1. No category

New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
page 192
E. How to access and use RiskMetrics Update
1. Data access
2. Implementing a risk
management framework
This section reviews where users can access the daily RiskMetrics datasets. It also lists and
describes the products that a number of firms have developed to incorporate the RiskMetrics
methodology and datasets.
3. Third party developers
of risk management
software
1. Data access
4. Examples disk user guide
RiskMetrics files are available through the services listed below. The estimates of volatilities
and correlations for the daily and monthly horizons are updated daily using the prior day’s close
of business data. The data is usually available by 10:00 a.m. U.S. Eastern Standard Time. The
datasets are compressed for DOS, Apple Macintosh, and Unix platforms. The DOS and Apple
files are “auto-unstuffing,” i.e., the decompression software is enclosed in the file.
Datasets are not updated on official U.S. holidays. Foreign market data for these holidays are included in the following day’s datasets. U.S. market data for U.S. holidays is adjusted according
to the EM method described in Section D. EM is also used for missing data in other markets.
RiskMetrics datasets are currently available from:
Internet/World Wide Web
RiskMetrics datasets are updated daily whereas RiskMetrics publications are updated as required. The data is accessible in two distinct ways.
For interactive browsing users can access the World Wide Web directly via Mosaic, Netscape,
or other equivalent browsers. It is also available through such services as CompuServe,
Prodigy, Delphi, and America Online.
To obtain the RiskMetrics data via the Web, users can access one of the following URL’s:
Home page
http://www.jpmorgan.com
RiskMetrics http://www.jpmorgan.com/RiskMetrics/RiskMetrics.html
Downloads
http://www.jpmorgan.com/RiskMetrics/DOWNLOADING/download-data.html
The RiskMetrics documentation can be downloaded from the RiskMetrics Report file page
(http://www.jpmorgan.com/RiskMetrics/DOWNLOADING/download-docs.html).
For automatic retrieval, or for those without access to the Web, the data is also available via
anonymous ftp.
• Use local ftp client software to connect to ftp.jpmorgan.com/pub/RiskMetrics/ and filename.
• Enter anonymous as the user name and your internet ID as the password.
• Users should download the file instructions.txt to obtain information about directory layout
and where to find specific data.
• Follow the instructions for the data you wish to access.
page 193
RiskMetrics – Technical Document
Third edition
CompuServe
J.P. Morgan has a forum on the CompuServe Information Service. Clients can dial in from
around the globe, generally via a local phone call, and download RiskMetrics datasets and
publications. Access to the datasets is available to the public at the first level of this forum. Other
parts are available exclusively to J.P. Morgan clients. The only costs are the normal
CompuServe connect-time charges for access. Hardware requirements are Macintosh, or DOS/
Windows-based PCs and a high-speed modem (9,600 bps or faster is suggested) for connecting
to CompuServe. Software requirements include the CompuServe Information Manager
(CIM) software (for Mac or PC) and a CompuServe account ID. Once one has accessed the
CompuServe network, enter GO JPM to reach the top level of the JPM forum (see below) or
GO JPM -15 or GO JPM-16 to reach the daily or monthly datasets directly.
Screen menus used to access the RiskMetrics library.
1. General CompuServe Menu
4. Within the JP Forum
Select “JP Morgan RiskMetrics Data and
Publications”
2. Accessing the JPM Forum
Select the <Services...Go> menu
3. Accessing the JPM Forum
Type “GO JPM”
5. Within the RiskMetrics library
Click on selections
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
Data Services
Telerate will distribute via its Telerate Workstation the RiskMetrics dataset into either an
Excel spreadsheets or Telerate’s Fixed Income for Windows. The Workstation enables Windows-based PC clients to import into resident applications, offered either by Telerate or other
vendors, both market information and the RiskMetrics datasets.
page 194
Reuters allows users of their IDN service to receive in logical record format any and all of the
RiskMetrics daily and monthly volatility estimates.
Subsets of the RiskMetrics volatility and correlation data are also published on the following
pages of major news wire services:
Bloomberg
Subsets of the RiskMetrics dataset can be accessed by typing RMMX Go.
Reuters
Subsets of the RiskMetrics dataset can be accessed on pages RKMS-Z.
Telerate
Subsets of the RiskMetrics dataset can be accessed on pages 6733-39 (commodities) and
17379-17389 (Foreign exchange, Government bond zero rates, equity indices).
2. Implementing a risk management framework
Setting up a risk management framework within an organization requires more than a quantitative methodology. The following consulting firms have set up capital markets advisory practices
to help end users implement effective risk management. All of the firms listed below have
reviewed RiskMetrics and can provide support for implementation:
Andersen Consulting LLP
1345 Avenue of the Americas, New York, NY 10105
Americas – Steven Mollenkamp (1-212) 708-3829, FAX (1-212) 708-7962,
steve.mollenkamp@ac.com
Europe - Andrew Marshall (44-171) 438-5721, FAX (44-171) 304-8314
Asia - Tomomichi Tomiie (81-3) 3470-9241
• Risk Integration Services. Andersen Consulting provides its clients with a cohesive set of
services to support the continuous linkage and interaction of strategy, people, processes and
technology. A key focus is the integration of capabilities across a firm to create an enterprise
wide view of risk using common measures such as RiskMetrics.
• Vision Architecture. A framework for constructing applications specifically for the
financial industry. The architecture is structured along functional lines and addresses key
challenges faced by the industry.
• Risk Management Practice Aid. Eleven binders of practical risk management system design
and implementation aids. This practice aid describes risk management concepts, covering
both business issues and design system considerations.
• Risk Management Designware. Provides a proven set of design materials covering both
market and credit risk management. Included are actual design specifications and sample
documents.
page 195
RiskMetrics – Technical Document
Third edition
Price Waterhouse
1177 Avenue of the Americas, New York, NY 10036
Mike Salzberg (1-212) 596-8710 FAX (1-212) 596-8945, mike_salzberg@notes.pw.com
• Risk Management Primer. Multimedia PC-based application that describes the concept of
market risk and its implications for senior management.
