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The 1988 Capital Accord represented the rst step toward risk-based capital adequacy requirements. However, the blunt-instrument nature of the 1988 Accord gave banks an incentive to engage in regulatory arbitrage, that is, using credit derivatives and loan securitization to decrease the percentage of the credit extended to high-credit-quality obligors (which attract too much regulatory capital). The 1988 Accord was an agreement by the members of the Basle Committee on Banking Supervision with respect to minimum regulatory capital for the credit risk. Under these rules, the minimum regulatory capital associated with loans or other cash assets, guarantees, or derivative contract is calculated as Capital = Risk Weight Exposure 8% The risk weight for a transaction is determined by characteristics of the obligor (except for loans fully secured by mortgages and derivatives). Exhibit 1.6 provides illustrative risk weights. The exposure is determined by the type of instrument. For fully funded loans or bonds, the exposure is the face amount. For unfunded commitments, the exposure is 50% of the commitment for undrawn commitments with maturity over one year and 0% of the commitment for undrawn com-



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In most jurisdictions there is a presumption of capacity. Once outcome and diagnostic (or status) approaches are rejected, it becomes a more dif cult to ascertain when capacity to consent to treatment should be assessed. Grisso and Appelbaum (1998, ch. 4) recommend that there are four circumstances in which this should be considered:

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4 A beta can be calculated with an ordinary least squares (OLS) regression. Consistent with the central limit theorem, any OLS regression ought to have a minimum of about 30 observations per series. Further, an investor ought to be aware of the assumptions inherent in any OLS regression analysis. These assumptions, predominantly concerned with randomness, are provided in any basic statistics text.

High signal in the upper cervical cord due to multiple sclerosis (arrow).

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where each pi is the probability that the value xi occurs Note that when we make a measurement in the real world, we give equal importance to all of our measurements by adding them all up (the xi s) and dividing by the total number of measurements In a manner of speaking, each measurement is given an equal probability or weight of 1/N When we know the underlying distribution, we know that each probability pi is a function of the possible outcome value, so we have pi = f(xi), where f(xi) is the functional form of the probability distribution (you will see many more of these in just a bit) If all the pi s are equal, then you have a uniform distribution and you can take out the common factor If not, we have to do the multiplication rst (as we mentioned earlier).

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All benchmarks presumably have some convention that is used to reinvest proceeds generated by the index. For example, coupons and prepayments are paid at various times intramonth, yet most major indices simply take these cash flows and buy more of the respective index at the end of the month generally, the last business day. In short, they miss an opportunity to reinvest cash flows intramonth. Accordingly, portfolio managers who put those intramonth flows to work with reverse repos or money market products, or anything else, may add incremental returns. All else being equal, as a defensive market strategy portfolio managers might overweight holdings of higher coupon issues that pay their coupons early in the month.

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Expected Value and Expectation As already discussed, taking an expected value of some variable (let s just call it x ) which has a known distribution [let s say f(x)] is equivalent to multiplying each possible value of x by the corresponding probability it will occur [ie, the probability distribution function f(x)] and summing up all the products We took the example of rolling a fair die But we can also think of an example of a measurement that does not take on only discrete values Let s say we want to model the heights of people off the street We may have a theory that the distribution of heights is not uniform (as in the case of the roll of a fair die), but has some speci c functional shape given by the probability density f(x) (we see many shapes of distributions in this appendix).

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