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Overview of Risk Dimensions
Contents
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
END-USER INTERFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ADMINISTRATION AND CONFIGURATION GUI . . . . . . . . . . . . .
4
GRAPHICAL-USER (GUI) AND BATCH INTERFACES . . . . . . . . . .
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REPORTING AND OUTPUT . . . . . . . . . . . . . . . . . . . . . . . . .
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SAS RISK ENGINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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MARKET RISK METHODOLOGIES . . . . . . . . . . . . . . . . . . . .
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CREDIT RISK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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MODELING SUBSYSTEM . . . . . . . . . . . . . . . . . . . . . . . . . . .
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RISK ANALYSIS ENVIRONMENT . . . . . . . . . . . . . . . . . . . . . . 10
FUNCTIONAL COMPONENTS . . . . . . . . . . . . . . . . . . . . . . . . 11
DATA MANAGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
USING MARKET DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
PRICING INSTRUMENTS AND
TRANSFORMING RISK FACTORS . . . . . . . . . . . . . . . . . . 15
EXTERNAL FUNCTIONS INTERFACE . . . . . . . . . . . . . . . . . . . 15
USING PORTFOLIO DATA . . . . . . . . . . . . . . . . . . . . . . . . . . 16
INTELLIGENT PROCESSING . . . . . . . . . . . . . . . . . . . . . . . . 17
FEATURES OF SAS SOFTWARE . . . . . . . . . . . . . . . . . . . . . . . 18
RESULT: AN OPEN, FLEXIBLE, AND EXTENSIBLE SYSTEM . . . . . 20
INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
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Overview of Risk Dimensions
Overview of Risk Dimensions
Introduction
Risk Dimensions software features tremendous power and versatility through an
open, flexible, and extensible design. It can be configured to solve any market, credit,
or other enterprise-wide risk management problem, using any analysis methodology
desired by users.
Risk Dimensions is software from SAS Institute, which develops and maintains the
world’s leading integrated system of hardware-independent, decision-support, and
information delivery software. SAS software is used by 98 of the FORTUNE 100
companies, government agencies, and major universities worldwide.
End-User Interface
Risk Dimensions incorporates a design that makes it easy to create specifications
for different aspects of the risk analysis process, to manage libraries of specification
objects, and to mix-and-match specifications for running analyses.
The main window has six tabs: Analysis, Portfolios, Market Data, Risk Models,
Report Gallery, and Configuration. The tabs can be selected to view an expandable
tree interface (explorers) that provides access to editors and browsers. The window
also contains a tool bar and pull-down menus for quick access to the power and
flexibility of Risk Dimensions.
Figure 1.
Main Window
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Overview of Risk Dimensions
Administration and Configuration GUI
Risk Dimensions can be configured to analyze virtually any type of financial instrument, to read instruments from any collection of input databases, and to value instruments and transform risk factors by calling any functions you supply. This flexibility
is supported by the extensive administration and configuration features that are available in Risk Dimensions.
Figure 2.
Report Gallery
SAS Risk Engine
Graphical-User (GUI) and Batch Interfaces
Graphical-user interfaces are very powerful and flexible when you want to use a system interactively. System configuration, analysis, and reporting are also available by
using batch code. The included batch interface procedures allow the system to be
driven by SAS scripts.
Reporting and Output
To display analytical results, Risk Dimensions utilizes SAS software’s cutting-edge
capabilities for reporting; executive information systems (EIS); online analytical processing (OLAP); and interactive, multidimensional data visualization.
The Report Gallery, which is included in Risk Dimensions, contains a wide variety
of risk report templates. You can customize report templates in the Report Gallery,
and you can add new report templates of your own design.
You can interactively view analysis results by using the industry-leading, online analytical processing (OLAP) features of the SAS software. This enables you to “sliceand-dice” risk analytics by aggregating or drilling down on any dimension by which
the instruments are cross classified. In addition, you can interactively explore results
by using the 3-D data visualization tools of SAS/INSIGHT.
SAS Risk Engine
At the core of Risk Dimensions is the powerful and versatile SAS Risk Engine, which
integrates many different types of information to select the analysis and to perform
the calculations. This enables you to create a library of analysis specifications. Each
named analysis specification indicates the type of analysis to be performed and includes the values of any options that control the analysis.
