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View getcatalog getcatalog findCatalogService makeConnection executeQuery getCatalog Makes a JDBC call to the Pointless database. getCatalog getProductCatalog return Product return String parse XML return return String return String build XML



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The JAXM specification also does not mandate the use of any particular communication protocols; the current support focuses around industry standards such as HTTP, SMTP, and FTP JAXM clients can be either message provider-driven or non-managed The API can be used by non-managed clients (standalone applications) for building SOAP-compliant XML messages and for sending them directly to the final destination In a non-managed environment, the client and its destination can communicate only in a point-to-point fashion Message providers handle the transmission and routing of messages from the sender to receiver(s), as shown in Figure 76 By using message providers, asynchronous communication can be achieved where the underlying architecture is based upon a Java Messaging Service (JMS) implementation For more information on JMS, refer to http://javasuncom/products/ jms/ The JAXM specification does not mandate the use of any particular messaging architecture.





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The provider architecture is completely transparent to the JAXM sender or receiver In a message provider environment, a sender builds a SOAP-compliant message by using a JAXM API The sender then sends a message by going through its provider, at which point the message is then transmitted over the network and is forwarded to the receiver s provider The receiver then gets the message and processes it using the JAXM API Figure 76 illustrates a simple JAXM messaging example of sending and receiving SOAP messages using HTTP..

=IF(SUM($B$1:$B$15)<>15,INT(RAND()*30+1),A1)

In this regard, the Normality assumption appears particularly handy, given that little has to happen in the markets for its dictums to be violated; that is, you don t have to wait for a really big tumble in order to be able to use your conveniently helpful scienti c alibi As soon as things deviate moderately and you begin to incur a few losses, the event can (courtesy of Normality) already be deemed a rare one and the excuse would be pleasantly on Almost any setback could then be categorized as probabilistically very unseemly, and thus beyond your realm of responsibility.

y (t)= (k Te ) h (t k Te )

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After all, can they really blame you for suffering from a risk that Nobel-endowed methods say would be beyond the 99 percent con dence interval You can always say that you did the right thing, probabilistically speaking, by exposing your investors to losing money only if the odd, weird, strange 1 percent phenomena took place Normality allows you not only to rationalize almost any losses, but also to appear as stringently cautious Of course, for this to fully work you need investors who don t understand that what you call 1 percent might actually be something like 30 percent, and what you call 99 percent might actually be something like 70 percent Taking positions that could be destroyed if a large unfavorable move takes place and admitting that you were aware of the (huge) risks beforehand is certainly not politically correct.

/*********************************************************************************/ MonteCarloFaureQuasiRandom : values a European call option using Faure sequence for variance reduction [in]: double S : asset price double X : strike price double vol : volatility double rate : risk-free rate double div : dividend yield double T : option maturity long N : number of time steps long M: : number of simulations [out] : double : callValue **********************************************************************************/ double MonteCarloMethod::MonteCarloFaureQuasiRandom (double S, double X, double vol, double rate, double div, double T, long N, long M) { int i, j, k; double dt = T/N; // step step double mudt = (rate - div - 0.5*vol*vol)*dt; // drift double voldt = vol*sqrt(dt); // diffusion term double sum = 0.0; double sum1 = 0.0; double lnSt, lnSt1, St, St1; double lnS = log(S); double deviate = 0.0; double callValue = 0.0;I double SD = 0.0; // standard deviation double SE = 0.0; // standard error vector<double> x; // stores Faure sequence cout.setf(ios::showpoint); cout.precision(3); k = 0; for (i = 1; i <= M; i++) { // generate Faure sequence x = util.generateFaure(N,M); // initialize log asset prices for next simulation path lnSt = lnS; lnSt1 = lnS;

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