Transcript MSCI PPT Template 2012

Current Global Equity Market Dynamics
and the Use of Factor Portfolios for Hedging
Effectiveness
Déborah Berebichez, Ph.D.
February 2013
©2013. All rights reserved.
©2012.
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Outline
I.
II.
III.
IV.
V.
©2013. All rights reserved.
Overview of Barra’s
Global Equity Model
Is Buying an Index Really
a Country bet?
Greece Case Study
Hedging out Undesired
Exposures with a FactorMimicking Portfolio
Rebalancing Frequency
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I. Overview of our
Global Equity
Model
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Barra Global Equity Model (GEM3) – Characteristics
 Barra Model Factors represent important drivers of both risk and return in
the global equity markets
 Common Factors are grouped into World, Country, Industry, Style, and
Currency components
 Barra Global Equity Model (GEM3) Long & Short Horizons
 Coverage of 77 Country Factors and 66 Currencies
 74,000+ Assets
 Daily Model Updates (Exposures, Covariance Matrix & Specific Risk)
 Optimization Bias Adjustment improves risk forecasts for optimized portfolios
 Volatility Regime Adjustment calibrates factor volatilities to current levels
 Daily model history back to 1997
 34 Industry GICS-based and 11 Style Factors
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GEM3 Regression Methodology
GEM3 treats country and industry factors symmetrically:
rn  f w   X nc f c   X ni fi   X ns f s  un
c







i
s
Every stock has unit exposure to World factor
Exposures to countries/industries given by (0,1)
Country and industry returns both net of World factor
Style exposures cap-weighted mean zero
Apply constraints to eliminate two-fold collinearity with World
Regression weighting: square-root of market-cap
Estimation universe: MSCI ACWI IMI
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Performance of Country Factors
Cumulative Return (Percent)
 USA has outperformed over sample period
 Japan has underperformed, with higher volatility
40
USA
Japan
20
0
-20
-40
1997 1999 2001 2003 2005 2007 2009 2011
Year
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Performance of Industry Factors
 Banking factor fared poorly during Internet Bubble and since 2007
 Airlines performed poorly from 1998-2008
Cumulative Return (Percent)
40
Airlines
Banks
20
0
-20
-40
-60
-80
1997 1999 2001 2003 2005 2007 2009 2011
Year
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11 Style Factors in GEM3











