1. Risk Management for Hedge
Funds  January 2008
William Peets
Abstract
Hedge funds and other longshort investors can use the Barra factor models to manage their risk
in a variety of ways covering different complexities. Using the assetbyasset covariance matrix,
which incorporates all information in the estimated factor covariance matrix and the exposures of
individual assets to the factors, is the most complete way to assess risk using Barra models.
However, often the hedge fund risk officer’s objective is to put in place simple controls that
portfolio managers can execute in a straightforward way. This article describes different levels of
risk control ranging from the simplest case to the full risk model, focusing on what tradeoffs exist
for each use case.
Introduction
Hedge funds often employ strategies that utilize short selling, derivatives and other complex
instruments. Many hedge fund managers also use leverage as a means of increasing the
flexibility to exploit return opportunities. The dynamic nature of these strategies requires fund
managers to closely monitor their positions and capital allocation. The Barra factor models
provide an individual contribution to risk for each asset to the portfolio. This contribution to risk
captures the relative riskiness of the asset, taking into account its exposures to the various
systematic factors in the market as well as the forecast volatilities and correlations between these
factors. However, contribution to risk can be a somewhat unwieldy and unintuitive tool for hedge
fund managers. Thus hedge fund risk officers are often looking for simple and intuitive ways to
impose risk controls on their managers’ trades.
A straightforward method often employed is to impose limits on the exposure of the portfolio
to various systematic factors in addition to individual position limits. This approach has the
disadvantage of not taking into account the different risk profiles of the individual factors.
Controls can then be adjusted to take into account factor volatilities either with, or without,
the correlations between factors. Of course, ideally the risk manager wants to use all of the
information he has at his disposal without sacrificing tractability in implementing risk controls.
In this paper, we discuss the different ways in which hedge funds could potentially implement risk
controls. Our goal is to clarify the assumptions for each type of risk control framework and what
tradeoffs the hedge fund risk manager faces.
1. Three Use Cases for Hedge Fund Risk Management
Exposures and Risk Forecasts in the Barra Factor Models
The aim of a fundamental factor model is to explain security level returns using a set of common
factors. These factors should be intuitive and robust. In the case of the US long horizon model
(USE3L), there are 55 industry factors and 13 style or risk factors. Style factors are created using
descriptors that are based on fundamental ratios such as market capitalization, dividend yield,
leverage, etc. The exposures of individual securities to these factors are observable and factor
returns are estimated via crosssectional regressions. (Factor returns are estimated by regressing
the equity returns of the estimation universe on the set of exposures every day.) Once a history of
factor returns is produced, a factor covariance matrix can be constructed.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 1 of 11
Please refer to the disclaimer at the end of this document.
2. Risk Management for Hedge Funds
 January 2008
The forecast risk for any security or portfolio is then a function of its exposures and the factor
covariance matrix.1
It is important to highlight that security exposures are normalized with respect to the estimation
universe which consists roughly of the largest 2000 securities in the US equity universe.
Normalizing the exposures this way implies that the weighted average of the estimation universe
has an exposure of 0 and standard deviation of 1.0 to each of the factors in the model. This
allows for a simple interpretation of factor exposures on the asset level. For example, if a security
has an exposure of 1.0 to the USE3L Momentum factor, this means it has an exposure that is one
standard deviation greater than the average exposure of the estimation universe. If Momentum is
expected to increase by 5% and a stock has an exposure of 1.0 to Momentum, then the stock is
expected to also increase by 5%, holding all else equal. Portfolio level exposures are simply the
weighted average of security level exposures.
Case 1: Imposing Limits Using Dollar Exposures
One way how some managers control risk is to set limits on a portfolios’ factor exposures.
Exposures are easy to compute and interpret since a portfolio’s exposure to a factor is just the
weighted average of the stocks’ exposures. Portfolio returns are linearly related to factor returns,
as described above, holding all else equal. Managers can convert the exposures to dollar
exposures by scaling the exposures by the position or portfolio value, which avoids the need to
calculate weights given the ambiguity of using them in the context of longshort portfolios.
DE f = PositionValue × X f (1)
where:
PositionValue = dollar value of the position
X f = Barra exposure of the position to factor f
Figure 1 illustrates the ease of computing dollar exposures.