All results can be produced at any level of aggregation, as requested by the user, in the
specified cross classifications: summable numbers, such as sensitivities, are simply
summed; non-summable numbers, such as VaR, are re-computed at each level. All
analysis features are based on full pricing of each instrument in the portfolio that is
being analyzed. However, even though the pricing methodology is user specified,
this does not preclude using delta or delta-gamma approximations or interpolation
approximations as the pricing method when appropriate.
The SAS Risk Engine supports a wide range of risk analysis methods, which are
described in the sections that follow.
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Overview of Risk Dimensions
Market Risk Methodologies
Risk Dimensions is capable of computing market risk measures by using different
methodologies that are described here. A general description of the analyses that can
be performed in Risk Dimensions follows.
• Sensitivity Analysis computes the vector of first derivatives (deltas) and, if
requested, the cross second derivatives (Hessian or gamma) matrix of the portfolio value with respect to a specified list of risk factor variables, evaluated at
the base case market state. The derivatives can be computed either as specified
in the pricing method for the instrument or by simple numerical differentiation,
which can be controlled by user-specified options.
• Profit/Loss Curve Analysis computes the value of the portfolio for different
market states produced by varying a specified risk factor variable over a range
of values. This involves re-pricing (using the specified pricing method) at each
point in the range.
• Profit/Loss Surface Analysis computes the value of the portfolio for different
market states produced by varying a specified pair of risk factor variables over
a two-dimensional grid of values. This involves re-pricing at each point on the
grid.
• Scenario Analysis and Stress Testing computes values for the portfolio under
user-specified values for one or more risk factors. The specified values can be
relatively small changes, which is usually called scenario or what-if analysis,
or they could be extreme changes in risk factors, which is usually called stress
testing. The specified values can be "educated guesses" about rates, prices,
volatilities, and so on.
• Delta-Normal Analysis computes an estimate of the value at risk (VaR) of the
portfolio (assuming a multivariate normal density for changes in risk factors
that have a zero mean and user-provided covariance matrix). VaR is computed
by multiplying the vector of first derivatives of the portfolio value with respect
to the risk factor variables (the “deltas”) by the specified covariance matrix,
and then multiplying by a multiplier that depends on the normal distribution
quantile point for the confidence level at which VaR is being computed.
To estimate market risks using Delta-Normal analysis, volatilities and correlations are needed. Volatilities and correlations can be estimated from historical
data. Risk Dimensions has tools that make it easy to compute covariance matrices from historical data by using different weighting schemes.
• Simulation Analyses of Risk Dimensions are a set of different types of simulation analysis. For each type, the distribution of the portfolio value over simulated market states for specified future time points is computed. This involves
re-pricing the instruments in the portfolio for each simulated market state and
aggregating the results over the user-specified levels of aggregation. The way
in which the different types of simulation analyses differ is in the method that
is used to produce the market states.
Market Risk Methodologies
For simulation, the base case of the market state is perturbed many times to
create many market state conditions. For each market state, the instruments are
valued (priced), and the value of the instruments are summed to value the portfolio. The changes in portfolio values from the base case create a distribution
from which a quantile can be specified as the value at risk (VaR).
– Historical Simulation - the simulated market states are produced by
adding to the base case the period-to-period changes in market variables
in the specified historical time series.
– Scenario Simulation - the simulated market states are produced by making specified changes to the base case values of risk factor variables. The
simulated market states can include very large movements in risk factors
for stress testing. In the terminology of Risk Dimensions, the difference
between scenario simulation and scenario analysis is that scenario simulation usually involves a large number of specified scenarios, and the
results include quantiles for portfolio values and so on; scenario analysis usually refers to specific scenarios for which portfolio values are
produced. Historical simulation is similar to scenario simulation in that
specified market states are used to value the portfolio. Historical simulation uses past market states; scenario analysis uses hypothesized market
states.
– Monte Carlo Simulation - the simulated market states are produced from
statistical models of the future evolution of risk factors. Monte Carlo
simulation analyses can have multiple time horizons.
∗ User-Specified Covariance Matrix - the simulated market states
are produced by assuming a multivariate normal distribution for the
changes in risk factor variables that have zero mean and a userspecified covariance matrix.
∗ Fitted Models - sophisticated model-based Monte Carlo simulation
in which randomly simulated market states are produced by simulating from fitted statistical models that are specified by the user in
the modeling subsystem.