Beta
Momentum
Size
Earnings Yield
Residual Volatility
Growth
Dividend Yield
Book to Price
Leverage
Liquidity
Non-linear Size
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Descriptors of Residual Volatility Factor
 Residual Volatility
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Descriptor of Momentum Factor
 Momentum
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January 2013 Global Equity Market Watch – Highlights
 The World factor continued its positive performance with a 5% percent
return in January 2013. This marks eight months of non-negative monthly
performance for the World factor
 The Value Factor posted a 1.1 percent return in January 2013. This is the
highest return among the style factors, both by the absolute value and by
z-score
 The Japan factor remained the top contributor to cross-sectional
volatility for the second month in a row
 The Korea and Malaysia factors are among the bottom performers by zscore, and top contributors to cross-sectional volatility
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Mean trailing 12-month realized volatilities of country, industry
(cap-weighted) and style factors (equal-weighted)
 Country factors dominated in late 1990s
 Industries dominated during wake of
Internet Bubble
Mean Trailing 12m Volatility
20
15
20
Countries
Industries
Styles
15
10
10
5
5
0
1997 1999 2001 2003 2005 2007 2009 2011 2013
0
 Systemic
Financial
Crisis
Year
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II. Is Buying an
Index Really a
Country Bet?
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Is Buying an Index Really a Country Bet?
 When you buy/sell an Index such as MSCI Greece IMI to gain (long or
short) exposure to the country Greece, you are not only getting exposure
to Greece but to many other style and industry factors
 You are getting more (or less) than just the country. The returns (or lack
thereof) depend on the exposure to multiple underlying factors
 A real country bet like a pure Greece exposure can be achieved in two
ways:
 Constructing a factor-mimicking Greece portfolio (very high exposure to Greece and very
low exposure to every other country, style, industry and the world factor)
 Or by hedging out the underlying exposure to all other undesired factors
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-0.20
2012/02/20
2012/02/29
2012/03/09
2012/03/20
2012/03/29
2012/04/09
2012/04/18
2012/04/27
2012/05/08
2012/05/17
2012/05/28
2012/06/06
2012/06/15
2012/06/26
2012/07/04
2012/07/13
2012/07/24
2012/08/02
2012/08/13
2012/08/22
2012/08/31
2012/09/11
2012/09/20
2012/10/01
2012/10/10
2012/10/19
2012/10/30
2012/11/08
2012/11/19
2012/11/28
2012/12/07
2012/12/18
2012/12/27
2013/01/07
2013/01/16
2013/01/25
2013/02/05
2013/02/14
Malaysia Cumulative Returns 12 Months February 2013
 MSCI Malaysia IMI Daily Cumulative Returns (blue) (-14%)
 Pure Malaysia Market Returns (red) (0.27%)
0.10
0.05
0.00
-0.05
Pure Malaysia Mkt Factor
MSCI Malaysia IMI Index
-0.10
-0.15
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III. Greece Case
Study
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2012/02/20
2012/02/29
2012/03/09
2012/03/20
2012/03/29
2012/04/09
2012/04/18
2012/04/27
2012/05/08
2012/05/17
2012/05/28
2012/06/06
2012/06/15
2012/06/26
2012/07/05
2012/07/16
2012/07/25
2012/08/03
2012/08/14
2012/08/23
2012/09/03
2012/09/12
2012/09/21
2012/10/02
2012/10/11
2012/10/22
2012/10/31
2012/11/09
2012/11/20
2012/11/29
2012/12/10
2012/12/19
2012/12/28
2013/01/08
2013/01/17
2013/01/28
2013/02/06
2013/02/15
Greece Cumulative Returns 12 Months February 2013
 MSCI Greece IMI Cumulative Returns (blue) (8.5%)
 Pure Greece Country Returns (red) (39%)
60.00%
40.00%
20.00%
0.00%
-20.00%
-40.00%
-60.00%
MSCI Greece IMI -Cumulative Returns
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Pure Greece -Cumulative Returns
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MSCI Greece IMI Return Attribution
Source of Return
 Country is a large positive driver
of returns (34.43%)
 Risk Styles are a large detractor
from returns (-23.88%)
 The most negative influence
comes from the Residual
Volatility factor (-16.39%)
 Followed by the Momentum
factor (-10.22%)
 Large negative specific return
(-16.62%) not typical of an Index
Contribution to Return
Total Managed
8.48%
Residual
Common Factor
World
6.16%
22.78%
11.88%
Industry
0.35%
Country
34.43%
Risk Indices
-23.88%
Beta
2.56%
Book-to-Price
0.04%
Dividend Yield
0.01%
Earnings Yield
-0.17%
Growth
-0.09%
Leverage
0.13%
Liquidity
-0.08%
Momentum
-10.22%
Non-Linear Size
Residual Volatility
0.21%
-16.39%
Size
Specific
0.10%
-16.62%
Currency
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2.23%
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MSCI Greece IMI Return Attribution
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MSCI Greece IMI Residual Volatility Contribution to return
 Exposure of the Index to Residual Volatility
 Cumulative Residual Volatility Returns (blue)
 Contribution of Residual Volatility to the Index Returns (-16.39%)
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MSCI Greece IMI Momentum Contribution
 Exposure of the Index to Momentum
 Cumulative Momentum Returns (blue)
 Contribution of Momentum to the Index Returns (-10.22%)
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IV. Hedging out
Undesired
Exposures with a
Factor-Mimicking
Portfolio
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Characteristics of Factor Mimicking Portfolios
 A pure factor portfolio exactly replicates the payoffs to the factor
 A factor-mimicking portfolio strikes a balance between factor tracking and
index investability and replicability
 Achieves a high level of exposure to a particular factor (the “Target Factor”) and very low
exposure to all other styles, industries, countries and the world factor, while minimizing
specific risk
 Constraints can be number of constituents, monthly turnover, trade limit, shorting cost,
etc
 Applications:
 PASSIVE: To capture alpha as the basis for ETFs for style investing such as value, growth,
large-cap, etc
 ACTIVE: To hedge out undesired risk
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Constructing a Factor-Mimicking Portfolio
 Barra Aegis Optimizer Settings
 Benchmark: Pure Factor Asset
 Universe: MSCI ACWI IMI
 Trading Constraint: Maintain Exposures Close to the Benchmark
 Style Constraints:
 Risk Style Exposures = All Zero except for a Target Exposure of 1 to the desired Style
 Country Equity Exposures = All Zero Country Equity Exposures
 Industries Exposures = All Zero Industry Exposures
 World Equity Exposure = Zero World Equity Exposure
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Characteristics of Residual Volatility Portfolio
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Risk Style Exposures for Residual Volatility Portfolio
 Insignificant Exposures to countries, sectors and the World Equity Factor
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V. Rebalancing
Frequency
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Change in Quality of the
Factor-Mimicking Portfolios
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Exposure of Initial Portfolio to Residual Volatility Over Time
 One Year Degradation (no rebalancing)
 With monthly rebalancing
Factor mimicking portfolios get degraded over time. This determines the frequency of
rebalancing
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Exposure of Initial Portfolio to Momentum Over Time
 One Year Degradation (no rebalancing)
 With monthly rebalancing
Factor mimicking portfolios get degraded over time. This determines the frequency of
rebalancing
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Notice and Disclaimer

