Call feature

For example, the objects may be chemical compounds. The individual components of a data vector are called features and may, for example, be molecular descriptors (see Chapter 8) specifying the chemical structure of an object. For statistical data analysis, these objects and features are represented by a matrix X which has a row for each object and a column for each feature. In addition, each object win have one or more properties that are to be investigated, e.g., a biological activity of the structure or a class membership. This property or properties are merged into a matrix Y Thus, the data matrix X contains the independent variables whereas the matrix Ycontains the dependent ones. Figure 9-3 shows a typical multivariate data matrix. 

One can — and sometimes must — reduce the number of features. One way is to combine the original variables in a smaller number of latent variables such as principal components or PLS functions. This is called feature reduction. 

There are three eomponents to the empirieal approach of developing a predietive elas-sifier. The first component is determining whieh genes to inelude in the predietor. This is generally called feature seleetion. Including too many noise variables in the predictor usually reduces the aeeuracy of predietion. The second component is speeifieation of the mathematical function that will provide a prediction for any given expression veetor. 

A pharmacophoric element (also called feature) is generally defined as an atom or a group of atoms (e.g. a hydrogen bond donor atom or an aromatic ring system) common to active compounds with respect to a target protein and essential for the activity. Thus, a pharmacophore model can also be regarded as the representation of a collection of pharmacophore features. 

The various stages in the signal processing of an electronic nose system are illustrated in Figure 11 in which the signals generated from an array of sensors (or, for example, pseudo-sensors in a mass spectrometer) are first pre-processed and then fed in to a pattern analyser. In other words the n dimension vector in sensor space is transformed into feature space (a process called feature extraction) and then identified using some form of pattern classifier. 

In the TRUE -1 test it was expected that most flow would be in what was called Feature A. 

The so-called feature tree fragment space (FTree-FS) method uses dynamic programming techniques and allows the detection of the molecule with the highest similarity to the query that can be found in the fragment space. The efficiency of searches in fragment spaces versus searches in product spaces can be easily shown. 

Convertible instruments are usually issued with attached call or put options. Such features can be implemented into the valuation model. If a soft call feature has been implemented, it enables the issuer to force the conversion when the share price overcomes a percentage or trigger level above the conversion price. However, this option cannot be called in the first years hard call . Differently, after the protection period, the issuer can exercise the option. This second time is referred to soft call . Using the same example shown in Section 9.3.1, we assume that the bond may be redeemed in whole but not in part at their principal amount plus accrued interest on the last 2 years, in which the maturity date is at 20 February 2019. On and after this call date , if the share price exceeds 130% of the conversion price the issuer can force the conversion. Figure 9.23 shows the stock price tree in which at years 4 and 5 the stock price is above the threshold. 

Figure 9.25, the added call feature reduces the value of the convertible bond from 107.5 to 104. 

Determine the Value of an Option-Free Bond After determining the evolution of the interest rates, we calculate the value of the option-free bond. In this case, we develop a binomial tree by ignoring the call feature in which at maturity the value of bond is 100. Although the final value could be equal to 104 (principal plus coupons), we consider at maturity the bond s ex coupon value. In fact, at year 5 the bond s price is 100. 

Kish, R., Fivingstone, M., 1992. The determinants of the call feature on corporate bonds. J. Banking Financ. 16, 687-703. 

Some bonds include a provision in their offer particulars that gives either the bondholder and/or the issuer an option to enforce early redemption of the bond. The most common type of option embedded in a bond is a call feature. A call provision grants the issuer the right to redeem all or part of the debt before the specified maturity date. An issuing company may wish to include such a feature as it allows it to replace an old bond issue with a lower coupon rate issue if interest rates in the market have declined. As a call feature allows the issuer to change the maturity date of a bond it is considered harmful to the bondholder s interests therefore the market price of the bond at any time will reflect this. A call option is included in all asset-backed securities based on mortgages, for obvious reasons. 

The discussion is easily expanded to include risky floaters (e.g., corporate floaters) without a call feature or other embedded options. A floater pays a spread above the reference rate (i.e., the quoted margin) to compensate the investor for the risks (e.g., default, liquidity, etc.) associated with this security. The quoted margin is established on the floater s issue date and is fixed to maturity. If the market s evaluation of the risk of holding the floater does not change, the risky floater will be repriced to par on each coupon reset date just as with the default-free floater. This result holds as long as the issuer s risk can be characterized by a constant markup over the risk-free rate. 

When the issuer has the option to call the notes on a specified date, the interest margin on the notes will usually increase. This gives the originator an additional economic incentive to arrange for the notes to be called. However, in certain jurisdictions this type of call may prevent the off-balance sheet treatment of the securitised loans, so this step and call feature is not found in all transactions. 

The steps in developing a two-point perspective are simple. Study the steps in Figure 5-23 to understand how to sketch a two-point perspective. Remember, objects or parts of an object such as holes, pins, slots, and braces (called features) will appear to be shorter and closer together the closer they get to vanishing points. Look at Figure 5-18 again to observe this perspective concept. 

The next step is to use this tree to describe the bond s price evolution, ignoring its call feature. The tree is constructed from the final date backwards, using the bond s ex-coupon values. At each node, the ex-coupon bond price is equal to the sum of the expected value plus the coupon six months forward, discounted at the appropriate six-month yield. At year 3, the bond s price at all the nodes is 100.00, its ex-coupon par value. At year 2.5, the bond s price at the highest yield, 7-782 percent, is calculated by using this rate to discount the bond s expected price six months forward. The price in six months in both the up and the down state is 103.00—the ex-coupon value plus the final coupon payment. The bond s price at this node, therefore, is derived using the risk-neutral pricing formula as follows ... 

Kish, R., M. Livingstone. 1992. The Determinants of the Call Feature on Corporate BorsAs. Journal of Banking and Finance 16, 687-703. 

Common and variable aspects of a product line can be specified in so called feature models [20]. In this sense, the MRD can be seen as a feature model, integrating component requirements documents (CRD) for all variants of a component or an assembly. Volkswagen distinguishes common and variable parts of a MRD based on three types of requirements, differing by their scope as illustrated in Fig. 5.8. 

In this way, all images are stored in the internal memory of the Real-Time Process Analysis System. Those belonging to a droplet colhsion are selected for the transfer the others are discarded. This mechanism is called feature-based triggering [11] which selects the images of collisions in real time with a latency below one millisecond for the image sizes used. 

We conclude this chapter with an illustration of the OAS technique. Consider a five-year semiannual corporate bond with a coupon of 8 percent. The bond incorporates a call feature that allows the issuer to call it after two years and is currently priced at 104.25. This is equivalent to a yield-to-maturity of 6.979 percent. We wish to measure the value of the call feature to the issuer, and we can do this using the OAS technique. Assume that a five-year Treasury security also exists with a coupon of 8 percent and is priced at 109.11, a yield of 5.797 percent. The higher yield reflects the market-required premium due to the corporate bond s default risk and call feature. 

Components. An AADL component is defined through a type (it declares the component interface elements called features) and zero or more implementations (they present the component internal structure). AADL defines three categories of components software components (data, thread, thread group, subprogram, subprogram group and process), hardware components (memory, bus, virtual bus, processor, virtual processor and device) and system component. We briefly describe the subset of AADL components considered in our work. 

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