Figure 1: Computing Dollar Exposures
Position USE3L MOM USE3L MOM
Security Weight
Value Exp $Exp
AAPL 82.45% 3,604.40 2.27 $8,193
GOOG 158.35% 6,922.60 0.20 $1,378

GS 4,550.40 1.42 $6,466
104.09%
MS 36.71% 1,605.00 0.40 $645.21
$4,372 0.86 $3,749
Weighted Port. Dollar
Total Port. Val. Exp Exp.
σ P = (wP (X T FX + Δ )wP ) where wP = vector of portfolio weights, X
T1/ 2
1
Specifically, the risk of a portfolio is = exposure
matrix, F = factor covariance matrix, and Δ = diagonal matrix of specific risk.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 2 of 11
Please refer to the disclaimer at the end of this document.
3. Risk Management for Hedge Funds
 January 2008
Dollar exposures capture how much money is exposed to the volatility of systematic risk and can
be calculated with respect to any of the factors in a model. For example, the manager may be
asked to control his or her dollar exposure to any of the USE3 factorsfor example Momentum,
Value, Growth, Size, or Leverage. A simple way of imposing risk limits is to cap the dollar
exposure any one manager can be exposed to for a certain factor. A similar use of dollar
exposures is to instruct portfolio managers to be dollarfactorneutral or confined within some
bound. For instance, portfolio managers may be required to have their net (long plus short) style
factor exposures within a given range of the singleside book value.2 Enforcing constraints like
these are very common in marketneutral and portable alpha strategies where a manager’s
success in isolating alpha depends on his or her ability to remain factorneutral.
Note that one pitfall with dollar exposures is that they may sometimes be unintuitive in that the
total dollar exposure of a portfolio can exceed the actual value of the portfolio. Thus if the hedge
fund has a 2.0 exposure to Momentum on a USD 1 billion portfolio, then the dollar exposure is
USD 2 billion. This may at first glance seem counterintuitive but does in fact represent the true
dollar exposure of the position though it does not offer insights into the risk coming from the
Momentum exposure.
Case 2: Riskadjusted Exposure Limits and Volatility Budgeting
So far, we have not accounted for differences in volatility across factors. In a later example, we
see that, Momentum has been a more volatile factor than Value. Thus, when setting limits using
dollar exposures, a hedge fund manager may want to consider the individual factor volatilities.
Specifically, dollar exposures can be adjusting using the forecasted risk for the relevant factor:
DollarsatRisk f = PortValue × X f × σ f (2)
where
X f = the exposure of the portfolio to factor f
σf = the forecast volatility of factor f
The quantity in Equation 2 is often called “dollar volatility” or “dollarsatrisk” and represents the
actual portfolio value at risk coming from the factor in question. Similar to dollar exposures, a
hedge fund risk manager can set limits on the dollarsatrisk for each factor which explicitly will
take into account the risk coming from the factor.
Dollarsatrisk can also be useful as a first step in volatility budgeting. Given some total dollar
amount at risk (the portfolio’s total value perhaps scaled for risk), the risk manager can set a
budget for each individual systematic factor. For example, he might state that no more than 20%
of dollar volatility can come from any one factor. Therefore, for a portfolio of USD 100 million, no
more than USD 20 million can be allocated to any one factor. The risk manager can then
compare his dollarsatrisk (from Equation 2) against this budget. Alternatively he can set the
maximum dollar exposure for each factor as:
PortfolioValue * k
MaxDollarExposure f = (3)
σf
where k is the maximum allocation to any one factor (i.e., 20% in this example).
2
For instance, assume a manager has a portfolio that is invested USD 100M long and USD 100M short, with the style factor budget being
+/10% of the single side book size. The hedge fund manager may wish to target some net exposure to a factor, for instance, 0.05 to the
Barra Momentum factor. The dollar exposure limit can then be used in an optimizer. The resulting portfolio might have a long dollar
exposure to Momentum of USD 20M, a short dollar exposure of USD 15M, with the net dollar exposure ending up as USD 5M. This ends
up being equivalent to 5% of a singleside book value and translates into the desired 0.05 (USD 5/USD 100) exposure to Momentum.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 3 of 11
Please refer to the disclaimer at the end of this document.
4. Risk Management for Hedge Funds
 January 2008
Case 3: Controlling Risk Through Beta
The last case we describe concerns the use of betas for controlling risk. Using betas to
understand the risk of the portfolio to the market as well as the individual factors has all the
advantages of the previous cases in that betas are tractable and relatively easy to work with.