• Implementation Guide. Practice guidance document describing key considerations in the
implementation of a comprehensive risk management framework and infrastructure within a
corporate or financial institution (approximately 30 pages).
• Risk ToolSet. Modular Windows-based PC system designed to analyze portfolios of financial
transactions over a wide array of cash and derivative products across a number of markets. It
uses the J.P. Morgan definitions for position maps and it links to the publicly accessible
RiskMetrics databases for updates on volatility and correlation estimates.
Coopers & Lybrand
Plumtree Court, London EC4A 4HT, England
Paul Reyniers (44-171) 583-5000 FAX (44-171) 822-4652
1301 Avenue of the Americas, New York, NY 10019
Rocco Maggiotto (1-212) 259-1000 FAX (1-212) 259-1347
• Strategic Risk Management Framework. Comprehensive methodology to develop a
strategic risk management framework to support executive management. This structured
approach covers all areas of the market, credit and operational risk as well as the development
of performance measures and capital allocation policies, providing the context in which
RiskMetrics may be used effectively as a strategic risk management tool.
• Derivatives Diagnostic. Four-step diagnostic approach that assesses risk and operational
controls for derivatives operations, either for dealers or end-users. This provides an evaluation
of management and controls against a series of detailed checklists and benchmarks, and
provides the basis for identifying priority actions and a longer term program for improving
risk management and operational controls. This diagnostic incorporates the G30 and other
recommendations and the high level principles underlying the RiskMetrics approach to risk
quantification.
• ECAM+. An integrated set of client-server software products to support the 1996 Capital
Adequacy Directive (CAD) regulatory reporting requirements. These products currently cover
Position Risk Requirements, Credit Risk Requirements, and Large Exposures for firms
regulated by the SFA or the Bank of England. Enhancements are under development to meet
the requirements of the other European regulators.
Ernst & Young LLP
750 7th Avenue, New York, NY 10019
Alvi Abuaf (1-212) 773-1255, Edwin Pisano (1-212) 773-2712,
David Cannon (44-171) 931-1180
• Systems Implementation and Integration. Define risk management business requirements,
select and implement industry leading software packages which incorporate RiskMetrics
and meet these requirements. Integrate these packages with front-office, back-office, and
accounting systems. Develop and implement custom software as required.
• Industry Comparisons. Review risk management and middle office procedures for best
practices comparisons, to evaluate regulatory and rating agency expectations, and to make
recommendations for improving controls. Assist in structuring risk management roles and
responsibilities. Apply best practices for measuring and monitoring all dimensions of risk
including market, credit, liquidity, and operational risks.
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
page 196
• Management Reporting Practices. Assess the reasonableness of the information management receives. Provide feedback with regard to the adequacy of the information based on
standard benchmarks and best practices.
• Benching of Valuation Methodologies. Assess the reasonableness of theoretical valuation
methodologies through the use of independent pricing models.
KPMG
2 Dorset Rise, Blackfriars, London EC4Y 8AE, England
Robert Armstrong (44-171) 311-5137, FAX (44-171) 311-5833
• RiskMetrics Review Document. Guidance for KPMG clients on the strengths and
weaknesses of the RiskMetrics methodology and comments on its applicability as a risk
management tool. This objective document has been reviewed by J.P. Morgan for technical
accuracy and is the basis of the RiskMetrics advice and consultancy support provided by
KPMG.
3. Third party developers of risk management software
“A methodology and the underlying data to apply it will not be sufficient to enable users of
RiskMetrics to implement internal market risk measurement and management systems.
A number of systems developers have committed to develop risk management estimation and
reporting tools based on the RiskMetrics methodology and data. Users will be able to choose
from a number of applications which will achieve different goals, offer various levels of
performance and run on a number of computer platforms.
The list below is a non-comprehensive review of a number of firms who publicly state their
applications integrate RiskMetrics, either the methodology, access to the data, or both. J.P.
Morgan does not endorse the products of these third party developers nor does it warrant
their accuracy in the application of the RiskMetrics methodology and the use of the
underlying data accompanying it. Clients should review the capabilities of these systems
thoroughly before committing to their implementation.”
Algorithmics Incorporated.
822 Richmond Street W., Suite 300, Toronto, Ontario, Canada M6J 1C9
Bob Boettcher (1-416) 703-0898, FAX (1-416) 703-0767, boettcher@algorithmics.com
• RiskWatch is built around a patented, scenario-based optimization methodology for
measuring and reengineering portfolio risk. It allows users to manage risk exposure across a
variety of trading operations and isolate the risk reward profile for each underlying market
position. RiskWatch also provides “what if” scenario analysis. In addition, it has been
designed to cater to Value-at-Risk calculations and incorporate the RiskMetrics dataset.
BARRA International Ltd.
1 Whittington Avenue, London, EC3V 1LE, England
Andrew Cauldwell (44-171) 283-2255, FAX (44-171) 220-7555
• World Markets Model. Windows-based system enabling clients to enter exposure to
RiskMetrics factors. The system displays the risk estimates based on the latest update of
RiskMetrics as well as associated analytics (provided to clients via diskette – daily
RiskMetrics data updated using the electronic framework BARRALINK).
page 197
RiskMetrics – Technical Document
Third edition
Brady Plc
Cambridge Science Park, Milton Road, Cambridge CB4 4WE, England
Jayne Knightley (44-1223) 423-250, FAX (44-1223) 420-302, brady 100031,2760
• Trinity. Brady’s Trinity RiskMetrics is a component of the Windows based trading and risk
management system. Trinity accepts deal input both manually using the provided deal capture
model and electronically through the published FTIS Level 2 API. Trinity can be used to
provide risk monitoring functionality both within a derivatives trading operation and globally.
Trinity RiskMetrics/1 provides calculations of diversified and undiversified DeaR using
equivalent cash flows from trade information as well as uploads of the RiskMetrics dataset.
The RiskMetrics module will be available in July 1995.
C•ATS Software Inc.
