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1 Probability distribution for default 2 Severity of loss in the event of default 3 A correlation model Probability Distribution for Default In structural models, like Portfolio Manager and CreditManager, default risk derives from a model of how default occurs (ie, from the structure of the rm) Both Portfolio Manager and CreditManager focus on the change in value of a rm s assets as the primary determinant of default risk5 By specifying the distribution of the default rate directly, reduced form models can be applied to any structure and obligor The challenge is to choose the distribution and parameters correctly Credit Risk+ leaves the choice of parameters (eg expected default rates and volatility) to the user Instead of a single distribution for the default rate, Credit Risk+ employs two a Poisson distribution for the arrival rate of defaults and a gamma distribution for mean default rates.

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Furthermore, McKinsey s CreditPortfolioView estimates the parameters of the default distribution via a logistic model (normally distributed factor changes) Severity of Loss in Event of Default At times we talk about recovery and at other times we talk about severity The two are mirror images of each other Recovery is equal to (1 Severity) Recovery Distribution The beta distribution is the generally accepted model for uncertain recovery in the event of default As re ected in Exhibit 429, Portfolio Manager, CreditManager, and CreditPortfolioView use it Mean Recovery and Volatility In order to model recovery in the event of default, it is necessary to specify the mean recovery and the variability of the mean recovery rate In both RiskMetrics and KMV the user can specify loss given default based on characteristics of the exposure Correlation Model In 3, we described two approaches One treats correlation as an explicit input.

The theoretical models underlying both Portfolio Manager and CreditManager presuppose an explicit correlation input Both models implement this via a separate factor model that examines asset or equity correlations The other approach is to treat correlation as an implicit factor In both CreditPortfolioView and Credit Risk+, the source of the correlation is the model itself..

items, frequency moments, L1 and L2 norms of vectors, inner products, frequent items, heavy hitters, quantiles, histograms, and wavelets. Recently, progress has been achieved for other problem classes, including computational geometry (e.g., clustering and minimum spanning trees) and graphs (e.g., triangle counting and spanners). At the same time, there has been a urry of activity in proving impossibility results, devising interesting lower bound techniques, and establishing important complementary results. This chapter is intended as an overview of this rapidly evolving area. The chapter is not meant to be comprehensive, but rather aims at providing an outline of the main techniques used for designing algorithms or for proving lower bounds. We refer the interested reader to the works by Babcock et al. [7], Gibbons and Matias [34] and Muthukrishnan [57] for an extensive discussion of problems and results not mentioned here. 8.1.1 Applications

The dd command above creates a 32MB file named swapfile. The chmod command locks down the permissions on the file, to avoid getting a warning from the swapon command down the road. The mkswap command formats the /tmp/swapfile file to be a swap partition.

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As the GSM MoU Group became more and more international, the ETSI policy of restricting access to documentation to ETSI members and also the policy for non-European GSM MoU Group member participation in the standardisation work became quite an issue. In close co-operation with Friedhelm Hillebrand a co-operation agreement between ETSI and the GSM MoU Group was signed in 1996 as described in 5, Section 2, paragraph 5.2.5.1.

Small-company respondents were also most likely to have insourced functions that were previously outsourced (11.1% of respondents). Midsize organizations showed streamlined processes, renegotiated vendor contracts, and reduced staff travel and conferences as tied for third place in their successful cost-control efforts (42.9% each). About one-third of respondents rely on Internet hiring (33.3%), human resource information systems (HRIS) improvements (35.7%), and Web-based applications (28.6%). Because relying on HR staff to take on more responsibility was far and away the most successful cost-control strategy (57.1% of respondents), it is possible that there was little budget for technology improvements among organizations in this size group. Respondents in this size group were most likely to have downsized HR staff (26.2%) and cut back on travel and conferences (42.9%). Large organizations were almost twice as likely as other size groups to have outsourced HR functions to save HR costs (21.1%), but least likely to have down-