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less than in the zero volatility change scenario Using the same 5-point drop in volatility on the purchase of the 10 October 2100 call options, we would see them fail to gain about $26300 compared to the original scenario With the volatility shift the out-ofthe-money calls have gained less than the in-the-money call purchase The opposite would be true if the direction of the volatility shift was positive The vega of the out-of-the money options would be multiplied by the number of options, thus giving a substantial boost to the value of the options The out-of-the-money calls would pro t substantially faster than the in-the-money call if the volatility were to shift 5 points to the positive The strategy of buying multiple out-of-the money options versus the at-the-money call or in-the-money calls is often referred to as delta positioning or delta trading What you are trying to accomplish by delta trading is to achieve the desired long delta with similar premium costs that will compound as the market moves and builds more value You can see from the example that in extreme market movement where the underlying asset moves far enough to place both strike prices deep in the money, the pro tability of the option trade increases dramatically for the multiple option purchase Some traders will go further and use deep out-of-the-money options and buy several options instead of a smaller number of at-the-money or near to the money options This strategy can certainly be effective in markets that move sharply up for call options or down for put options, but it is also a recipe for losing if the market enters a trading range over much of the life of the option If too much time value elapses from the options, it will take an extreme market move to make up the lost premium much less put the options into a pro table situation.

Great performance occurs when a system is well balanced such that it can deliver the needed volume of data residing in storage to fully use the processing power of the CPUs which, in turn, return results to the business users in time for them to make decisions Balancing large storage volumes, speedy and powerful processors, and adequate throughput is fundamental to the design of an optimal system Of course, selection of all of these components by your platform vendor must also include having an eye on the pricing of the components and of the overall system that is assembled Since the evolution of key system components proceeds at different rates over time, maintaining a balanced system over that system s lifetime presents its own set of challenges The Oracle Exadata Database Machine holds a reputation for delivering such a balanced system, dating from the platform s introduction through its subsequent iterations We ll next explore how the components work together as an optimal system and will consider the roles of the Intel-based CPUs, memory, InfiniBand interconnect, and flash and disk storage technologies in the Database Machine

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Using the convention that gradients of scalar functions are de ned as row vectors, d (u v) dv d (Av) dv d v A dv d v Av dv = = = = = d (v u) = u dv A A v A+A (B49) (B50) (B51) (B52) (B53)

The concept of balanced computer systems has been well understood since the earliest computers were designed by hardware engineers Early efforts by these engineers mandated specific custom processors, memory, and storage to form the systems The invention of the microprocessor built upon the integrated circuit suddenly changed the speed of development and dynamics in the creation of new systems As CPUs were supplied by a variety of competing vendors, the speed of change in this important component in a computer system escalated faster than some of the other key system components Processors began to double in performance every 12 to 18 months, and memory capacities increased at a similar rate of change But increases in speed of disk drives, relying on mechanical parts, occurred much more slowly Disk capacities did increase at escalating rates, though, and so retrieving data became more problematic because of throughput limitations To regain system balance, more disk spindles were needed to deliver data off of disk More connections to storage over faster interconnects from the disks to memory were also needed So how does a system like the Oracle Exadata Database Machine deliver large data volumes from storage to the processors and eliminate time delays

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