Global versus Local Asset Pricing:
A Speculation Based Test of Market Integration
Imperial College London
October 19, 2010
Harald Hau
INSEAD
http://www.haraldhau.com
1
Motivation
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Cost of capital calculation depend on stock betas and
are a key input into capital budgeting decisions
We have no consensus about whether global or local
market betas most appropriate
This paper uses a large global index revision as an
identification mechanism to study market integration in
terms of risk pricing
Idea: Index change was large enough to change market
benchmark and therefore all stock betas
© Harald Hau, INSEAD
2
Three Dimensions of the Analysis
Asset
Pricing
Market
Integration
Limited
Arbitrage
© Harald Hau, INSEAD
3
Asset Pricing Issues
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Asset pricing test are joint tests of the market
benchmark and the pricing model
Roll’s critique: Market benchmark is difficult
(impossible) to identify
What is the market benchmark?
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All capital in assets subject to dynamic optimization
Not part of the market benchmark:
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Capital of index funds
Capital held for control reasons (family ownership, etc)
Capital of retail investors? (no diversification, investment
inertia)
© Harald Hau, INSEAD
4
Asset Pricing for Market Benchmark Change
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Idea: Test asset pricing model in differences for a
change in the market benchmark
Large index change can identify a market
benchmark change:
 Old benchmark:
(kx1 vector)
So
 Index change:
w  wn  wo (kx1 weight changes)
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New benchmark:
S n  S o  w
Market benchmark change identifies exogenous
beta change
© Harald Hau, INSEAD
5
Asset Pricing for Market Benchmark Change
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Beta is the return covariance with the market benchmark
i 

1
 m2
covRi , Rm  
1
 m2
covRi ,  j w j R j  
1
 m2
i S o
Benchmark change alters all stock betas:
 i  
1
 m2
 i  ( w n  wo )
   Σ( wn  wo )

Return effect (for constant cash flows):
ri  log Pit t  log Pit  ( r n it t  r o it )  i ( wn  wo )
© Harald Hau, INSEAD
Three Dimensions of the Analysis
Asset
Pricing
Market
Integration
Limited
Arbitrage
© Harald Hau, INSEAD
7
Market Integration Issues
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Global or local market benchmark?
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Integration test depend on the correct identification of both
benchmarks
Conditional asset pricing models: Difficult specification choices and
implementation issues
Event study approach provides alternative:
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Covariance decomposition:
  
G
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L
Int
Global covariance: G
L
National covariance terms: 
International covariance terms:
© Harald Hau, INSEAD
 Int
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Testing for Market Integration
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Local asset pricing:
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Only weight changes of local stocks affect (via local benchmark
change) asset prices:
p  Lw , no return effect for Int w
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Global asset pricing:
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Weight changes of all stocks (globally) affect (via global
benchmark change) asset prices:
p  G w  Lw  Int w
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Equally strong returns effects from local and international
covariance terms
Need not identify either global or local market benchmark
© Harald Hau, INSEAD
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Three Dimensions of the Analysis
Asset
Pricing
Market
Integration
Limited
Arbitrage
© Harald Hau, INSEAD
10
Arbitrage of Index Change
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Index change ∆w might have been predicted by some
speculators (arbitrageurs)
Front-running might accelerate price adjustment
Speculators may acquire hedging positions to reduce
arbitrage risk:
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Positions of risk neutral speculators:
x  w
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Positions of risk averse speculators:
x  w   w
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Need to control of price impact of hedging term
© Harald Hau, INSEAD
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Limited Arbitrage Literature
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Limited arbitrage in multiple assets:
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Degree of arbitrage depends each asset depends on marginal
risk contribution of each asset
“Return chasing” arbitrage vector is modified by hedging terms
which creates an additional price effect
Generalize Greenwood model (2007) by allowing a price
elastic (uninformed) liquidity supply in each asset
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Arbitrageurs learn about index change: t A
Liquidity suppliers learns about index change:
Index change occurs: tu
© Harald Hau, INSEAD
tL
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Speculative Price Dynamics
© Harald Hau, INSEAD
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Event and Data
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MSCI (Morgan Stanley Capital International Inc.)
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Most important international index
US$ 3 trillion benchmarked, US$ 300-350 directly indexed
MSCI all country world index (= 50 countries, 2077 stocks)
Event: Move to new free float weights
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Nov 2000: Industry Consultation on index change
Dec 1, 2000: Announcement that decision on free float weights
adoption is imminent and to be communicated on Dec 10, 2000
Dec 10, 2000: Time table for index change becomes public:
 First adjustment of 50% on Nov 30, 2001
 Second adjustment of 50% on May 31, 2002
© Harald Hau, INSEAD
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Country Weight Changes of Index Revision
w n  wo
n
o
1
2 (w  w )
© Harald Hau, INSEAD
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Event and Data
Position
Build-up
Period
Hedge
Liquidation
Period
Dec 1, 2000:
Decision day announcement
Nov 30, 2001:
May 31, 2002:
1. Implementation
Event
2. Implementation
Event
Dec 10, 2000:
Index change announcement