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RV Jan 2012
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A Brief Digression: Risk Attribution
Rt   xm gmt
Return Attribution, Period t
m
xm  Source Exposure;
g mt  Source Return
  R    xm  gm    gm , R 
m
Risk Attribution
x-sigma-rho formula
 Identifies three drivers of time series volatility
 Risk contributions are intuitive and fully additive
 Aligns risk attribution model with investment process
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Exact CSV Decomposition
rn  n  un
n   f k  X nk
Return Decomposition (factor vs specific)
Linear Factor Structure
k
 ( )   f k    X k     X k ,  
k
Explained CS Volatility
x-sigma-rho formula
 Identifies three drivers of cross-sectional volatility
 Volatility contributions are intuitive and fully additive
 CSV can be attributed to individual factors!
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Approximate CSV Decomposition
 Collinearity among GEM2 factors is typically small
 Reasonable and useful approximation:
 ( )  
k
2

Xk 

2
fk
  
No-collinearity
Approximation
 Contribution to explained CSV is roughly proportional to the squared
factor return and the variance of factor exposures
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Style Factor Selection
 Good style factors should:
 Significantly increase explanatory power of model
 Have high statistical significance
 Be stable across time
 Not be excessively collinear with other factors
 Be intuitive and consistent with investors’ views
 Stability Measure:
 kt  corr  X kt , X kt 1 
Factor Stability
Coefficient
 Collinearity Measure:
X nk   X nl bl + nk
l k
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1
 VIFk 
1  Rk2
Variance Inflation
Factor
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Contribution of Style Factors to Cross-Sectional Volatility
Monthly RMS Contribution (%)
 Beta dominated in the aftermath of the Internet bubble
 Momentum dominated in late 1990s and in 2009
1.2
1.0
Momentum
Beta
0.8
0.6
0.4
0.2
0.0
1997
1999
2001
2003
2005
2007
2009
2011
Year
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Outline
 Model Highlights and Overview
 Methodology Details
 Factor Structure
 Explanatory Power
 Optimization Bias Adjustment
 Volatility Regime Adjustment
 New Specific Risk Model
 Additional Empirical Results
 Summary
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Model Highlights




Full daily updates of all components of the model
Extended coverage to 22 frontier markets
Enhanced style factors
Methodology Advances:
 An innovative Optimization Bias Adjustment methodology designed to provide improved
risk forecasts for optimized portfolios by reducing the effects of sampling error
 Volatility Regime Adjustment designed to calibrate volatility forecasts to current levels
 A new specific risk model based on daily asset-level specific returns with Bayesian
adjustment designed to reduce biases due to sampling error
 Improved risk forecasts
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GEM3 Regression Methodology: Constraints
GEM3 Regression:
rn  f w   X nc f c   X ni fi   X ns f s  un
c
i
s
Cap-weighted country/industry factor returns sum to zero:
w f
c c
0;
c
w f
i i
0
Constraints
i
f k   kn rn
Factor returns
n
 kn gives the weight of stock n in pure factor portfolio k
Interpret fw as the cap-weighted return of the world portfolio
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Style Factor Selection
 Good style factors should:
 Significantly increase explanatory power of model
 Have high statistical significance
 Be stable across time
 Not be excessively collinear with other factors
 Be intuitive and consistent with investors’ views
 Stability Measure:
 kt  corr  X kt , X kt 1 
Factor Stability
Coefficient
 Collinearity Measure:
X nk   X nl bl + nk
l k
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1
 VIFk 
1  Rk2
Variance Inflation
Factor
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-20.00%
2012/02/20
2012/03/01
2012/03/13
2012/03/23
2012/04/04
2012/04/16
2012/04/26
2012/05/08
2012/05/18
2012/05/30
2012/06/11
2012/06/21
2012/07/02
2012/07/12
2012/07/24
2012/08/03
2012/08/15
2012/08/27
2012/09/06
2012/09/18
2012/09/28
2012/10/10
2012/10/22
2012/11/01
2012/11/13
2012/11/23
2012/12/05
2012/12/17
2012/12/27
2013/01/08
2013/01/18
2013/01/30
2013/02/11
Korea Cumulative Returns 12 Months February 2013
 MSCI Korea Daily Cumulative Returns (blue) (1.27%)
 Pure Korea Market Returns (red) (-9%)
10.00%
5.00%
MSCI Korea Daily Returns
0.00%
Korea Mkt Factor
-5.00%
-10.00%
-15.00%
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Optimization Bias
Adjustment
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Initial Factor Covariance Matrix