A unique and powerful aspect of Risk Dimensions is the ability to
perform large-scale multivariate simulation from separate models,
each of which may be fitted by using different distributional specifications, including non-normal distributions. To accomplish this,
the simulation engine uses a framework based on the statistical concept of a copula. A copula is a function that combines marginal
distributions into a specific joint distribution. For more details about
the copula and other aspects of Monte Carlo methodology, see the
section, “Details of Monte Carlo Simulation Methodology” in “The
Analytical Methods of Risk Dimensions”.
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Overview of Risk Dimensions
Credit Risk
Credit risk is the impact on portfolio value of a counterparty who fails to perform on
an obligation. Measures of credit exposure, such as current and potential exposure,
gauge the loss to which the portfolio is exposed in the event of a counterparty default.
Exposure is determined by the fluctuating market values of instruments that involve
counterparties, as well as by the nature of netting agreements. Risk Dimensions
supports the ability to factor netting agreements into the exposure computations, including multiple netting sets for each counterparty. Measures such as Credit VaR go
beyond exposure and are geared towards factoring in the probability of counterparty
rating migrations and defaults.
Whereas market risk focuses on the time required to close a net open position, credit
risk focuses on the remaining life of the instruments in a portfolio. Credit risk is
affected by the probability of rating migrations and defaults, as well as by the probability of market movements, which affects the consequences of nonperformance by
the counterparty.
A portfolio that has a single counterparty often contains instruments that have opposing sensitivities to changes in risk factors. Some instruments gain in value as
a risk factor (such as an interest rate) increases, and other instruments lose value.
Furthermore, rating migrations and defaults are correlated across counterparties.
Given these circumstances, a realistic assessment of credit risk requires conducting
the analysis on a portfolio basis.
Risk Dimensions computes Current Exposure as Max (Sum of Mark-to-Market
Values over the Netting Set, 0) and then aggregates the netted exposures to userspecified levels such as counterparty, rating, and so on. For Potential Exposure
analysis, the risk factors are perturbed repeatedly according to user-specified simulation models. For each perturbation or scenario, the instruments are marked to market,
and the exposure is re-computed. The simulations can be for multiple time horizons.
The exposure distribution at each horizon can then be used to determine a maximum
exposure for that horizon, based on a specified quantile, and thus to find a maximum
exposure across all time horizons.
For computing measures such as Credit VaR, the Monte Carlo simulation capabilities
can be used in the same way as they are used in the market risk case. The modeling
and simulation capabilities support integrated measurement of market risk and credit
risk. The Monte Carlo simulation engine has the unique and powerful ability to
meaningfully combine many separately estimated models for market and credit risk
factors, so that the interrelationships between the risk factors is taken into account for
more accurate risk measurement.
Modeling Subsystem
With appropriate data available, the aspects of credit risk modeling that can be incorporated include:
• The impact of credit spreads on instrument valuation.
• The dynamic movement of credit spreads over a period of time in interrelationship with market risk factors, such as interest rates.
• Dynamic migration in credit rating, up to and including default, by using models that derive rating transitions from dynamic asset return models or from
transition matrices.
• Correlations between rating migrations of counterparties.
• Conditionality in credit migration transition probabilities.
• Dynamic movement in credit-worthiness indices and their dependence on other
variables.
Modeling Subsystem
The modeling subsystem consists of a batch interface and a graphical-user interface
(GUI) and is based on the SAS/ETS MODEL procedure. In the batch interface, users
submit statements based on the syntax of the MODEL procedure, while the GUI provides a point-and-click interface. The modeling subsystem is designed so that users
can specify, estimate (fit), test, and save models of risk factor evolution. The fitted
models can be used later in Monte Carlo simulations, as part of the computation of
market and credit risk measures. A variety of estimation methods are available including Maximum Likelihood Estimation (MLE) and Generalized Method of Moments
(GMM).
The model specifications consist of equations that model the conditional means by
using functional forms that are user-specified, and equations that specify the conditional distribution of the residual errors, with user-specified functional forms allowed for transforming from the elemental distributions, such as the normal or the
t-distribution. For example, nonlinearity in the conditional variance equation for the
residual errors makes possible the specification of various types of GARCH models.
The parameters in a model specification are estimated (fit) by using the user-provided
data. Fitted models contain all the relevant information, such as the mean equations
and the residual distributions, the parameter values, and the estimated residual vector;
and they can be saved for use in Monte Carlo simulation. Each model specification
(for example, a type of GARCH) can be used as a template to estimate many fitted
models. The difference among the models is the data used and the variable(s) being
modeled.