They also make full use of the information in the risk model.
Expected profit and loss at the portfolio and individual asset level can be linked to the exposures
and the volatility of the factors through betas. The standard concept of beta links a security’s
return to aggregate market movements; in other words beta captures the sensitivity to market
wide risk. Within the Barra factor model, betas are called “fundamental betas” and capture the
risk from all sources of systematic or common factor risk. In addition, a variety of betas can be
computed, not just that of the beta to the market. Examples include:
The beta of a stock or portfolio to a market index
The beta of a factor (i.e., a factormimicking portfolio) to a market index
The beta of a stock or portfolio to some other portfolio
The beta of a stock or portfolio to a factor
The beta of one stock to another
Fundamental betas make use of the factor covariance matrix and the exposures of the individual
assets. They are calculated for a portfolio or security as:
cov(ri , rm ) wiT XFX T wm + wiT Δwm
β i ,m = = (4)
σm
2
σm
2
where
σ m = market volatility
wi = vector of N weights for a portfolio i
wm = vector of N weights for market portfolio
X = exposure matrix of N assets to K factors
F = K x K factor covariance matrix
Δ = N x N diagonal matrix of specific risk
Returning to our previous cases where the objective was to understand the risk of the portfolio
arising from some factor like Momentum, we introduce the concept of “factor betas.” Factor betas
are the betas of the Barra factors to the market. Then we can use the factor betas to compute the
beta of individual assets to the market purely due to its exposure to an individual factor f .
β i , f = xi , f β f (5)
where xi , f is the exposure of the stock to the factor and β f is the beta of the factor to the
market. Note that this representation is not the same as a stock’s beta to the market β i ,m .
The beta shown in Equation 5 is the beta of a stock to the market arising solely from (i.e., holding
all else equal) a factor.
To illustrate the use of these betas, consider the securities shown in Figure 2 with their exposures
to a handful of factors as of November 2007.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 4 of 11
Please refer to the disclaimer at the end of this document.
5. Risk Management for Hedge Funds
 January 2008
Figure 2: Portfolio Position Exposures in BarraOne
US Momentum US Growth US Leverage US Volatility
Asset Name Mkt Value
Exp Exp Exp Exp
Apple
$3,604 2.273 0.779 0.120 0.755
Computers
Google $6,923 1.421 3.671 0.556 0.135
Goldman Sachs $4,550 0.199 0.715 1.381 0.448
Morgan Stanley $1,605 0.402 0.362 1.451 0.028
Figure 2 tells us that the exposure of Apple to US Momentum is much higher than the other three
assets shown. Apple’s exposures to other factors shown—Growth, Leverage, and Volatility—are
not quite as large. But without knowing how volatile these factors are, we cannot say how risky
these exposures really are. Figure 3 shows these volatilities.
Figure 3: Sample Factor Exposure Report in BarraOne
Factor Volatility
US Volatility 4.79
US Momentum 3.81
US Earnings Yield 3.14
US Size NonLinearity 2.39
US Size 1.74
US Earnings Variation 1.53
US Trading Activity 1.52
US Value 1.46
US Leverage 1.42
US Growth 1.26
US Yield 1.19
US Currency Sensitivity 1.14
From Figures 2 and 3, we glean that for a USD 1M holding of Google, the position value of
Google is expected to change by +/USD 54,102 [USD 1M*1.42 (Exposure of Google to
Momentum) *3.81% (US Momentum Volatility) =USD 54,102] with a 68% probability over the
course of the next year through its exposure to US Momentum, assuming changes in Momentum
do not affect any of the other factors.3 In reality, a change in one factor usually does not take
place in isolation but in conjunction with other factor movements, meaning that we may be better
off using beta in Equation 5, which accounts for factor relationships.
3
Of course the portfolio manager must also be wary of deviations from normality since the distribution of certain factor returns can be
leptokurtic. This suggests the occurrence of a one sigma event is much more common and the frequency of 4 or 5 sigma events is much
higher than if factor returns were normally distributed. See MSCI Barra Research Bulletins, “Risk Management During Turmoil” (August 13,
2007) and “The End of the Momentum Run?” (November 15, 2007).
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 5 of 11
Please refer to the disclaimer at the end of this document.
