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the money, but it is a reality nonetheless Again, this is driven by supply and demand If you have an abundant supply of option writers away from the market or a weak group of buyers, the implied is likely to retreat If the market appears to have little or no potential for growth or disaster, you will see a similar situation What we can learn from the volatility smile is to spot when we have distortions that we may be able to capitalize on Selling call and put premiums on the outside edges of a steep volatility smile is likely to be a good opportunity to capture the volatility Someone is or has been willing to buy these options at high volatility for the smile to exist so you must be certain of your fundamental and technical analysis You can extrapolate from the smile what strategies might be most effective in each situation You have to reverse your thinking and consider trading implied volatility and not the market fundamental itself If volatility is low in the-at-the-money options and elevated on both ends, we might consider a short out-of-the-money condor, short strangle, long straddle, long at-the-money call or put, bull call or bear put spreads, and ratio spreads covered calls and puts All of these take advantage of out-of-the money implied volatility For elevated implied volatility in the center like the frown, we have another set of trading strategies: a short iron butter y, short straddle, long strangle, back spreads, long out-of-the-money calls or puts, and long condors These trades take advantage of your being able to purchase low out-of-the-money implied volatility in relation to the at-the-money positions Pricing your option trades based on volatility as much as on the price and on market fundamentals will do a number of things for your trading:.

The following subsections will de ne vectors xa and xg and matrices Fva and F g such that the instrument errors can be modeled as f b b ib = = Fva xa + a F g xg + g (11100) (11101)

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where a and g represent measurement errors that can be accurately represented as white and Gaussian The matrices Fva and F g will be known The vectors xa and xg contain the parameters necessary to calibrate the accelerometer and gyro Speci c de nitions for Fva and xa will be discussed in Section 1162 Speci c de nitions for Fvg and xg will be discussed in Section 1163 The models derived in this section have two primary purposes to the system analyst First, the models are necessary for the speci cation of a truth model as required for covariance analysis Second, when it is determined that an error source has a signi cant e ect on the navigation system error and that it is possible to estimate the parameters pertinent to describing the error source, then the parameter estimates can be augmented to the error state vector of the implemented lter Common candidates for augmentation to the error state vector (ie, xa and xg ) include3 : accelerometer bias (3), accelerometer scale factor (3), accelerometer non-orthogonality (6), accelerometer nonlinearity (18), gyro bias (3), gyro scale factor (3), gyro non-orthogonality (6), gyro acceleration sensitivity (27), and calibration factors for additional sensors The nal decision as to which additional

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3 The number in parenthesis indicates the number of states that would be augmented to the nominal state vector to model the listed error These numbers are de ned in Sections 1162 and 1163

error states should be included in the implemented lter involves a tradeo between the cost of increased computation and the bene t of increased accuracy The tradeo results are di erent for each application Quantitative decisions can be based on covariance or simulation analysis only if the truth model is accurate Given the models of eqns (11100 11101), the estimates of the instrument calibration factors are b f ib b = Fva xa = F g xg (11102) (11103)

Software Development Labs (SDL), later known as Oracle, is founded Teradata is founded and creates a tightly integrated decision support system Britton Lee is founded and creates a database accelerator platform Oracle is rewritten in C programming language for portability across platforms DB2 for MVS on IBM mainframes is introduced Oracle database is introduced for VAX clusters IBM introduces the AS/400 with DB2/400 for mid-range transaction processing Oracle Parallel Server for Unix-based systems scales large data warehouses Netezza is founded with the intention of creating a data warehouse appliance Oracle9i with Real Application Clusters can scale any Oraclebased application Oracle Database 10g enables grid computing and features Automatic Storage Management and the Automatic Database Diagnostics Monitor Oracle announces support of Red Hat and SUSE Linux Oracle announces Oracle Enterprise Linux Oracle announces Optimized Warehouse Initiative reference configurations (described below) Oracle announces the HP Oracle Database Machine and Exadata Storage Servers especially for data warehousing Oracle announces the Sun Oracle Database Machine (Exadata Version 2) for data warehousing, transaction processing, and consolidation of databases Oracle completes the acquisition of Sun; Oracle announces Exadata X2-2 and X2-8 nodes, Solaris 11 support for Exadata, an Unbreakable Enterprise Kernel for Linux, Oracle Exalogic, and the Exalogic Elastic Cloud

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