An important aspect of the fitted models is that measures of parameter variability,
such as the covariance matrix of the parameters, are saved by the system and can be
used, at the choice of the user, during the simulation to factor in parameter uncertainty.
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Overview of Risk Dimensions
Risk Analysis Environment
Risk Dimensions uses the concept of a risk analysis environment to store metadata
and other files. All of the registrations and configurations—the “meta-data” of the
risk management problem—are stored. Logically, a Risk Dimensions environment
is just the set or collection of information and files that is created to implement a
particular part of a risk management solution. Physically, an environment consists
of a SAS catalog and an associated directory, which is used to store various files
produced by the analysis process.
Any number of environments can be created, which is useful and efficient if there
are several different risk management problems that require substantially different
variations of the solution configuration.
Analysis environments can inherit information from one another. This can be very
useful, for instance, when individuals in a risk management department are working
on different projects, all of which are based on and draw from the common corporate
risk management system.
For example, in Risk Dimensions, a corporate-wide risk management solution can be
configured to analyze the current business portfolio, and an analyst who is investigating the risk impact of a proposed new financial product can create a separate analysis
environment. The analyst’s individual environment would be based on the corporate
analysis environment and inherit all the information about the current portfolio, and
he would add to it registrations and the hypothetical holding of the proposed new
instrument.
This inheritance feature for linking environments together is also convenient for including registrations of pricing function libraries, libraries of simulation models, and
other resources into the configuration for a particular problem without the user having
to copy them.
Functional Components
Functional Components
• Projects are the central concepts for the user. A project consists of portfolios,
market data sources, transformation sets, models, analyses, cross classifications, and reports. A project lists the different environment items that are
necessary to access required inputs, to compute risk measures, and to create
reports.
• Analysis is, for example, a sensitivity calculation or a VaR calculation on the
portfolio defined in a project. A project can have multiple analyses specified.
• Cross classifications specify how analysis results are to be disaggregated, so
that results are available for the different sub-portfolios of the portfolio that is
being analyzed.
• Portfolio defines the group of instruments to which the project pertains. The
portfolio is defined by a portfolio file and one or more portfolio filters.
• Portfolio Files hold instrument data, which has been made available to Risk
Dimensions from various sources and is ready to present to the Risk Engine
for analysis.
• Portfolio Filters are sets of subsetting criteria used to subset instrument data.
Subsetting is performed with respect to the attributes of the defined instrument
types.
• Portfolio Data Sources are environment items that map instrument data into
Risk Dimensions.
• Market Data Sources are environment items that make market data accessible
to Risk Dimensions.
• Instrument Types are essentially an enumeration of the attributes of a specific type of financial instrument. Examples of instrument types include: FX
forwards, government bonds, interest rate swaps, equity options, commodity
derivatives and so on. Inheritance (an object-oriented programming concept)
is supported to make the process of defining a new instrument type easier. One
important aspect of the instrument type definition is the identity of a method
program that is used to price any instance of that instrument type.
• Method Programs are used to price instruments, transform risk factors, or
to modify instrument input variables. Method programs can be written in the
SAS language, which has fourth-generation programming features. The logic
can be contained within the method programs, or they can be used to access
function libraries written in SAS or C/C++. In the case of C/C++ libraries, this
facilitates the use of proprietary or third-party functions to price instruments or
for other tasks.
• A Transformation Set is a set of risk factor transformations that can be
used to transform risk factors into the form needed for input into the instrument pricing methods. Examples are computations of zero curves and implied
volatilities.
• A Model is a specification of a functional relationship between variables, as
well as parameter estimates that are needed to complete that functional rela-
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Overview of Risk Dimensions
tionship. A model is used to produce simulated risk factor values for performing risk measurement computations. The modeling subsystem enables users
to develop non-normal models, which includes GARCH models, interest rate
models such as Cox-Ingersoll-Ross, t-distributions, and so on. Methods available in the system for estimating such models include maximum likelihood
estimation (MLE) and the general method of moments (GMM).
• Results are the calculations produced from the analyses. Results are output
in the form of SAS data sets and multidimensional databases (MDDBs). By
using the tools that are included in the solution, the results can be converted
into virtually any data format, including Excel, Oracle, SYBASE, and others.
