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asset classes and an additional four provisions that cover various alternative asset classes. Each of the nine provisions contains a number of requirements that need to be met before an investment management rm can claim AIMP PPS compliance. In addition, each provision contains some recommendations that do not need to be followed, but are strongly suggested. Here are some of the speci c requirements under each provision. A full list can be obtained via AIMR s web site. Provisions for Traditional Asset Classes 1. Input data. Rules for the inclusion and exclusion of performance data to ensure fair and comparable investment performance presentations. All data necessary to calculate AIMR compliant performance records must be available. Portfolio valuations must be based on market value. Portfolio valuation must be done at a minimum on a quarterly basis. 2. Calculation methodology. Rules to ensure all investment returns are calculated using the same calculation methodologies. Total return, which includes all realized and unrealized gains, must be used. Returns must be time weighted. Composites must be asset weighted. Trading costs must be deducted from performance. 3. Composite construction. When investment rms report performance for a given product, it is in the form of a composite. An investment management rm calculates a composite by aggregating all the portfolios they manage with similar investment objectives.

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Figure 2.2 How the level of personal interest affects the preferred content delivery mechanism. Reproduced by permission of Nokia

of entry Although we could always refer back to the actual historical prices at the time of entry to derive a percentage-based stop-loss level, there is no need to bother as there are a plethora of equally robust mechanisms for stop-loss placement that can be employed instead Point Value versus Percentage Changes in Data History A final issue applies not only to equalized continuation charts, but also to all of historical data This is the problem of point value changes as opposed to percentage value changes I will use equalized continuation charts to exemplify the issue Equalized continuation charts merely adjust the price difference between today s data and historical data, as illustrated by the lean hog example In many instances, if the asset in question has experienced a longterm bull market trend, then the price differences between entry and exit will be dramatically different from the percentage differences.

This chapter contains two independent subjects. In the rst part we use Rice formulas to obtain bounds for the tails of the distribution of the maximum of one-parameter Gaussian processes having regular paths. We also include some results on the asymptotic behavior of the tails of the supremum on a xed interval when the level tends to in nity. In the second part of the chapter we provide a quite detailed account of two examples of statistical applications of the distribution of the maximum, one to genetics and the other to the study of mixtures of distributions. These two examples share the common trait that statistical inference is performed in the presence of nuisance parameters, which are not identi able under the null hypothesis. In standard classical situations in hypothesis testing, the critical region consists of the event that some function of the empirical observations (the test statistic) falls into a subset of its value space. Computation of the probabilities that are relevant for the test, for large samples, follows from some weak limit theorem, allowing us to obtain the asymptotic law of the test statistic as the sample size goes to in nity. Typically, this will be a normal distribution or some function of the normal distribution. In certain more complicated situations, as happens when nuisance parameters are present and are not identi able under the null hypothesis, it turns out that a reasonable choice consists of using as a test statistic the supremum of a process indexed by the possible values of the nuisance parameter. Then, when passing to the limit as the sample size grows, instead of limiting distributions of nite-dimensional-valued random variables, we have to deal with

For example, let us assume that our equalized continuation chart for Nymex natural gas futures shows a long entry price of $1001 during August 1991 and an exit price of $1356 for a profit of $3,45000 per contract (trade profit was $3,55000 minus $10000 for slippage and commissions) Although the absolute price difference between entry and exit levels is correct, if we consider this difference in percentage terms based on August 1991 valuations, we can determine that the actual contract was trading at $150 and that a price move of $0355 represents a 2367% profit Now compare this same price move based on October 2003 natural gas prices of $600 and our 2367% profit shrinks to a mere 592%.