6. Risk Management for Hedge Funds
 January 2008
Figure 4 shows the beta of the USE3L style factors relative to the MSCI US Prime Market 750
Index.
Figure 4: Factor Betas in BarraOne
Beta (MSCI US Prime
US Style Factor
Market 750 Index)
US Currency Sensitivity 0.0103
US Earnings Variation 0.0053
US Earnings Yield 0.0051
US Growth 0.0107
US Leverage 0.0013
US Momentum 0.0110
US Size NonLinearity 0.0472
US Size 0.0291
US Size NonLinearity 0.0424
US Trading Activity 0.0767
US Value 0.0004
US Volatility 0.3073
US Yield 0.0218
Combining the factor betas in Figure 4 with the exposures in Figure 2, we expect that given a 5%
return in the market, Apple is expected to move 1.16% due to Volatility, 0.125% due to
Momentum and so forth.
Lastly, we show the common factor, idiosyncratic, and total risk forecast for each of the securities
in our example in Figure 5. Style risk in Figure 5 reflects risk coming from the 13 US styles while
industry risk reflects risk due to the stock’s industry membership. Note that style and industry
variance sums to common factor variance, and that common factor variance and selection
variance sums to total variance, which we discuss in more detail in the next section.
Figure 5: Portfolio Position Risk in BarraOne
Common
Style Industry Selection Total
Asset Name Factor
Risk Risk Risk Risk
Risk
Apple Computers 11.14 13.36 20.93 35.46 41.18
Google 8.93 19.23 24.31 33.18 41.13
Goldman Sachs 6.18 19.54 23.72 28.02 36.71
Morgan Stanley 6.28 19.30 20.68 21.50 29.83
It is important to highlight that in Figure 5, selection risk dominates risk for these four stocks. For
most individual stocks, the larger portion of risk comes from the idiosyncratic risk, not common
factors. This implies that using factor betas and exposures to manage risk has a greater impact at
the portfolio level than the stock level. At the stock level, the selection risk must be managed
separate from the factor exposures. Still, assetlevel analysis can be helpful in understanding the
drivers of individual stock movements.
In sum, analyzing individual asset exposures and betas to certain factors and looking at betas to
the market via individual factors can be extremely useful for longshort portfolio managers.
Managers can moreover use this type of analysis in conjunction with their fundamental or
proprietary information including company comparisons, cash flow models, dividend discount
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 6 of 11
Please refer to the disclaimer at the end of this document.
7. Risk Management for Hedge Funds
 January 2008
models, etc. Barra exposures and betas may help to red flag certain positions, particularly those
for which the portfolio manager is less confident. In some instances, managers may identify large
unintended bets which can then be neutralized.
2. Additional Risk Tools
The previous three cases provide successively more detailed means of evaluating risk, each
using incrementally more information from the risk model. For a more comprehensive risk
analysis, additional tools are available to help augment trade decisions and better understand the
risk exposures and their sources.
First, a correlation report can be used in conjunction with a factor exposures report to better
understand the potential impact of factor return movements. Looking at Figure 6, which shows a
handful of correlations as of November 2007, we see that there is a moderate positive predicted
correlation between US Growth and US Volatility. This might suggest that a portfolio manager be
more sensitive to an asset that has a positive exposure to both Growth and Volatility.
Figure 6: Factor Correlation Report in BarraOne
USE3L Factor US Momentum US Leverage US Growth US Volatility
US Momentum 1.000
US Leverage 0.202 1.000
US Growth 0.127 0.139 1.000
US Volatility 0.093 0.051 0.234 1.000
Similar to the factor betas, this view addresses the impact of factor correlations. To better capture
the degree to which factor exposures contribute to or reduce risk, we can look at a risk
decomposition report in conjunction with a factor exposures report for either a portfolio or
individual security. Figure 7, for example, shows how risk can be decomposed for Apple
Computers to understand what are the biggest sources or contributors of risk.4 Figure 7 also
shows the exposures of Apple to the factors shown above in Figure 6.
4
The risk decomposition report shown in Figure 7 is performed using an adaptation of the BarraOne risk decomposition which is described
in Menchero (2006). See Menchero, J. “Portfolio Risk Attribution,” Journal of Performance Measurement, Spring 2006.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 7 of 11
Please refer to the disclaimer at the end of this document.