• Reports contain the analysis results presented in user-specified formats.
Data Management
Risk Dimensions incorporates SAS software for data acquisition, management, and
processing features, as well as SAS/Warehouse Administrator. These components are
used to construct a risk data warehouse that consolidates portfolio data from virtually
any database. The source databases can be maintained by SAS or by a variety of other
database management systems, such as Oracle or SYBASE. After it is constructed,
the risk data warehouse makes the portfolio data available to the SAS Risk Engine.
Risk Dimensions also supports access to real-time market data feeds provided by
vendors such as Reuters.
Using Market Data
Using Market Data
Market data is required for several purposes in the risk management process.
Moreover, the market data might need to be transformed or modeled. The use of
market data includes the following.
Market Data Sources
Market data is stored in the Risk Data Warehouse or other risk data repository in the
form of SAS data sets or data views. These SAS data sets or data views are registered
in the analysis environment so that Risk Dimensions can access them. SAS Software
provides transparent access to many database management systems, such as DB2 and
IMS from IBM, MS EXCEL, Ingres INFORMIX, SYBASE, and Oracle.
The following different kinds of market data files can be registered.
• Current Market Data is used to compute the base-case market state for the
initial portfolio Mark-to-Market value.
• Time Series Data, at various frequencies can be used to drive historical simulation analysis, to initialize lagged processing for model-based simulations,
and to estimate the parameters of simulation models.
• Volatility Data supply estimates of the standard deviations of market risk factors.
• Covariance Matrix Data is a market data set that supplies estimates of the
variances and covariances of market risk factors, and it is used for covariancebased simulation analysis.
• Scenario Data is a type of data set that enables the user to drive market simulations by using risk factor perturbations, which are generated externally to
Risk Dimensions.
• Change Scenario Data is a type of data set, like the Scenario Data, but in the
form of changes to be applied to the base-case market state instead of simulated
risk factor values.
• Linear Transformation Matrix is a type of data set that supplies a matrix to
compute a list of risk factor variables from the values of another list of risk
factors.
• Parameters Data Set is a type of data set that supplies values for parameter
matrices that can be used in pricing or in the transformation method programs.
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Overview of Risk Dimensions
Risk Factor Transformation Method Programs
Some risk factor variables or risk factor curves are not available from the market data
but must be derived from the market data.
An example is a zero-coupon yield curve that is used to price instruments; the zerocoupon rates are not observable in the market but may be inferred from the market
prices of coupon bonds that have different maturities.
Risk factor transformation programs can be written to perform these calculations.
These programs are written in the SAS programming language and stored in the
method program library of the analysis environment. The logic can be contained
within the programs, or they can be used to access function libraries written in the
SAS language or the C language. In the case of C libraries, this facilitates the use of
proprietary or third-party functions to price instruments or for other tasks.
Parameter Matrices
These are matrix variables that can be associated with a Parameter type market data
source and can be used in pricing or transformation method programs.
Transformation Sets
A number of different risk factor transformation programs might have to be executed
to compute the values of derived risk factor variables and curves. You can create
transformation sets that mix-and-match different transformation programs for use in
different analyses.
Risk Models
The most advanced type of risk analysis that is provided by Risk Dimensions is full
model-based Monte Carlo simulation of probable market movements. For this type
of simulation, a set of models for the various risk factors is needed, together with statistical estimates of the model parameters, which are computed by fitting the models
to historical market data. For more details, see the “Modeling Subsystem” section.
External Functions Interface
Pricing Instruments and
Transforming Risk Factors
Risk Dimensions can register and execute user-supplied instrument pricing functions
to value instruments, as well as risk factor transformation subroutines to compute
derived pricing rates, such as zero curves or implied volatilities. Users can employ
their own algorithms or invoke code that is supplied by third-party vendors. The
system supports calls to the following types of functions.
• External C/C++ language functions written by the user or supplied by thirdparty vendors.
• User-written SAS language functions.
• Other functions readily available in SAS.
External Functions Interface
Risk Dimensions has the flexibility to integrate functions from external sources. This
is an important feature because users can gain competitive advantages by using their
in-house C and C++ functions or functions that are written by other vendors. Being
able to integrate functions from external sources makes it easy to add new instrument
types and their associated pricing functions to the system. External functions can be
included in two ways:
• Interactively through the GUI.