Use daily factor returns to estimate factor covariance matrix (FCM)
Use shorter half-life to estimate volatilities (responsiveness)
Use longer half-life for correlations (conditioning)
Account for serial correlations and asynchronicity using the Newey-West
method
Model
GEM3S
GEM3L
Factor
Volatility
Half-Life
84
252
Newey-West
Factor
Volatility
Correlation
Lags
Half-Life
10
504
10
504
Newey-West
Correlation
Lags
3
3
Factor
CSV
Half-Life
42
168
 S-Model designed for most accurate forecasts at one-month horizon
 L-Model designed for greater stability in risk forecasts (less responsive)
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Eigenfactors and Optimization Bias
 Traditional risk models tend to underpredict the risk of optimized
portfolios
 This bias is related to estimation error in the covariance matrix
 Eigenfactors represent uncorrelated linear combinations of pure factors
 Eigenfactors solve certain classes of minimum variance optimizations
 Eigenfactors reliably capture systematic biases in the sample factor
covariance matrix (FCM)
 The biases can be demonstrated and estimated by simulation
 Removing the biases of the eigenfactors is effective at removing the biases
of optimized portfolios
Jose Menchero, DJ Orr, and Jun Wang. “Eigen-Adjusted Covariance Matrices,”
MSCI Research Insight, May 2011
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Optimization Bias Adjustment Methodology






Assume that the sample FCM F0 denotes the “true” FCM
Simulate a set of factor returns fn from F0 (e.g., Cholesky approach)
Compute simulated FCM Fn using same estimator as used for F0
Diagonalize Fn to obtain simulated eigenfactor volatilities
Use F0 to compute the “true” volatilities of simulated eigenfactors
Compute the average bias of simulated eigenfactors by Monte Carlo
simulation
 Assume F0 suffers from the same biases as the simulated FCM and debias the eigenvariances
 Transform adjusted FCM back to the original pure basis
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Volatility Regime Adjustment
for Factors
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Volatility Regime Adjustment for Factor Covariance Matrix
 Construct factor covariance matrix F using “standard” time-series
techniques (e.g., EWMA with serial correlation adjustments)
 Use cross-sectional observations (bias statistics) to calibrate factor
volatilities  k to current levels
1
B 
K
2
t
 f kt 
k   
 kt 
F2   Bt2 t
2
Cross-Sectional Bias
Statistic (squared)
(EWMA)
F
Factor Volatility Multiplier
t
 k  F k  F   F
2
F
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Volatility Regime Adjusted
Factor Covariance Matrix
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Volatility Regime Adjustments for Factor Covariance Matrix
Industry and Style Factors
1.8
Factor
Volatility
Multiplier
1.6
1.4
1.6
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
Factor
CSV
0.2
1995 1997 1999 2001 2003 2005 2007 2009 2011
Year
CSVt 
F
1
2
f

kt
K k
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0.2
Factor CSV (percent daily)
Factor Volatility Multiplier
1.8
 Cross-sectional
observations provide
an “instantaneous”
measure of factor
volatility levels
 During stable periods,
Volatility Regime
Adjustment tends to
be very small
 Adjustments are rapid
and intuitive following
market shocks
 Volatility Regime
Adjustment helps
“when needed most”
(Factor CSV)
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Improvement with Volatility Regime Adjustment (Factors)
 Plot mean bias statistics (rolling 12m) of all factors, with and without
Volatility Regime Adjustment
Volatility Regime Adjustment (GEM3S)
1.4
1.4
Mean Bias Statistic
1.3
1.3
With Volatility
Regime Adjustment
1.2
1.2
1.1
1.1
1.0
1.0
0.9
0.9
0.8
0.8
0.7
0.6
1998
0.7
Unadjusted
0.6
2000
2002
2004
2006
2008
2010
 With Volatility Regime
Adjustment, most
months the mean bias
statistics are closer to
the ideal value of 1
 Volatility Regime
Adjustment reduces the
underforecasting bias
during crises and the
overforecasting bias
following crises
2012
Year
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