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Just as rating agencies rate the creditworthiness of companies, rating agencies often rate the creditworthiness of the products issued by those companies. The simple reason for this is because how a product is constructed most certainly has an influence on its overall credit risk. Product construction involves the mechanics of the underlying security ( 1) and the cash flows associated with it ( 2). To give an example involving the former, consider this case of bonds in the context of a spot profile. Rating agencies often split the rating they assign to a particular issuer s short-term bonds and long-term bonds. When a split maturity rating is given, usually the short-term rating is higher than the long-term rating. A rationale for this might be the rating agency s view that shorter-term fundamentals look more favorable than longer-term fundamentals. For example, there may be the case that there is sufficient cash on hand to keep the company in good standing for the next one to two years, but there is a question as to whether sales forecasts will be strong enough to generate necessary cash beyond two years. Accordingly, short-term borrowings may be rated something like double A while longer-term borrowing might be rated single A. In sum, the stretched-out period of time associated with the company s longer-dated debt is deemed to involve a higher credit risk relative to its shorter-dated debt. Now consider an example of bonds in the context of a spot versus forward profile. As 2 showed, an important variable distinguishing a spot and a forward is the length of time that passes from the date of trade



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From the above assumptions, the expected cash ow assuming full availability is the following: E(Pk (H(G + g)) + V)M1 = 5,068,800 dollars k The variance in the price is Var(Pk) = 100: SD(Pk) = 10 dollars/MWH Var(Pk (H(G + g)) + V)M1 = Var(W1 ) = 8,294,400,000,000 k k SD(Pk (H(G + g)) + V)M1 = SD(W1 ) = 2,880,000 dollars k k For all units, E(Avj) = 92 For all units, Var(Avj) = 0625 and SD(Avj) = 25 Corr(W1 ,B) = 1 (this is the same as Pk = B) k SD(W 1 ) SD(W 1 ) Corr(W 1 ,B) = E(Av) Corr(W ,B) = E (Av) SD(W ) SD(W ) With these assumptions we can assess the risk of Example 4 SD(W ) = n VarAv k VarWk1 + [EWk1 ]2 VarAv + n[EAv k ]2 VarWk1 Var(Avk)Var(W1 ) = .





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the gure are per unit length of the interconnection. Capacitive couplings of the line with its rst and second neighbors are also shown in the gure. The series resistance and inductance elements for the neighboring interconnections will not affect the propagation characteristics along the interconnection in question; therefore, these are not included in the model. The symbols used in the equivalent circuit of Fig. 3.2.1 are de ned as follows: R resistance of interconnection line L inductance of interconnection line Ci self-capacitance (i.e., capacitance between conductor and ground plane) of interconnection line Cil1 mutual capacitance between interconnection and its rst neighbor on its left Cir1 mutual capacitance between interconnection and its rst neighbor on its right Cil2 mutual capacitance between interconnection and its second neighbor on its left Cir2 mutual capacitance between interconnection and its second neighbor on its right Cl1 self-capacitance of rst neighbor on left of interconnection Cl2 self-capacitance of second neighbor on left of interconnection Cr1 self-capacitance of rst neighbor on right of interconnection Cr2 self-capacitance of second neighbor on right of interconnection As far as the propagation along the interconnection is concerned, the effect of its coupling with its rst right neighbor is to connect an impedance Z1 in parallel with the capacitance Ci , where Z1 Cir1 Cr1 sCr1 Cir1 3:2:1

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This work is compelling but has mostly remained centered around single devices as opposed to distributed systems composed of many devices It is still observed that there is a very wide gap between the new design materials at hand (eg smart artifacts, ad hoc networks, location technologies) and the potential applications For example, microprocessors and wireless radio can now be built into practically everything to create smart networked objects, but we lack the technology to integrate these in an open platform for a wide range of applications Likewise, the components are in place for prototyping and exploring application ideas, but we lack the foundations for the principled study of ubiquitous systems and application designs..

0625 8,294,400,000,000 = 518,400,000,000 k [EW1 ]2Var(Av) = 25,692,733,440,000 0625 = 1,605,795,840,000 k [EAvk]2Var(W1 ) = 7,020,380,160,000 k Table 104 provides the risk of the future earnings, SD(W), as a function of the number of units, n There are several things to note For a single unit the risk assuming full availability is 2,880,000 dollars This compares to 3,024,000 dollars when availability is considered The uncertainty of availability adds 144,000 dollars in risk This represents 48 percent of the total risk As the number of units increases, the total risk does not increase linearly Indeed, the risk of 10 units is substantially less than 10 times the risk of 1 unit The risk of increasing the number of units in the portfolio by 1 decreases as the number of units gets larger.

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