Beyond Real Estate:
Examining Global Real Asset Allocation Frameworks for Institutional Investors
by
Xiangyu Li
B.Eng., Urban Planning, 2007
Sichuan University
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate in
Partial Fulfillment of the Requirements for the Degree of Master of Science in Real Estate Development
at the
Massachusetts Institute of Technology
September, 2012
©2012 Xiangyu Li
All rights reserved
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any medium now known or hereafter created.
Signature of Author_____________________________________________________________
Center for Real Estate
July 30, 2012
Certified by____________________________________________________________________
David Geltner
Professor of Real Estate Finance, Department of Urban
Studies and Planning
Thesis Supervisor
Accepted by____________________________________________________________________
David Geltner
Chair, MSRED Committee, Interdepartmental Degree
Program in Real Estate Development
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2
Beyond Real Estate:
Examining Global Real Asset Allocation Frameworks for Institutional Investors
By
Xiangyu Li
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate on
July 30, 2012 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Real
Estate Development
ABSTRACT
Real estate is often considered an asset to provide long term value enhancement and to protect
institutional investors against inflation risk. It is a typical real asset due to the physical form and fixed
geographic location with a steady return. However, real estate has its limitations. Risks associated with it
such as lack of trading flexibility, special property management expertise required, and a growth prospect
not always applicable towards the short term favor have impeded certain institutional investors from
allocating major investment in real estate.
In management of a dynamic investment portfolio, how institutional investors look at certain real assets is
the key issue discussed in this thesis. Infrastructure, for instance, which can refer to roll roads, shipping or
railways, is a comparable asset with real estate as it demonstrates a term with physical form and stable
income stream. There are other types of real assets such as commodity, regulated utilities, and maritime
assets which are also studied.
This thesis delves into the dynamic structure of an institutional investment portfolio and targets to explore
the following questions: What do real assets contribute to institutional investors’ traditional stock-andbond portfolio? What kinds of correlations do real assets have with typical equity and fix-income assets?
How do institutional investors strategize their investment plan by allocating real assets in their global
portfolio?
The thesis is designed to study the underlying factors for determining the asset allocation framework from
both a qualitative and a quantitative perspective. A quantitative analysis including mean-variance
optimization, downside risk, correlations, risk parity and Value at Risk will test out how various asset
allocation frameworks position real assets in a portfolio. The study also brings in selected real estate
indexes to examine how different parings compare with each other and what impact does illiquidity
exhibits on portfolio management. An interview-based research is designed to provide understanding of
institutional investors’ perspective on how they apply the theoretical framework to the real world practice
and how they strategize the management of investment portfolios.
Thesis Advisor: David Geltner
Title: Professor of Real Estate Finance
3
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4
Acknowledgement
I would like to express my gratefulness to:
Dr. David Geltner (Professor of Real Estate Finance and Chair of MSRED Committee, Interdepartmental
Degree Program in Real Estate Development, MIT Department of Urban Studies and Planning) for
advising the thesis throughout the summer semester and his constant guidance along the way in exploring
the thesis research topic through various industry leaders;
Dr. William Wheaton (Professor, MIT Department of Economics) for his kind guidance in the early stage
of the thesis and his clear foresight on the thesis scope and direction;
Dr. Tony Ciochetti (Chairman, MIT Center for Real Estate) for his effort in bridging industry sponsorship
in the student thesis;
Mr. Pulkit Sharma (MIT CRE ’10) for his kind guidance in the thesis research and his invaluable help
throughout the conversations with industry practitioners at J.P. Morgan Asset Management;
Dr. Sheharyar Bokhari (PHD, MIT DUSP) for his continuous help on thesis research methodology and
data collection;
All the interviewees participating in this thesis including the real estate investment professionals, asset
management professionals in the United States, Europe, and Asia for offering their insightful comments
and expertise on investment management topics;
Industry professionals and MIT CRE alumni who offered their guidance and shared their professional
experiences. Jacques Gordon (LaSalle Investment Management), Alexandre Levy (GIC Singapore), Ying
Liao (Guotai Junan Securities Asset Management), Carrie Linyan Yang (Ping An Asset Management),
Andrew Wei Hu (Liancui Investment), Vance Si Wan (China Construction Bank), Steven Yusong Xie
(Mitsui), Ted Stover (FTSE), Brad Case (NAREIT), Antoinette Schoar (MIT Sloan School of
Management), Xiaoyang Guo (MIT exchange scholar, PHD, Tsinghua University), Allan Xiaojun Wu
(MIT CRE ’02), Arvind Pai (MIT CRE ’06), and Pulkit Sharma (MIT CRE ’10);
I would also like to thank Maria Vieira, the MIT CRE faculties and staff for their kind support and help;
Last, I would like to dedicate this thesis as a conclusion of my year at MIT to my parents, Shurong Lu,
Zhi Li and my family members for supporting me throughout the year.