1870 Embarcadero Road, Palo Alto, CA 94303
Rod A. Beckström (1-415) 321-3000, FAX (1-415) 321-3050, rod@cats.com
• FICAD financial objects. Functions and algorithms for DEaR have been implemented in
FICAD, an object oriented toolkit designed for financial systems integration. Includes objects
for mapping transaction data into standardized positions, reading the RiskMetrics dataset,
and performing the algebra required to calculate DEaR.
Centre Financial Products
Limited
One Chase Manhattan Plaza, 42nd floor, New York, NY 10005
Robert A. Lopez (1-212) 898-0150, FAX (1-212) 509-6591
• The Capital Adequacy Model analyzes an entity’s credit risk across a portfolio that
includes traditional financial instruments and derivatives, both exchange traded and OTC
contracts (interest and currency swaps, caps, quantos, equity index options). Credit exposure is
measured by analyzing the impact of various economic events, portfolio growth and
counterparty default probabilities utilizing Monte Carlo simulation techniques and
RiskMetrics correlation data.
• The Value at Risk Model analyzes the simulated movement of cross correlated market risk
components utilizing the RiskMetrics datasets. Changes in portfolio value can be reviewed at
different confidence intervals. The corresponding output can be reviewed and broken down by
counterparty, product type, currency or any other common attribute of the portfolio.
EDS Systems and
Management SpA
Via Carlo Farini 82, 20159 Milano, Italy
Marco Pissarello (39-2) 668-921, FAX (39-2) 668-03330
• GFI is an integrated system designed to offer a complete Financial Treasury System based on
an organizational model of a bank's Finance Department. The applications modules are focused
on the management of trades, on risk measurement and performance analysis for credit risk and
market risk. Market risk analysis is performed in any base currency using the RiskMetrics
dataset and methodology. The system is based on several servers that perform the management
of yield curves, analytics for all clients on the network and on-line computation of VaR trade
by trade. Instruments have been designed as objects and the mathematics related to the class of
financial instruments relies on the inheritance mechanism. GFI covers most common financial
instruments, either exchange traded or OTC. It is a flexible systems able to collect trades from
specialized front office systems. It is Unix based and available on Sun or IBM workstations.
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
Derivative Strategy
170 West 73rd street, New York, NY 10023
Mark Edwards (1-212) 874-0071, 71033.426@compuserve.com
page 198
• Getriskdata. A Microsoft Excel function for calculating value at risk numbers from J.P.
Morgan’s RiskMetrics database. The disk is “Freeware” and may be copied freely. The
function was developed independently of both Microsoft Corporation and J.P. Morgan. An
article outlining how to use Getriskdata was published in the December 12, 1994 edition of
Derivatives Strategy.
Dow Jones/Telerate
Harborside Financial Ctr, 600 Plaza Two, Jersey City, NJ 07311
Bernard F. Battista (1-201) 938-5650, FAX (1-201) 938-5455
• Excel spreadsheets on PC. DEaR templates using real time feed update to be used as training
material. Can be modified by users to fit individual requirements.
• Fixed Income for Windows. Risk management simulation package.
FEA
2484 Shattuck Ave, Suite 225, Berkeley, CA 94704-2029
Chris Fehily (1-510) 548-6200, FAX (1-510) 548-0332, outlook@fea.com
• Outlook. Outlook is a series of templates, macros, and libraries which work together to
produce RiskMetrics VaR estimates. Outlook displays your undiversified and diversified
exposures for a specified horizon and confidence interval. Outlook is supported for
Microsoft Excel for Windows and Applix spreadsheet for Sun. Outlook supports a wide
range of cash and derivative products. Outlook decomposes each instrument of a portfolio into
its component cash flows, then maps the cash flows into the standard RiskMetrics vertices.
Financialware Pty Ltd.
Level 7, 155 George Street, Sydney NSW 2000, Australia
Christopher Millhouse (612) 247-8055
• Plato. Is a front and middle office tool designed to measure and manage market risk for both
discretionary portfolios and trading positions. FX, money market and bond market physical
and derivative products are analyzed using scenario based stress testing, return analysis and
optimization. Windows based, PLATO is extremely open, allowing connection to live data
feeds, back office products and RiskMetrics data sets. Comprehensive risk measurement is
provided using RiskMetrics DEaR and VaR methodology.
Infinity Financial
Technology Inc.
2001 Landings Drive, Mountain View, CA 94043
Jan Ellison (1-415) 940-6100, FAX (1-415) 964-9844, jellison@infinity.com
• Montage. Montage provides VaR/DEaR functionality using the RiskMetrics methodology
and dataset. Support is provided at the application and class library levels, and uses transaction
data stored in the Montage Data Model, a risk management data warehouse. A wide range of
interest rate and foreign exchange instruments are supported, and the product is fully extensible. The Fin++ Class Library provides classes for importing J.P. Morgan data files, creating
cash flow maps and performing VaR calculations.
page 199
RiskMetrics – Technical Document
Third edition
International Risk Control
3475G Edison Way, Menlo Park, CA 94025
Joshua Sommer (1-415) 323-9494, FAX (1-415) 568-9497, jsommer@intlrisk.com
• MerX. Since May 1, 1995 International Risk Control, Inc. (IRC) is shipping Version 1.41
of MerX for Windows, its popular foreign exchange pricing, modeling, and analysis
software. Version 1.41 will incorporate, for foreign exchange positions, the RiskMetrics
methodology of calculating Value-at-Risk. Users of MerX will be able to select between
using RiskMetrics volatilities and correlations or statistics calculated using the RiskMetrics
methodology from their own data series.
Leading Market
Technologies
53 Wall Street, 5th Floor, New York, NY 10005
Ericka Stewart (1-212) 858-7770, FAX (1-212) 858-7632, estewart@lmtech.com
• Expo/RiskMetrics. Expo RiskMetrics allows users to generate RiskMetrics compatible
volatility and correlation calculations of securities using J.P. Morgan’s methodology, as well
as to perform the Daily Earnings at Risk (DEaR) and Value at Risk (VaR) calculations using
the RiskMetrics dataset. Users can choose to generate volatilities and correlations using
their own underlying data to estimate risks in a set of tradable securities or use the
RiskMetrics data which is supplied free of charge by J.P. Morgan.