Estimate covariance matrix over period July 1, 1998 to July 1, 2000
© Harald Hau, INSEAD
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Position Build-up and Hedge Liquidation
© Harald Hau, INSEAD
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Event and Data
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MSCI ACWI: 2077 stocks originally
489 inclusions and 298 deletions
Exclude two crisis countries: Turkey and
Argentina with 62 stocks
31 stocks not found in Datastream and 182 with
incomplete price history
Sample: 2291 stocks (of which 396 are
inclusions and 265 excluded stocks)
© Harald Hau, INSEAD
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Summary Statistics I
© Harald Hau, INSEAD
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Summary Statistics II
© Harald Hau, INSEAD
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Evidence
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Testing dynamic predictions:
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Position buildup period: Event windows up to December 1
Hedge Liquidation period: Event windows after December 1
Global versus local risk pricing:
G   L   Int
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Integrated risk pricing between developed and emerging
market stocks?
G
H
CH
  
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Segmentation of EM stocks by cross-listing and liquidity?
G  H  CH  H  List  List
G  H  CH  H  Liq  Liq
© Harald Hau, INSEAD
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Position Buildup Period
Predictions:
1  0
1  0
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Stocks with beta decreases experience positive event returns
Stocks with high arbitrage risk have negative returns (short selling)
© Harald Hau, INSEAD
22
Hedge Liquidation Period
Prediction:
2  0
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Gradual liquidation of the hedge position (after December 1)
generates a positive return effect for stocks with high arbitrage risk
© Harald Hau, INSEAD
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Global versus Local Pricing
Integration:
L
Int
(i ) 1  1  0
1L  1Int  0
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Segmentation:
L
Int
(ii ) 1  1  0
1L  1Int  0
Local asset pricing hypothesis strongly rejected
Global asset pricing hypothesis cannot be rejected
© Harald Hau, INSEAD
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Segmentation between Emerging and Developed Markets
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Decompose into two hemispheres:     
CH
Matrix  captures risk pricing integration between developed
and emerging markets
G
H
CH
Find evidence for market integration between developed and
emerging markets
© Harald Hau, INSEAD
25
Role of Cross-Listing and Liquidity
 H  CH  H  List  List
 H  CH  H  Liq  Liq
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Cross-Listing Decomposition: 
Liquidity Decomposition:
G
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Cross-listed and liquid emerging market stocks are integrated
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G
© Harald Hau, INSEAD
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Robustness I
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Traditional (univariate) price pressure hypothesis does not have a
good cross-sectional fit:
© Harald Hau, INSEAD
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Robustness II
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Robustness with respect to the matrix 
~
Replace by a new matrix  based the 20, 40 or 60 principle
components of 
Obtain qualitatively similar results
© Harald Hau, INSEAD
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Conclusions
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MSCI index revision allows us to
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apply CAPM in differences (without benchmark identification)
obtain insights into the structure of arbitrage trading (the role of
hedging positions)
explore the degree of integrated risk pricing (global versus local
beta)
Evidence:
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Predicted beta changes explain front-running returns
Hedging demand has an important (temporary) price effect
Market Integration:
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Global risk pricing is supported by the event returns, while local risk pricing
is rejected in the data
Only illiquid stocks emerging market and stocks without cross listing show
evidence for segmentation
© Harald Hau, INSEAD
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