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This book is targeted toward readers who may have a familiarity with information systems but are not necessarily familiar with enterprise Java technologies and architectures. The book does assume that the reader has a basic familiarity with Java and does not provide an introduction to Java programming or to programming for J2EE platforms. Source-code examples are
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After the conversion from A-law or -law PCM to uniform PCM, the input signal is partitioned into blocks of five consecutive input signal samples. For each input block, the encoder passes each of 1 024 candidate codebook vectors (stored in an excitation codebook) through a gain scaling unit and a synthesis filter. From the resulting 1 024 candidate quantized signal vectors, the encoder identifies the one that minimizes a frequency-weighted mean-squared error measure with respect to the input signal vector. The 10-bit codebook index of the corresponding best codebook vector (or "codevector"), which gives rise to that best candidate quantized signal vector, is transmitted to the decoder. The best codevector is then passed through the gain scaling unit and the synthesis filter to establish the correct filter memory in preparation for the encoding of the next signal vector. The synthesis filter coefficients and the gain are updated periodically in a backward adaptive manner based on the previously quantized signal and gain-scaled excitation (see Figure AP3.1-1 a).
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with the Black market formula for cap and swaption prices, which makes calibration to market data simple since the quoted Black (implied) volatilities can be directly inputted into the model, avoiding the numerical tting procedures that are needed for spot or forward rate models. Second, market models are based on observable market rates such as LIBOR and swap rates. They do not depend on, and thus one does not require, (unobservable) instantaneous spot and forward rates. However, due to the complex dynamics of LMMs and the fact that the forward rates have a fully state-dependent drift, recombining lattices/trees cannot be used to evolve interest rate dynamics, and thus price interest rate derivatives, as they can for instantaneous spot and forward rate models. Instead, Monte Carlo and other numerical techniques must be used to price caps, swaptions, and other (exotic) interest rate derivatives. The BGM model, known also as the lognormal forward-LIBOR model (LFM), prices caps consistently with Black s formula. Consequently, implied volatilities quoted in the market are consistent with speci ed volatility structures used in the model. The lognormal forward-swap model (LSM), developed by Jamishidian (1997), prices swaptions consistently with Black s swaptions formula. However, the LFM and LSM are not compatible with each other. If forward LIBOR rates are lognormal each under its measure, as assumed by the LFM, forward swap rates cannot be lognormal at the same time under their measure, as assumed by the LSM. Despite this drawback, the LFM allows for a deterministic calculation, and thus evolution, of the future term structure of volatilities. This calculation requires no simulation while for other models this is not the case. This chapter is broken down as follows. In section 13.1, we discuss the LIBOR market models. In section 13.2, speci cations of the instantaneous volatility of forward rates are given. In section 13.3, Hull and White s adaptation and implementation of the LMM is given. In section 13.4, calibration of the LFM to cap prices is discussed. In section 13.5, pricing swaptions with the lognormal LFS model is discussed, while in section 13.6, approximate swaptions pricing using Hull and White s approach is detailed. In section 13.7, an LFM formula that approximates swaption volatilities is given. In section 13.8, Monte Carlo pricing of swaptions using the LFM is discussed and an implementation is given. In section 13.9, an improved Monte Carlo pricing of swaptions is given using a predictor-corrector. An implementation of this approach is given. In section 13.10, incompatibilities between the LSM and LFM are analyzed. In section 13.11, instantaneous and terminal correlation structures are discussed in the context of the LFM. In section 13.12, calibration to swaption prices using the LFM is given, while in section 13.13, the connection between caplet volatilities and S 1-swaption volatilities is made so that one can calibrate to both the cap and swaption market. In section 13.14, we discuss incorporating the observed volatility smile seen in the cap market into the LFM. In section 13.15, we discuss Rebonato s (2002) stochastic extension of the LIBOR market model to deal with recent changing dynamics in the market that the LMM is not capturing. Finally, in section 13.16, we discuss computing Greek sensitivities in the forward LIBOR model.
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C(T ) =
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Figure 9-8. Structures of dibenzocyclooctadiene lignans 130 141 and dibrominated derivatives 142 146.
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message is the text to be displayed as a message. options are alternative choices that you may use to tailor a standard message box. Some of the options that you can use are default and parent. The default option is used to specify the default button, such as ABORT, RETRY, or IGNORE in the message box. The parent option is used to specify the window on top of which the message box is to be displayed.
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Part II
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Figure 19-15: Using a custom VBA function to apply conditional formatting to cells that contain a formula
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Space Op: Guardbands at the lower &upper bands (14/11MHz)
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From the folks at Indiana University comes XPP3 (XML Pull Parser), another XML pull parser that conforms to the XmlPull standard. Because of its small size and limited feature set, XPP3 is targeted towards the same environments as kXML. XPP3 s performance is even better than that of kXML. What it lacks is the visibility and support behind the kXML project. XPP3 is an excellent product that benefits from the freedom to innovate that comes from being an academic research project. See the following table for a summary of XPP3.
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Recommendation ITU-R S.579 provisionally stipulates that, in the FSS: the unavailability of a hypothetical reference circuit or digital path due to equipment should be not more than 0.2% of a year; the unavailability due to propagation should be not more than: 0.2% of any month for a hypothetical reference digital path; X% of any month for a hypothetical reference circuit (X being still under study, X = 0.1 being suggested).
Given the total level benchmark premium and surprise, we can decompose the value added due to currency effects. The forward premium effect is equal to the over- or underweight in the segment multiplied by the difference between the benchmark segment forward premium and the benchmark total forward premium. Forward premium effect = ( Fund segment weight Benchmark segment weight ) ( Benchmark segment forward premium Benchmark total forward premium )
In other versions of Windows, you can do this via the System icon in the Control Panel. Under Linux (and Bash) you can do this by adding the following line to the profile in the user s home directory:
[a-f] means any one character from the range a through f ^a means any lines beginning with a z$ means any lines ending with z
If condition Then true_instructions [Else false_instructions]
Another conditional construct available in JavaScript is the switch statement. The switch statement is similar to the if and else if statement. Recall the example of vowel testing in the else if statement. Although the code in that example was formatted properly, it still looks complex due to several blocks of statements to be tested and executed. You can write the same logic with a switch statement. It is preferable to use the switch statement as compared to an if and else if statement when you use multiple conditions. This is because the program is more efficient and easier to read and understand. The switch statement allows you to merge several evaluation tests of the same variable or expression into a single block of statements. The execution process of switch statements is similar to that shown in the preceding example of vowel testing. The syntax of the switch statement is as follows:
2: Basic Facts about Formulas
The cap is said to be ITM if R < RATM and OTM if R > RATM, with the converse holding for a oor. Caps can be valued with Black s 1976 model. If we assume the rate R(ti 1, ti) is lognormally distributed with some known (estimated) standard deviation of lnR(ti 1, ti) equal to (ti ) = (ti ; ti 1 , ti ) = ti 1 where is the common volatility parameter that is retrieved from market quotes at time t, then we can use Black s formula for a caplet Ci that pays at time ti : _ Ci = iNP(t, ti)(f(t; ti 1, ti) (d+) R (d )) (10.102) where P(t, ti) = e R(t,ti)(t ti) is the one-period discount bond, f(t; ti 1,ti) is the forward LIBOR rate for the period, ( ) is the cumulative normal distribution, and d = f (t ; t , t ) 2 (t ; t , t ) 1 i 1 i i 1 i ln 2 (t ; ti 1 , ti ) R
Applying Names to Existing Formulas
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