Oy Trema Ab
Salomonkatu 17A, FIN-00100 Helsinki, Finland
Risto Lehtinen (358) 06859-020 risto@trema.se
• Finance Kit. Finance Kit is an integrated trading, risk management and back office system for
money and FX markets, with extensive reporting capabilities and links to various external
systems. One of the core principles of Finance Kit is the real-time monitoring of market
values and risks associated with a position or a portfolio. Any changes in market variables or
transactions immediately show up in Treasury Monitor. The position monitored is any userdefined combination of cash flows belonging to the transactions entered into the systems
through Deal Capture. RiskMetrics data will be used to calculate DEaR as another risk
measure.
Quantec Ltd.
City Gate House, 39-45 Finsbury Square, London EC2A 1PX
Jason MacQueen (44-171) 972-0303, FAX (44-171) 256-9284
• GPAS. Global Portfolio Analysis Service provides pension fund managers with a global asset
allocation tool incorporating the RiskMaster quadratic optimization algorithm. Custom
versions of GPAS exist for Treasuries of banks and multinationals (focus on short term risks)
and for Central Banks (optimizing currency and interest rate exposure).
Renaissance Software Inc.
17 State Street, 20th floor, New York, NY 10004
Michael Seppi (1-212) 344-6900, FAX (1-212) 344-7039, mseppi@rs.com
• OPUS. The Opus Value-at-Risk application has been developed for multi-currency and multiproduct market risk assessment. Opus VaR incorporates RiskMetrics datasets, and provides
an analytically tractable approach for integrating financial instruments with non-linear risk
characteristics and various unwinding periods for the calculation of DEaR and total VaR. An
integration framework enables the capture of instrument positions from other sources, thereby
supporting organization-wide risk assessment.
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
Sailfish Systems
150 Broadway, Suite 710, New York, NY 10038
Peter N.C. Davies (1-212) 587-0007, FAX (1-212) 587-0027, pdavies@salfish.com
page 200
• Trading Manager. Designed to identify and quantify market risks of discretionary portfolios
down to the individual position level. Positions can be collected from multiple deal capture
systems and integrated into a relational database to calculate and store Risk capital utilization.
Market risk simulation options includes use of RiskMetrics data.
True Risk
165 Queen Street West, Suite 1505, Toronto, Ontario, Canada M5H 2M5
Paul Boore (1-416) 869-1119, FAX (1-416) 865-1116, paul@truerisk.com
• TrueVaR. TrueVaR calculated DEaR/VaR using the RiskMetrics dataset. It is available as
an application and a C++ class library. It covers a wide range of interest rate and foreign
exchange instruments, and is easily extendible. The class library provides classes for importing
the RiskMetrics data files, creating cash flow maps, performing VaR calculations and
showing the results. The product integrates fully with the Montage range of products from
Infinity Financial Technology Inc.
Wall Street Systems
30 Broad Street, 25th floor, New York, NY 10004
Joseph A. Patrina (1-212) 809-7200, FAX (1-212) 809-7578
Wall Street Systems (WSS) is a true global engine for International Treasury, covering the front,
middle, and back offices in one seamless system. Users in different cities all trade of one worldwide WSS database in real-time. The WSS Money at Risk (MAR) module is built within the
global engine itself, so that risk figures can be calculated on demand for any portfolio, profit
center, city or for all cities.
• As dealers transact business through the WSS pricing/deal-capture front end, the MAR server
decomposes the underlying cash flows, so that sensitivity levels are already precalculated. The
RiskMetrics statistics are then applied without processing delays.
• For FX and Interest options, simulation tools are available to measures sensitivity distributions.
• Because WSS also manages P&L for each portfolio, risk/return ratios are generated as part of
management reports.
• Stress testing scenarios are run using client-supplied volatility tables.
• In addition to dealer support and market risk management, WSS also handles counterparty
credit, accounting, and full back office. This complete offering makes a unified operation
possible, without developments of complex interfaces, without clerical dependencies, and
without the reconciliation problems which are all encountered when using risk software
products which sit outside of the operational engine.
page 201
RiskMetrics – Technical Document
Third edition
4. RiskMetrics Technical Document examples disk user guide
In order to address numerous questions regarding the examples included in previous editions of
the RiskMetrics Technical Document, we now include an examples disk. The enclosed disk
contains a Microsoft Excel workbook file containing 6 spreadsheets and 1 macro file. The
workbook can be used under Excel Version 4.0 or higher.
Certain spreadsheets allow the user to modify inputs in order to investigate different scenarios. Others display less functionality. In the latter case, the objective is to provide the user
with a detailed illustration of the essential calculations. This workbook and user guide is
presented to the experienced user of Microsoft Excel, although we hope the material is
meaningful to less experienced users. Please make a duplicate of the Examples.XLW
workbook and save at least one copy on your hard drive and at least one copy on a floppy
disk. This will allow you to manipulate the enclosed spreadsheet without sacrificing the
original format.
Opening the “examples.xlw” workbook will show the following file structure:
The files listed above relate to the following:
File
TD Section
Description
CFMapTD.xls
C.2.3
Decomposition of the 10-year benchmark OAT
into RiskMetrics vertices
CFMap.xls
C.2.3
Generic Excel cash flow mapping spreadsheet
(users are given flexibility to map standard bullet
bonds)
FRA.xls
C.2.4
Mapping and VaR calculation of a 6x12 French
franc FRA
FX_Fwd.xls
C.1.2
Mapping and VaR calculation of a DEM/USD 1year forward
Str_note.xls
C.2.4
Mapping and VaR calculation of a 1-year Note
indexed to 2-year DEM swap rates
FXBase.xls
D.3
Generic calculator to convert U.S. dollar based
volatilities and correlations to another base
currency
Examples.XLM
Macro sheet that links to buttons on the various
spreadsheets
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
page 202
CFMapTD.xls & CFMap.xls
These two spreadsheets are similar, although CFMap.xls allows the user to change more of
the inputs in order to investigate different scenarios, or to perform sensitivity analysis.
