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多因子模型的分类和定义。blackrock基金管理人编写。量化多因子模型开发必读
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Factor Models for Asset Returns
Eric Zivot
University of Washington
BlackRock Alternative Advisors
March 14, 2011
Outline
1. Introduction
2. Factor Model Specification
3. Macroeconomic factor models
4. Fundamental factor models
5. Statistical facto r models
Introduction
Factor models for asset returns are used to
• Decompose risk and return into explanable and unexplainable components
• Generate estimates of abnormal return
• Describe the covariance structure of returns
• Predict returns in specified stress scenarios
• Provide a framework for portfolio risk analysis
Three Types of Factor Models
1. Macroeconomic factor model
(a) Factors are observable economic and financial time series
2. Fundamental factor model
(a) Factors are created from observerable asset characteristics
3. Statistical factor model
(a) Factors are unobservable and extracted from asset returns
Factor Model Specification
The three types of multifactor models for asset returns have the general form
R
it
= α
i
+ β
1i
f
1t
+ β
2i
f
2t
+ ···+ β
Ki
f
Kt
+ ε
it
(1)
= α
i
+ β
0
i
f
t
+ ε
it
• R
it
is the simple return (real or in excess of the risk-free rate) on asset i
(i =1,...,N) in time period t (t =1,...,T),
• f
kt
is the k
th
common factor (k =1,...,K),
• β
ki
is the factor loading or factor beta for asset i on the k
th
factor,
• ε
it
is the asset specificfactor.
Assumptions
1. The factor realizations,
f
t
, a re stationary with unconditional moments
E[
f
t
]=μ
f
cov(f
t
)=E[(f
t
− μ
f
)(f
t
− μ
f
)
0
]=Ω
f
2. Asset specific erro r terms, ε
it
, are uncorrelated with each of the common
factors, f
kt
,
cov(f
kt
,ε
it
)=0, for all k, i and t.
3. Error terms ε
it
are serially uncorrelated and contemporaneously uncorre-
lated across assets
cov(ε
it
,ε
js
)=σ
2
i
for all i = j and t = s
=0, otherwise
Notation
Vectors with a subscript t represent the cross-section of all assets
R
t
(N×1)
=
⎛
⎜
⎝
R
1t
.
.
.
R
Nt
⎞
⎟
⎠
,t=1,...,T
Vectors with a subscript i represent the time series of a given asset
R
i
(T ×1)
=
⎛
⎜
⎝
R
i1
.
.
.
R
iT
⎞
⎟
⎠
,i=1,...,N
Matrix of all assets over all time periods (columns = assets, r ows = time period)
R
(T ×N)
=
⎛
⎜
⎝
R
11
··· R
N1
.
.
.
.
.
.
.
.
.
R
1T
··· R
NT
⎞
⎟
⎠
Cross Section Regression
The multifactor model (1) may be rewritten as a cross-sectional regression
model at time t by stacking the equations for each asset to give
R
t
(N×1)
= α
(N×1)
+ B
(N×K)
f
t
(K×1)
+ ε
t
(N×1)
,t=1,...,T (2)
B
(N×K)
=
⎡
⎢
⎣
β
0
1
.
.
.
β
0
N
⎤
⎥
⎦
=
⎡
⎢
⎣
β
11
··· β
1K
.
.
.
.
.
.
.
.
.
β
N1
··· β
NK
⎤
⎥
⎦
E[ε
t
ε
0
t
|f
t
]=D = diag(σ
2
1
,...,σ
2
N
)
Note: Cross-sectional heterosked asticity
Time Series Regression
The multifactor model (1) may also be rewritten as a time-series regression
model for asset i by stacking observations for a given asset i to give
R
i
(T ×1)
= 1
T
(T ×1)
α
i
(1×1)
+ F
(T ×K)
β
i
(K×1)
+ ε
i
(T ×1)
,i=1,...,N (3)
F
(T ×K)
=
⎡
⎢
⎣
f
0
1
.
.
.
f
0
T
⎤
⎥
⎦
=
⎡
⎢
⎣
f
11
··· f
Kt
.
.
.
.
.
.
.
.
.
f
1T
··· f
KT
⎤
⎥
⎦
E[ε
i
ε
0
i
]=σ
2
i
I
T
Note: Time series homoskedasticity
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