it ‘risk analysis diversification’, if you will.

One potential avenue could be to first figure out which individual strategies to deploy within a multiple strategy portfolio. This can be accomplished by using the relatively simple to compute and compare, and fittingly named, Ulcer Index (at least to those whose gastric ailment is not rooted by a H. pylori infection). Ulcer Index is apposite because its formula contains, instead of standard deviation as is used in Sharpe Ratio, the sum-of-root-mean-square of all periods’ percent-drawdown. Vital to maximizing precision is using long-term historic data. Importantly, data should include periods of extreme price excursion. If there is no long term data, generate a synthetic long term series, or if possible, select

a highly probable highly and positively correlated surrogate asset. Normalize the surrogate to the actual asset’s volatility over a comparable time period (compare periods after regular high volume has been maintained in a new issue). Next, Ulcer Index can be computed for each strategy’s returns over a uniform time period. Then, rank and check for correlation among strategies and choose accordingly.

Lastly, to engineer the desired overall portfolio risk/reward profile, optimize expected Intra-Portfolio Correlation and Modified Sharp Ratio. For accuracy, Modified Sharp Ratio’s (which incorporates VAR) inputs should be projections, partly extended from historic and if necessary synthetic data. Do expect to make regular revisions.

Of course, there are other combinations of methods to achieve risk analysis diversification benefits; this is but one example

Russ Sears writes:

The problem with these measures are they cannot measure the risks of cannibalizing the future for short term gains. The more a model is used, the clearer it is to all those involved how to tip the scales in their favor. Remember the chaos of the gyspy moth, overbreeding until the trees cannot regenerate leaves quick enough for the last generation.

Take VAR, its lack of liquidity measure, and do a cursory scan of the role it played in the overallocation to sub-prime, mortgages, real estate and structured credit or rating agencies, AIGFP, SIV's, banks and insurance companies required capital. Even those titans of business Havard and Yale fell for these gapping holes in measuring risks and performance. Soon everybody is overallocated to the same thing. Then suddenly everybody is surprised when the door is not big enough when the liquidity fire alarm is pulled.

For diversification of risks measures, I have had much more luck understanding the weakness of the model and trying to prevent myself or a company taking my advice from letting the weakness of the model predetermine my allocation or strategy. The genuis of Chaos Theory, in my estimation, is clearly understanding what a model does not and cannot measure. Then you won't expect it to measure all risks and are not alarmed by those that cannot see they stepped off the edge of a cliff. 

Anton Johnson responds:

Mr. Sears makes exceptional observations, and to extend on those, I would add that the endowment funds’ problems were exacerbated by inexcusable errors in basic portfolio management. By simply monitoring the portfolio and maintaining prudent volatility adjusted component size limits and intra-portfolio correlation level, much of the funds’ losses engendered by the managers’ usage of VAR, and their apparent ignorance of its shortcomings, would have been mitigated. Of course, the root cause of their problems was unfortunate asset selection and poor foresight; and that is an entirely different topic.


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