CFMap.xls allows provides more vertices to which to map the cash flows. Note that only
data in red is changeable on all spreadsheets.
In CFMapTD.xls, Example Part 1 illustrates the mapping of a single cash flow, while
Example Part II illustrates the mapping of the entire bond.
To begin mapping on either spreadsheet, enter your chosen data in all cells that display red
font. Then click the ‘Create cash flows’ button. Wait for the macro to execute, and then click
the ‘Map the cash flows to vertices’ button. This macro routine executes for the final output
of Diversified Value at Risk, Market Value, and Percentage of market value. If you wish to
print the cash flow mapping output, simply click the ‘Print Mapping’ macro button.
FRA.xls
This Forward Rate Agreement example is for illustrative purposes only. We encourage the
user, however, to manipulate the spreadsheet is such a way as to increase it’s functionality.
Changing any spreadsheet, of course, should be done after creating a duplicate workbook.
In this spreadsheet, cells are named so that formulae show the inputs to their respective
calculations. This naming convention, we hope, increases user friendliness. For example,
looking at cell C21 shows the calculation for the FRA rate utilizing the data in 1. Basic
Contract Data and data under the Maturity column under 2. FRA Mapping and VaR on 6Jan-95.
Cells are named according to the heading under which they fall, or the cell to their left that
best describes the data. For example, cell B30 is named Maturity_1, while cell K32 is named
Divers_VaR_1. Also note that the RiskMetrics Correlations are named in two-dimensional
arrays: cells L30:M31 are named Corr_Matrx_1, while cells L40:N42 are named
Corr_Matrx_2.
If you have any confusion about the naming convention, simply go to the Formula Define
name... command. The Define Name dialogue box will appear, where the cell names are
listed in alphabetical order along with their respective cell references.
The cells containing the individual VaR calculations (K30, K31, K40, K41, K42) contain the
absolute value of the value at risk. In order to calculate the Diversified VaR, however, in
cells K32 and K43, we have placed the actual VaR values to the right of the correlation
matrices. If you go to cell K32, you will see that the formula makes use of VaR_Array_1,
which refers to cells O31:O32. This VaR array contains the actual values of VaR_1 and
VaR_2, which are essential to calculating the Divers_VaR_1. Cells O31 and O32 are
formatted with white font in order to maintain the clarity of the spreadsheet. Similarly, the
calculation in cell K43 utilizes VaR_Array_2, found in cells O40:O42.
FX_Fwd.xls
This spreadsheet offers some interaction whereby the user can enter data in all red cells.
Before examining this spreadsheet, please review the names of the cells in the 1. Basic
contract data section in order to better understand the essential calculations. If you have any
page 203
RiskMetrics – Technical Document
Third edition
confusion about the naming convention, simply go to the Formula Define name... command.
The Define Name dialogue box will appear, where the cell names are listed in alphabetical
order along with their respective cell references.
Please note that the Diversified Value at Risk calculation utilizes the var_array input, which
refers to cells I33:I35. These cells are formatted in white font in order to maintain the clarity of
the worksheet.
Str_note.xls
This spreadsheet is for illustrative purposes only. Again, we encourage the user to format the
spreadsheet for custom use.
Please notice that the Diversified VaR calculations make use of VaR_Array1 and VaR_Array2.
VaR_Array1 references cells N26:N28, while VaR_Array2 references cells O37:O40. These
two arrays are formatted in white font in order to maintain the clarity of the worksheet.
If you get confused about the naming convention, simply go to the Formula Define
name...command The Define Name dialogue box will appear, where the cell names are listed in
alphabetical order along with their respective cell references.
New York
May 26, 1995
Morgan Guaranty Trust Company
Global Research
Till M. Guldimann (1-212) 648-6480
guldimann_t@jpmorgan.com
page 204
Glossary
This glossary defines important terms in RiskMetrics
Absolute market risk: Risk associated with the change in value of a position or a portfolio resulting from changes in market conditions i.e., yield levels or prices.
Adverse move X: Defined in RiskMetrics as 1.65 times the standard error of returns. It is a
measure of the most the return will move over a specified time period.
ARCH: Autoregressive Conditional Heteroskedascticity. A time series process which models
volatility as dependent on past returns. GARCH - Generalized ARCH, models volatility as a
function of past returns and past values of volatility. EGARCH – Exponential GARCH,
IGARCH – Integrated GARCH. SWARCH – Switching Regime ARCH.
Autocorrelation (Serial Correlation): When observations are correlated over time. In other
words, the covariance between data recorded on the same series sequentially in time is non-zero.
Beta: A volatility measure relating the rate of return on a security with that of its market over
time. It is defined as the covariance between a security’s return and the return on the market
portfolio divided by the variance of the return of the market portfolio.
Bootstrapping: A method to generate random samples from the observed data’s underlying,
possibly unknown, distribution by randomly resampling the observed data. The generated
samples can be used to compute summary statistics such as the median. In this document,
bootstrapping is used to show monthly returns can be generated from data which are sampled
daily.
CAPM: Capital Asset Pricing Model. A model which relates the expected return on an asset to
the expected return on the market portfolio.
Cholesky factorization/decomposition: A method to simulation of multivariate normal returns
based on the assumption that the covariance matrix is symmetric and positive-definite. Refer to
section B.4.1.2.
Constant maturity: The process of inducing fixed maturities on a time series of bonds. This is
done to account for bonds “rolling down” the yield curve.
Decision horizon: The time period between entering and unwinding or revaluing a position.
Currently, RiskMetrics offers statistics for 1-day and 1-month horizons.
Decay factor: See Lamda.
Delta equivalent cash flow: In situations when the underlying cash flows are uncertain (e.g.
option), the delta equivalent cash flow is defined as the change in an instrument's fair market
value when its respective discount factor changes. These cash flows are used to find the net
present value of an instrument.
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RiskMetrics – Technical Document
Third edition
Delta neutral cash flows: These are cash flows that exactly replicate a callable bond’s sensitivity to shifts in the yield curve. A single delta neutral cash flow is the change in the price of the
callable bond divided by the change in the value of the discount factor.