8. Risk Management for Hedge Funds
 January 2008
Figure 7: Apple Computers: XSigmaRho Risk Analysis Report
Apple Computers
Risk Source Portfolio Risk Portfolio Variance % Portfolio Risk
Local Market Risk 41.18 1,695.87 100.00%
Common Factor Risk 20.93 438.18 25.84%
Industry 13.36 178.50 10.53%
Style 11.14 124.14 7.32%
Factor Interaction N/A 135.54 7.99%
Selection Risk 35.46 1,257.70 74.16%
Total Risk 41.18 1,695.87 100.00%
% Contribution to Total Risk
Factor Volatility Exposure Risk MCTR Cont. from from Total
to TR Volatility Covariance
US Volatility 4.79 0.76 3.62 0.02 2.58 0.77% 2.41% 3.18%
US Momentum 3.81 2.27 8.66 0.01 3.27 4.42% 0.38% 4.04%
US Leverage 1.42 0.12 0.17 0.00 0.03 0.00% 0.04% 0.04%
US Growth 1.26 0.78 0.98 0.00 0.23 0.06% 0.22% 0.28%
Style Factors 6.110 5.25% 2.29% 7.54%
Total Portfolio
Risk Source Portfolio Risk Portfolio Variance % Portfolio Risk
Local Market Risk 54.81 3,004.52 100.000%
Common Factor Risk 25.34 642.07 21.370%
Industry 15.55 241.86 8.050%
Style 14.42 207.82 6.917%
Factor Interaction N/A 192.39 6.403%
Selection Risk 48.61 2,362.45 78.630%
Total Risk 54.81 3,004.52 100.000%
% Contribution to Total Risk
Factor Volatility Exposure Risk MCTR Cont. from from Total
to TR Volatility Covariance
US Volatility 4.79 0.38 1.82 0.01 0.42 0.11% 0.25% 0.36%
US Momentum 3.81 4.06 15.49 0.01 7.33 7.98% 1.81% 6.18%
US Leverage 1.42 2.95 4.20 0.00 1.76 0.59% 0.90% 1.48%
US Growth 1.26 5.58 7.01 0.00 2.24 1.63% 0.25% 1.89%
Style Factors 11.744 10.32% 0.41% 9.90%
Figure 7 shows that although US Volatility contributes a substantial amount to the total risk of
Apple, it plays a relatively minor role in the total risk of the portfolio. Furthermore, it shows that
US Leverage and US Growth have a larger impact on the total risk of the portfolio than they do to
Apple individually, and that the covariances between the factors at the portfolio level actually
reduce total risk.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 8 of 11
Please refer to the disclaimer at the end of this document.
9. Risk Management for Hedge Funds
 January 2008
Conclusion
Hedge funds and other longshort investors can use the information in Barra factor models to
manage their risk in a variety of ways. First, Barra exposures can be a useful metric for designing
limits on individual and portfolio positions. Second, adjusting these exposures for their respective
factor volatilities provides important additional information, for instance in setting up a risk budget.
Third, factor betas can help illuminate how correlations between factors impact portfolio risk.
Overall, factor models provide a flexible framework to allow risk managers to slice and dice the
components of risk in the manner he or she finds most valuable.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 9 of 11
Please refer to the disclaimer at the end of this document.
10. Risk Management for Hedge Funds
 January 2008
Contact Information
clientservice@mscibarra.com
Americas
Americas 1.888.588.4567 (toll free)
Atlanta + 1.404.949.4529
Boston + 1.617.856.8716
Chicago + 1.312.706.4999
Montreal + 1.514.847.7506
New York + 1.212.762.5790
San Francisco + 1.415.576.2323
Sao Paulo + 55.11.3048.6080
Toronto + 1.416.943.8390
Europe, Middle East & Africa
Amsterdam + 31.20.462.1382
Cape Town + 27.21.683.3245
Frankfurt + 49.69.2166.5325
Geneva + 41.22.817.9800
London + 44.20.7618.2222
Madrid + 34.91.700.7275
Milan + 39.027.633.5429
Paris 0800.91.59.17 (toll free)
Zurich + 41.1.220.9300
Asia Pacific
China Netcom 10800.852.1032 (toll free)
China Telecom 10800.152.1032 (toll free)
Hong Kong + 852.2848.7333
Singapore + 65.6834.6777
Sydney + 61.2.9033.9333
Tokyo + 813.5424.5470
www.mscibarra.com
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 10 of 11
Please refer to the disclaimer at the end of this document.