• Batch processing with SAS scripts.
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Overview of Risk Dimensions
Using Portfolio Data
Accessing and using the portfolio data involves the following.
Portfolio Data Sources
There might be multiple sources of portfolio data residing in various database management systems that correspond to different lines of business or subsidiary operating
units.
To supply this data to Risk Dimensions, a SAS Data Warehouse or other data repository is created that contains SAS data views into these databases or copies of the data
in SAS data sets. Then the portfolio data sets or data views are then registered in the
analysis environment.
Risk Dimensions has the flexibility to read portfolio data that is represented in several
different formats.
Portfolio Input Lists
Because the portfolio to be analyzed is often stored in different databases, a Portfolio
Input List needs to be provided to Risk Dimensions. A Portfolio Input List contains
the names of the portfolio data source registrations that give access to this data.
Also, because different portfolios can be analyzed for different purposes, Risk
Dimensions enables you to create and use any number of Portfolio Input Lists.
Portfolio Filters
For some purposes, you might want to analyze only part of a portfolio. Risk
Dimensions enables you to create Portfolio Filters that provide SAS WHERE clauses
for selecting instruments to create subsets of the portfolio. You can create any number
of filters and combine them in different ways.
Portfolio Files
A portfolio input list and, optionally, a list of portfolio filters, is used to create a binary
extract file, which contains the instruments of the portfolio to be analyzed in a form
that Risk Dimensions can process efficiently. Efficiency of access through replicating
the portfolio data in such a form is crucial for the multiple re-pricings needed during
simulation analyses. This avoids repeated accessing of the source data, which can
be very inefficient. You can create any number of these portfolio files in an analysis
environment.
Intelligent Processing
Intelligent Processing
Risk Dimensions uses considerable intelligence in processing data and performing
analyses. When a project is defined, it typically includes many parts (risk factor variables, method programs, instruments, analysis scenarios, and so on). Risk
Dimensions not only examines each part for internal consistency but also examines
the interrelationships of the project parts. The project is considered in its entirety. If
there is an inconsistency, then the user is notified and action can be taken.
Risk Dimensions is also able to eliminate redundant or unnecessary parts of the
project so that it will run as fast as possible. The following are important questions
that Risk Dimensions is designed to answer.
• Each part alone seems straightforward and well-defined, but do all the parts
make sense when put together?
• Are all the appropriate data elements provided that are needed to compute the
requested results?
• In what order do risk factor transformations have to be executed so that the
interrelationships are preserved?
• If an exchange rate that is needed for a computation is not directly available,
can it be triangulated from the available rates?
Risk Dimensions provides an automated intelligence processing system to answer
these complex questions. The intelligent processing system includes:
• Data Flow Analysis Subsystem
• Trace Mechanism
Together these subsystems provide the flexibility to define the small parts individually, in the way that you decide, and then brings them together in a consistent and
efficient manner.
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Overview of Risk Dimensions
Features of SAS Software
Risk Dimensions incorporates many features of SAS software to provide an end-toend risk management solution – from market and portfolio data through sophisticated
analyses and data exploration to presentation-quality reports.
Data Access and Data Transfer
SAS Software provides transparent access to many database management systems,
such as DB2 and IMS from IBM, MS EXCEL, Ingres INFORMIX, SYBASE, and
Oracle, flat files, system-specific host files, and other legacy data types. In addition
to the support of real-time access to market data feeds such as Reuters, you can also
use SAS software to access many economic and financial data sets that are provided
by vendors and simultaneously convert them to SAS data sets.
SAS Software interacts with and supports Open Data Base Connectivity (ODBC)
both as a client and as a server. Moreover, data from PCs, mini-computers, UNIX systems, or mainframes can be combined with data from online transaction-processing
systems and with public data from external sources.
SAS data tables, catalogs, graphics catalogs, entire libraries, and external files can
be transferred easily between local and remote systems, and movement can be in
either direction. When moving data or files between machines that have different
architectures, SAS performs all necessary translations, which includes converting
SAS data sets to the appropriate target platform format, as well as recompiling any
user applications that are written in the SAS language.
Data Management and Data Warehousing Solutions
SAS Software, with its data access, management, analysis, and presentation capabilities, has been a viable data warehouse tool for many years. The SAS/Warehouse
Administrator contains these SAS capabilities and new tools for defining warehouses
and subjects; transforming and summarizing data; updating summary data; creating,
managing, and viewing metadata; and implementing data marts.