Duration (Macaulay): The weighted average term of a security’s cash flow.
EM algorithm: A statistical algorithm that can estimate parameters of a function in the presence
of incomplete data (e.g. missing data). EM stands for Expectation Maximization. Simply put, the
missing values are replaced by their expected values given the observed data.
Exponential moving average: Applying weights to a set of data points with the weights declining exponentially over time. In a time series context, this results in weighing recent data more
than the distant past.
GAAP: Generally Accepted Accounting Principles.
Historical simulation: A non-parametric method of using past data to make inferences about
the future. One application of this technique is to take today’s portfolio and revalue it using past
historical price and rates data.
Kurtosis: Characterizes relative peakedness or flatness of a given distribution compared to a
normal distribution.1
4

N  X −x 
N2 − 2N + 3

i
Kx = 
∑
 
 ( N − 1)( N − 2)( N − 3) i=1  σ x  
( N − 1)(2 N − 3)
−3
N( N − 2)( N − 3)
Since the unconditional normal distribution has a kurtosis of 3, excess kurtosis is defined as
Kx-3.
λ Lamda (decay factor): The weight applied in the exponential moving average. It takes a
value between 0 and 1. In the RiskMetrics Lamda is 0.94 in the calculation of volatilities and
correlations for a 1-day horizons and 0.97 for 1-month horizon.
Leptokurtosis (fat tails): The situation where there are more occurrences far away from the
mean than predicted by a standard normal distribution.
Linear risk (nonlinear): For a given portfolio, when the underlying prices/rates change, the
incremental change in the payoff of the portfolio remains constant for all values of the underlying prices/rates. When this does not occur, the risk is said to be nonlinear.
Log vs. change returns: For any price or rate Pt, log return is defined as ln(Pt/Pt-1) whereas the
change return is defined by (Pt-Pt-1)/Pt-1. For small values of Pt -Pt -1, these two types of returns
give very similar results. Also, both expressions can be converted to percentage returns/changes
by simply multiplying them by 100.
1
We would like to thank Steven Hellinger of the New York State Banking Department for pointing this formula out
for us.
New York
May 26, 1995
page 206
Morgan Guaranty Trust Company
Global Research
Till M. Guldimann (1-212) 648-6480
guldimann_t@jpmorgan.com
Mapping: The process of translating the cash flow of actual positions into standardized position
(vertices). Duration, Principal, and cash flow.
Mean: A measure of central tendency. Sum of daily rate changes divided by count
x=
1 N
∑ Xi
N i=1
Mean reversion: When short rates will tend over time return to a long-run value.
Modified Duration: An indication of price sensitivity. It is equal to a security’s Macaulay duration divided by one plus the yield.
Outliers: Sudden, unexpectedly large rate or price returns.
Overlapping data: This occurs when subsequent returns share common data points. An example would be when monthly returns (25-day horizon) are computed on a daily basis. In this
instance adjacent returns would share 24 data points.
Non parametric: Potential market movements are described by assumed scenarios, not statistical parameters.
Parametric: When a functional form for the distribution a set of data points is assumed. For
example, when the Normal distribution is used to characterize a set of returns.
Principle of expected return: The expected total change in market value of the portfolio over
the evaluation period.
Relative market risk: Risk measured relative to an index or benchmark
Residual risk: The risk in a position that is issue specific.
Skewness: Characterizes the degree of asymmetry of the distribution around its mean. Positive
skews indicate asymmetric tail extending toward positive values (right-hand side). Negative
skewness implies asymmetry toward negative values (left-hand side).
Sx =
N  X −x
N
∑ i 
( N − 1)( N − 2) i=1  σ x 
3
Speed of adjustment: A parameter used in modelling forward rates. It is estimated from past
data on short rates. A fast speed of adjustment will result in a forward curve that approaches the
long-run rate at a relatively short maturity.
Stochastic volatility: Applied in time series models that take volatility as an unobservable random process. Volatility is often modeled as a first order autoregressive process.
page 207
RiskMetrics – Technical Document
Third edition
Standard deviation: Indication of the width of the distribution of changes around the mean.
σx =
(
1 N
∑ Xi − x
N − 1 i=1
)
2
Structured Monte Carlo: Using the RiskMetrics covariance matrix and to generate random
Normal variates to simulate future price scenarios.
Total variance: The variance of the market portfolio plus the variance of the return on an individual asset.
Zero mean: When computing sample statistics such as a variance or covariance, setting the
mean to an a prior value of zero. This is often done because it is difficult to get a good estimate
of the true mean.
New York
May 26, 1995
Morgan Guaranty Trust Company
Global Research
Till M. Guldimann (1-212) 648-6480
guldimann_t@jpmorgan.com
page 208
References
Bachelier, L. (1900), Theorie de la Speculation, Paris: Gauthier-Villars.
Bartunek, K. and C. Mustafa, (1994), Implied volatility v. GARCH: A comparison of forecasts,
Managerial Finance, forthcoming.
Becker, Kent G., Joseph E. Finnerty, Alan L. Tucker, The intraday interdependence structure
between U.S. and Japanese equity markets, The Journal of Financial Research, Vol. XV, No. 1,
1992 Bera, A., and Higgins, W. (1990). ARCH Estimation, Journal of Economic Surveys,
36,306-359.
Blattberg, R., and Gonedes, N. (1974), A comparison of Stable and Student Distributions as
Statistical Models for Stock Prices, Journal of Business, 47, 244-280.
Bollersev, T. (1986), Generalized Autoregressive Conditional Heteroscedasticity, Journal of
Econometrics, 31, 307-327.
Bollersev, T. (1987), A Conditional Heteroskedastic Model for Speculative Prices and Rates of
Return, Review of Economics and Statistics, 69, 542-547.
Boudoukh, J., Richardson, M., and Whitelaw, R.F. (1994), A Tale of Three Schools: Insights on
Autocorrelations of Short-Horizon Stock Returns. The Review of Financial Studies, 3, 539-573.
Boudoukh, J., Richardson, M., and Whitelaw, R.F., (1995) Taking the Pain Out of Volatility
Estimation, manuscript.
