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that the U.S. federal government will not go into default in the next three months and renege on its debts.1 If we did in fact believe there was a chance that the U.S. government might not make good on its obligations, then we would have to adjust downward our 100 percent recovery assumption at maturity. But since we are comfortable for the moment with assigning 100 percent probabilities to both of our Treasury bill cash flows, it is possible for us to state with 100 percent certainty what the total return on our Treasury bill investment will be. If we know for some reason that we are not likely to hold the threemonth Treasury bill to maturity (perhaps we will need to sell it after two months to generate cash for another investment), we can no longer assume that we can know the value of the second cash flow (the sale price) with 100 percent certainty; the sale price will likely be something other than par, but what exactly it will be is anyone s guess. Accordingly, we cannot say with 100 percent certainty what a Treasury bill s total return will be at the time of purchase if the bill is going to be sold anytime prior to its maturity date. Figure 2.1 illustrates this point. Certainly, if we were to consider what the price of our three-month Treasury bill were to be one day prior to expiration, we could be pretty confident that its price would be extremely close to par. And in all likelihood

5000 4500 4000 3500 3000 kbps 2500 2000 1500 1000 500 0 Average 10 % 50 % 90 % 5-code 10-code 15-code

However, a price is paid in that the effective SNR is signi cantly reduced in proportion to the number of satellites being tracked Resistance to jamming is also degraded by values of 7 dB or more Additionally, the process of acquiring new satellites without disruption is made more demanding because the acquisition search must be broken into numerous short time intervals Due to the rapidity with which satellites are sequenced, a common practice with a two-channel receiver is to use a.

a dom(A) b dom(B)

2 Next select a case:

Before moving on to the pricing of callable structures, we note that the signature of the function call operator for the Monte-Carlo pricer differs from that of the lattice pricer. The function call operator now takes an optional list of tuples representing symbol-value pairs that need to be inserted into the symbol table prior to the evaluation taking place. The reason for this addition is down to the strongly path-dependent nature of the type of payoffs evaluated by the Monte-Carlo pricing framework. The strongly path-dependent nature implies the existence of variables whose history influence the payoff and such variables need to be initialised to some starting values. 9.2.2 Pricing Callable Trades The pricing of callable structures entails findin the optimal exercise rule by solving an optimal stopping time problem. Once the optimal stopping time rule has been discovered, the pricing of a structure simply involves the computation of the expected discounted payoff, subject to the stopping time rule. Let V (t) denote the discounted payoff from exercise at time t and T be the class of stopping times with values in [0, T ], where T is the last exercise time. The problem then becomes to fin the optimal expected discounted payoff sup T E[V (t)]. (9.5)

The JSP uses Java code stored in a class called CustomerBean to verify the account ID and to check the entered password. Before looking at the code in CustomerBean.java, which does the actual checking, here s the JSP code that accesses the code in CustomerBean.java:

At its 2003 fiscal year-end, a 300-bed, nearly 2,000-employee nonprofit Midwestern medical center found itself faced with millions of dollars in red ink Requirements for pension funding for its defined benefit pension plan and the financial failure of a related physician practice contributed to the problem In addition, the medical center had, over time, begun to run a little rich, with a per-patient/per-bed cost that significantly exceeded the average for successful, profitable hospitals Most of these positions were in midlevel management; some were in facilities management Complicating the problem, this organization had a large number of long-tenured employees Many had worked at the medical center for 20 or even 30 years, starting as radiology technicians, for example, and working their way up to top management positions People there had known each other and worked together for many years in a small, close-knit community.