11. Risk Management for Hedge Funds
 January 2008
Notice and Disclaimer
This document and all of the information contained in it, including without limitation all text, data, graphs, charts
(collectively, the “Information”) is the property of MSCI Inc. (which is registered to do business in New York under the
name NY MSCI), Barra, Inc. (“Barra”), or their affiliates (including without limitation Financial Engineering Associates,
Inc.) (alone or with one or more of them, “MSCI Barra”), or their direct or indirect suppliers or any third party involved
in the making or compiling of the Information (collectively, the “MSCI Barra Parties”), as applicable, and is provided
for informational purposes only. The Information may not be reproduced or redisseminated in whole or in part
without prior written permission from MSCI or Barra, as applicable.
The Information may not be used to verify or correct other data, to create indices, risk models or analytics, or in
connection with issuing, offering, sponsoring, managing or marketing any securities, portfolios, financial products or
other investment vehicles based on, linked to, tracking or otherwise derived from any MSCI or Barra product or data.
Historical data and analysis should not be taken as an indication or guarantee of any future performance,
analysis, forecast or prediction.
None of the Information constitutes an offer to sell (or a solicitation of an offer to buy), or a promotion or
recommendation of, any security, financial product or other investment vehicle or any trading strategy, and
none of the MSCI Barra Parties endorses, approves or otherwise expresses any opinion regarding any
issuer, securities, financial products or instruments or trading strategies. None of the Information, MSCI
Barra indices, models or other products or services is intended to constitute investment advice or a
recommendation to make (or refrain from making) any kind of investment decision and may not be relied on
as such.
The user of the Information assumes the entire risk of any use it may make or permit to be made of the Information.
NONE OF THE MSCI BARRA PARTIES MAKES ANY EXPRESS OR IMPLIED WARRANTIES OR
REPRESENTATIONS WITH RESPECT TO THE INFORMATION (OR THE RESULTS TO BE OBTAINED BY THE
USE THEREOF), AND TO THE MAXIMUM EXTENT PERMITTED BY LAW, MSCI AND BARRA, EACH ON THEIR
BEHALF AND ON THE BEHALF OF EACH MSCI BARRA PARTY, HEREBY EXPRESSLY DISCLAIMS ALL
IMPLIED WARRANTIES (INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF ORIGINALITY,
ACCURACY, TIMELINESS, NONINFRINGEMENT, COMPLETENESS, MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE) WITH RESPECT TO ANY OF THE INFORMATION.
Without limiting any of the foregoing and to the maximum extent permitted by law, in no event shall any of
the MSCI Barra Parties have any liability regarding any of the Information for any direct, indirect, special,
punitive, consequential (including lost profits) or any other damages even if notified of the possibility of
such damages. The foregoing shall not exclude or limit any liability that may not by applicable law be
excluded or limited.
Any use of or access to products, services or information of MSCI or Barra or their subsidiaries requires a license
from MSCI or Barra, or their subsidiaries, as applicable. MSCI, Barra, MSCI Barra, EAFE, Aegis, Cosmos,
BarraOne, and all other MSCI and Barra product names are the trademarks, registered trademarks, or service marks
of MSCI, Barra or their affiliates, in the United States and other jurisdictions. The Global Industry Classification
Standard (GICS) was developed by and is the exclusive property of MSCI and Standard & Poor’s. “Global Industry
Classification Standard (GICS)” is a service mark of MSCI and Standard & Poor’s.
The governing law applicable to these provisions is the substantive law of the State of New York without regard to its
conflict or choice of law principles.
© 2008 MSCI Barra. All rights reserved.
About MSCI Barra
MSCI Barra is a leading provider of investment decision support tools to investment institutions worldwide. MSCI Barra
products include indices and portfolio analytics for use in managing equity, fixed income and multiasset class portfolios.
The company’s flagship products are the MSCI International Equity Indices, which are estimated to have over USD 3
trillion benchmarked to them, and the Barra risk models and portfolio analytics, which cover 56 equity and 46 fixed income
markets. MSCI Barra is headquartered in New York, with research and commercial offices around the world. Morgan
Stanley, a global financial services firm, is the majority shareholder of MSCI Barra.
MSCI Barra
© 2008 MSCI Barra. All rights reserved. 11 of 11
Please refer to the disclaimer at the end of this document.
Be the first to comment