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood from incomplete
data via the EM algorithm, Journal of the Royal Statistical Society B 39, 1-38.
Efron, B., (1979), Bootstrap methods: Another look at the jacknife. Annals of Statistics, 7, 1-26.
Fabozzi, Frank, ed. (1991) The Handbook of Fixed Income Securities, Irwin Inc, NY, NY.
Fama, E. (1965), The Behavior of Stock Market Prices, Journal of Business, 38, 34-105.
Fama, E., and French, K. (1988), Permanent and Temporary Components of Stock Prices,
Journal of Political Economy, 96, 246-273.
Figlewski, Stephen, (1994) Forecasting Volatility using Historical Data. New York University
Working Paper, S-94-13.
French, K., Schwert, W.G, and Stambaugh, R.F. (1987), Expected Stock Returns and Volatility,
Journal of Financial Economics, 19, 3-29.
Hansen, L.P., and R. J. Hodrick, (1980), Forward Exchange Rates as Optimal Predictors of
Future Spot Rates: An Econometric Analysis, Journal of Political Economy, 829-853.
Harvey, A.C., E. Ruiz and N.G. Shepard, (1994), Multivariate Stochastic Variance Models,
Review of Economic Studies, 61, 247-264.
page 209
RiskMetrics – Technical Document
Third edition
Heuts, R.M.J, and Rens, S. (1986) Testing Normality When Observations Satisfy a Certain Low
Order ARMA- Scheme, Computational Statistics Quarterly , 1, 49-60.
Heynen, R., and Kat, H., (1993), Volatility prediction: A comparison of GARCH(1,1),
EGARCH(1,1) and Stochastic Volatility model. Mimeograph.
Inclan, C., and Tiao, G.C., (1994), Use of Cumulative Sums of Squares for Retrospective
Detection of Changes of Variance. Journal of the American Statistical Association, 913-923.
Jarque, C.M., and Bera, A.K., (1980), Efficient Tests for Normality, Homoskedasticity and
Serial Independence of Regression Residuals, Economic Letters, 6, 255-259.
Johnson, Mark E., (1987), Multivariate Statistical Simulation, John Wiley & Sons, New York.
Johnson, R.A., and Wichern, D.W., (1992), Applied Multivariate Statistical Analysis, 3rd ed,
Prentice-Hall: Englewood Cliffs.
Johnston, J., (1984), Econometric Methods. New York: McGraw-Hill.
Jorion, P., (1988), On Jump Processes in the Foreign Exchange and Stock Markets, Review of
Financial Studies, 62, 281-300.
Kempthorne, J., and Vyas, M., (1994). Risk Measurement in Global Financial Markets with
Asynchronous, Partially Missing Price Data. IFSRC Working Paper No. 281-94.
Kiefer, N., and Salmon, M., (1983), Testing Normality in Econometric Models, Economic
Letters, 11, 123-127.
Kon, S.J., (1988), Models of Stock Returns: A comparison. Journal of Finance, 39, 147-165.
Kroner, K., K.P. Kneafsey, and S. Claessens, (1995), Forecasting volatility in commodity
markets, forthcoming International Journal of Forecasting.
Lamoureux, C.B. and W.D. Lastrapes, (1993), Forecasting stock return variance: toward an
understanding of stochastic implied volatilities, Review of Financial Studies 6(2), 293-326.
LeBaron, B. (1994), Chaos and Nonlinear Forecastability in Economics and Finance. Working
Paper (February) University of Wisconson - Madison.
Longerstaey, J. and P. Zangari, (1995) Five Questions about RiskMetrics , J.P. Morgan
research publication
Mandlebrot, B. (1963), The Variations of Certain Speculative Prices, Journal of Business, 36,
394-419.
Merton, R.C. (1980), On Estimating the Expected Return on the Market: An Exploratory
Investigation, Journal of Financial Economics, 8, 323-361.
Mittnik, S., and Rachev, S.T., (1993), Modeling Asset Returns with Alternative Stable Distributions, Econometric Reviews, 12, 261-330.
New York
May 26, 1995
Morgan Guaranty Trust Company
Global Research
Till M. Guldimann (1-212) 648-6480
guldimann_t@jpmorgan.com
page 210
Nelson, D. B. (1991), Conditional Heterosckedasticity in Asset Returns: A New Approach.
Econometrica, 59, 347-370.
Pagan, A., and Schwert, G.W., (1990), Alternative Models for Conditional Stock Volatility.
Journal of Econometrics, 45, 267-290.
Parkinson, M. (1980), The Extreme Value Method for Estimating the Variance of the Rate of
Return. Journal of Business, 1, 61-65.
Richardson, M. and Smith, T. (1991), Tests of Financial Models in the Presence of Overlapping
Observations. The Review of Financial Studies, 2, 227-254.
Richardson, M., and Smith, T. (1993), A Test for Multivariate Normality in Stock Returns.
Journal of Business, 2, 295-321.
Ruiz, Esther. (1993), Stochastic Volatility verses Autoregressive Conditional Heteroskedasticity,
Working Paper 93-44, Universidad Carlos III de Madrid.
Ruiz, Esther. (1994), Quasi-maximum likelihood estimation of stochastic volatility models,
Journal of Econometrics, 63, 289-306.
Schwert, W.G., and Seguin, P.J. (1990), Heteroskedasticity in Stock Returns, Journal of
Finance, 1990, 1129-1155.
Schwert, W.G. (1989), Why does Stock Market Volatility Change over Time? Journal of
Finance , 44, 1115-1153.
Shapiro, S.S., and Wilk, M.B. (1965), An Analysis of Variance Test for Normality, Biometrika,
52, 591-611.
Shimko, D., and Masters, B., (1994), Commodities - a suitable asset class, J.P. Morgan research
publication.
Shimko, D., and Masters, B., (1994), The JPMCI - a commodity benchmark, J.P. Morgan
research publication.
Taylor, S.J. (1986), Modeling Financial Time Series, John Wiley, Chichester, U.K.