A data warehouse is a collection of data organized into subjects, often with added
time values. A data warehouse includes detail data, summarized data, and metadata,
and it may include reports, graphs, and other information generated from data in the
warehouse. Data warehouses are built to match the company’s information technology (IT) architecture and overall strategy, and the end-user’s desired functionality.
Statistical Modeling
SAS Institute is dedicated to developing and maintaining state-of-the-art statistical
analysis software. Relevant parts of a vast array of SAS statistical analysis software
are incorporated into Risk Dimensions. Sophisticated statistical modeling of the joint
probability distribution of future market changes is the key to modern methods of
analyzing market risk, and Risk Dimensions provides cutting-edge solutions to these
problems. Regarding the analysis methodologies available, you can select from a
wide variety of included analysis types, as well as define your own specialized solutions.
Features of SAS Software
Reporting, EIS, OLAP, MDDB
SAS/EIS software is an object-oriented development environment for creating and
maintaining highly intuitive enterprise information systems. It offers a code-free development environment that consolidates the capabilities of SAS Software for data
access, management, analysis, and presentation.
SAS software includes an easy-to-use report generator for interactive use and batch
processing. Users can easily perform operations and define report attributes by selecting items from pull-down menus.
Online analytical processing (OLAP) is also known as slicing-and-dicing data, or
multidimensional data analysis. OLAP utilization offers high-performance access to
large amounts of summarized data for complex multidimensional analysis and easy
reporting.
SAS Software also provides the multidimensional database (MDDB), which is a specialized storage facility where data can be accessed from a data warehouse or other
data storage source. The MDDB stores data in a matrix-like format for fast-and-easy
access by tools, such as multidimensional data viewers.
Exploratory Graphics (SAS/INSIGHT)
To examine and explore portfolio data and risk analysis results, Risk Dimensions
links to SAS/INSIGHT software, which is a dynamic tool for data exploration and
analysis. With SAS/INSIGHT, you can
• Explore data by using interactive histograms, box plots, scatter plots, and rotating 3-D plots.
• Examine distributions and explore correlations between variables.
• Calculate principal components and plot them in two or three dimensions to
reveal the essential structure of the data.
• Construct predictive models based on relationships found in the data.
• Perform multiple regression, analysis of variance, and generalized linear modeling (which includes logistic regression and Poisson regression).
• Test fitted models with a complete set of diagnostic outputs.
All graphs and analyses are linked, so that changes to data in one graph or analysis
immediately shows in all others. Linking the data permits exploratory techniques,
such as point identification and scatterplot brushing.
19
20
Overview of Risk Dimensions
Portability, Client/Server Architecture
SAS Software’s MultiVendor Architecture (MVA) is hardware-independent architecture that enables 90 percent of SAS code to be portable across all platforms, and
makes SAS the only client/server software that is available on all major platforms.
Hardware independence enables organizations to take advantage of all client/server
models, fitting their client/server solution to their networking and data requirements,
rather than the other way around. MVA makes SAS Software applications sourcecode compatible and portable to any platform, and they run under various operating
systems without modification.
Result: An Open, Flexible, and Extensible
System
The variety of instruments that are traded in financial markets, the number of algorithms available to value them, and the number of methodolgies to analyze their
risk are expanding rapidly. Extensibility, access to custom and third-party instrument
pricing algorithms, and a robust framework for non-normal multivariate simulation
are key requirements of a modern risk management system. The Risk Dimensions
solution offers great advantages with its open, flexible, and extensible architecture.
Index
A
analysis, 11
C
components, 11
credit risk, 8
cross classifications, 11
D
data
access, 18
management, 12, 18
market sources, 11
position, 16
sources, 11
E
environment, 10
external functions, 15
F
functions
external, 15
G
graphical user interface (GUI), 3
I
instrument
pricing, 15
types, 11
M
market data, 11
market risk
methodology, 6
method programs, 11
modeling, 9, 18
P
parameter
matrices, 14
portfolio, 11
data sources, 16
files, 11, 16
filters, 11, 16
input lists, 16
position data, 16
pricing
instruments, 15
programs
method, 11
projects, 11
R
reporting, 19
risk
credit, 8
risk analysis engine, 5
risk models, 14
S
SAS Risk Engine, 5
T
transformation sets, 11, 14