Tucker, A. L. (1992), A Reexamination of Finite- and Infinite-Variance Distributions as Models
of Daily Stock Returns. Journal of Business and Economic Statistics, 1, 73-81.
Warga, Arthur. (1992). Bond Returns, Liquidity, and Missing Data. Journal of Financial and
Quantitative Analysis, 27, 605-617.
West, K., and Cho, D., (1994), The Predictive Ability of Several Models of Exchange Rate
Volatility, NBER Technical Working Paper No. 152.
White, H., (1982), Maximum likelihood estimation of unspecified models, Econometrica , 1-16
page 211
RiskMetrics – Technical Document
Third edition
Look to the J.P. Morgan site on the Internet for updates of
these examples or useful new tools
If there is no diskette in the pocket below, the examples it
contains are available from the Internet web pages
The enclosed Excel spreadsheets are intended as a demonstrator of the RiskMetrics market risk management methodology and volatility and correlation datasets. They have been
designed as an educational tool and should not be used for the
risk estimation of actual portfolio positions. Clients should
contact firms specialized in the design of risk management
software for the implementation of a market risk estimation
system. If you have any questions about the use of this
spreadsheet contact your local J.P. Morgan representative or:
New York
North America
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
London
Europe
Benny Cheung (44-71) 325-4210
cheung_benny@jpmorgan.com
Singapore
RiskMetrics
Technical Document examples
For Microsoft Excel
Asia
Michael Wilson (65) 326-9901
wilson_mike@jpmorgan.com
New York
May 26, 1995
Morgan Guaranty Trust Company
Market Risk Research
Scott Howard (1-212) 648-4317
howard_james_s@jpmorgan.com
RiskMetrics – Technical Document
Third edition
page 212
RiskMetrics products
Worldwide RiskMetrics contacts
Introduction to RiskMetrics: A 12-page document which
broadly describes the RiskMetrics methodology for measuring market risks.
For more information about RiskMetrics, please contact
the author or any person listed below:
North America
New York
Jacques Longerstaey (1-212) 648-4936
longerstaey_j@jpmorgan.com
Chicago
Michael Moore (1-312) 541-3511
moore_mike@jpmorgan.com
RiskMetrics Monitor: A quarterly publication which
discusses broad market risk management issues, statistical
questions as well as new software products built by third party
vendors to support RiskMetrics.
San Francisco
Paul Schoffelen (1-415) 954-3240
schoffelen_paul@jpmorgan.com
Toronto
Dawn Desjardins (1-416) 981-9264
desjardins_dawn@jpmorgan.com
RiskMetrics datasets: Two sets of daily estimates of future
volatilities and correlations of approximately 450 rates and
prices – each a total of 100,000+ datapoints. One set is to
compute short-term trading risks, the other for medium-term
investment risks. Datasets currently cover Foreign Exchange,
Government Bond, Swap, and Equity markets in up to 22
currencies. 11 commodities are also included. A
RiskMetrics Regulatory dataset which incorporates the latest
recommendations from the Basel Committee on the use of
internal models to measure market risk is now available.
Europe
RiskMetrics Directory: A short brochure on where to go
for risk management systems and other products related to
RiskMetrics.
RiskMetrics Databook: A 400-page document which
provides statistics and graphs of historical data, volatilities and
estimation errors on approximately rate and price series
includes in the RiskMetrics dataset and on a key set of
correlation series (subsets available to large institutional
clients only).
London
Benny Cheung (44-71) 325-4210
cheung_benny@jpmorgan.com
Brussels
Geert Ceuppens (32-2) 508-8522
ceuppens_g@jpmorgan.com
Paris
Ciaran O’Hagan (33-1) 4015-4058
ohagan_c@jpmorgan.com
Frankfurt
Guido Barthels (49-69) 712-4238
barthels_g@jpmorgan.com
Milan
Roberto Fumagalli (39-2) 774-4230
fumagalli_r@jpmorgan.com
Madrid
Jose Luis Albert (34-1) 577-1722
albert_j-l@jpmorgan.com
Zurich
Victor Tschirky (41-1) 206-8686
tschirky_v@jpmorgan.com
Asia
Bond Index Cash Flow Maps: A monthly insert into the
Government Bond Index Monitor outlining synthetic cash flow
maps of J.P. Morgan’s bond indices.
Trouble accessing the Internet? If you encounter any
difficulties in either accessing the J.P. Morgan home page on
http://www.jpmorgan.com or downloading the RiskMetrics
data files, you can call 1-800-JPM-INET in the United States.
Singapore
Michael Wilson (65) 326-9901
wilson_mike@jpmorgan.com
Tokyo
Yuri Nagai (81-3) 5573-1168
nagai_y@jpmorgan.com
Hong Kong
Martin Matsui (85-2) 973-5480
matsui_martin@jpmorgan.com
Australia
Debra Robertson (61-2) 551-6200
robertson_d@jpmorgan.com
RiskMetrics is based on, but differs significantly from, the market risk management systems developed by J.P. Morgan for its own use. J.P. Morgan does not
warrant any results obtained from use of the RiskMetrics data, methodology, documentation or any information derived from the data (collectively the "Data")
and does not guarantee its sequence, timeliness, accuracy, completeness or continued availability. The Data is calculated on the basis of historical observations
and should not be relied upon to predict future market movements. Examples are for illustrative purposes only; actual risks will vary depending on specific
circumstances. The Data is meant to be used with systems developed by third parties. J.P. Morgan does not guarantee the accuracy or quality of such systems.
Additional information is available upon request. Information herein is believed to be reliable but J.P. Morgan does not warrant its completeness or accuracy. Opinions and estimates constitute our judgment and are
subject to change without notice. Past performance is not indicative of future results. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. J.P. Morgan may hold
a position or act as market maker in the financial instruments of any issuer discussed herein or act as advisor or lender to such issuer. Morgan Guaranty Trust Company is a member of FDIC and SFA. Copyright 1994
J.P. Morgan & Co. Incorporated. Clients should contact analysts at and execute transactions through a J.P. Morgan entity in their home jurisdiction unless governing law permits otherwise.