-Xiangyu Li
5
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6
TABLE OF CONTE
ENTS
Chapter 1 Introducttion ............................................................................................................................... 11
1.1
Research Mo
otivation ....................................................................................................................... 11
1.2
Research Ou
utline ............................................................................................................................. 12
Chapter 2 An Overv
view of Real Asset
A
for Insstitutional In
nvestors ........................................................ 14
2.1
Definition ........................................................................................................................................ 14
2.2
C
................................................................................................................ 14
Real Asset Classification
2.3
Asset Allocaation Framewo
ork Review ............................................................................................. 15
2.4
Literature Reeview............................................................................................................................ 17
Chapter 3 Correlatio
on Analysis ................................................................................................................. 20
3.1
Objective ......................................................................................................................................... 20
3.2
odology .................................................................................................... 20
Data Resourcce and Metho
3.3
Methodology
y ................................................................................................................................... 21
3.4
Correlation Analysis
A
....................................................................................................................... 21
A
...................................................................................................................... 34
Chapter 4 Portfolio Analysis
4.1
Literature Reeview............................................................................................................................ 34
4.2
Data and Meethodology .................................................................................................................... 35
4.3
Portfolio Analysis ........................................................................................................................... 37
4.4
Conclusions ..................................................................................................................................... 44
Chapter 5 Value at Risk
R Analysis .............................................................................................................. 46
5.1
Definition ........................................................................................................................................ 46
5.2
Data and Meethodology .................................................................................................................... 46
5.3
Portfolio Sim
mulation ........................................................................................................................ 49
5.4
Downside VaR ................................................................................................................................ 55
5.5
P
Mod
del ............................................................................................................ 56
Traditional Portfolio
5.6
Conclusions ..................................................................................................................................... 59
Chapter 6 Quantifyiing Liquidity
y for Real Asset Transacttion in a Mixxed Portfolio .......................... 60
6.1
Objective ......................................................................................................................................... 60
6.2
Data Resourcce and Metho
odology .................................................................................................... 60
6.3
Rebalancing Analysis...................................................................................................................... 60
6.4
Conclusions ..................................................................................................................................... 68
Chapter 7 Institution
nal Investorss’ Asset Alloccation Strateegy ............................................................... 69
7.1
Objective ......................................................................................................................................... 69
7.2
S
...................................................................................................................... 69
Interviewee Selection
7.3
Research Meethodology ................................................................................................................... 69
7.4
Questionnairre .................................................................................................................................. 70
7
7.5
Topics and Discussion
D
.................................................................................................................... 71
Chapter 8 Final Con
nclusions ...................................................................................................................... 77
BIBLIOG
GRAPHY ......................................................................................................................................... 85
APPEND
DIX .................................................................................................................................................... 88
Append
dix 1-NPI, RE
EIT-based Pu
ureProperty & CPPI Indice s (2000-20122) .............................................. 88
Append
dix 2-Summaary of Sharpe--Markowitz Portfolio
P
.............................................................................. 89
Append
dix 3-Value at
a Risk Test Su
ummary .................................................................................................. 99
8
TABLE OF EXHIBITS
Figure 1-Institutional Plan Sponsor Asset Allocation Framework ............................................................. 11
Figure 2-Thesis Structure ............................................................................................................................ 13
Figure 3-PREA Surveyed 2010 Plan Sponsor Asset Allocation ................................................................. 15
Figure 4-PREA Surveyed 2010 Plan Sponsor Asset Allocation (by strategy) ........................................... 16
Figure 5-Distribution of Private Real Estate Investments by Strategy—By Plan Size ............................... 17
Figure 6-Scatter Plot of Returns and Risks among Selected Assets ........................................................... 22
Figure 7-Stock and Commodity Historic Index (1975-2011) ..................................................................... 23
Figure 8-Stock and Commodity Historic Annual Return (1975-2011)....................................................... 24
Figure 9-S&P 500 and NCREIF NPI Historic Index (1975-2011) ............................................................. 25
Figure 10-S&P 500 and NCREIF NPI Historic Annual Return (1975-2011)............................................. 25
Figure 11-S&P 500 and Composite Infrastructure Historic Index (1975-2011) ......................................... 26
Figure 12-S&P 500 and Composite Infrastructure Historic Annual Return (1975-2011) .......................... 27
Figure 13-Annual Returns of Selected Assets VS Inflation Movement ..................................................... 28
Figure 14-Scatter Plot of Returns and Risks among Selected Assets (1989-2011) .................................... 30
Figure 15- Quarterly Historic Index (1992-2011)....................................................................................... 31
Figure 16-Quarterly Returns (1992-2011) .................................................................................................. 32
Figure 17-Portfolio Analysis Data List ....................................................................................................... 35
Figure 18-List of Pairing Tests ................................................................................................................... 36
Figure 19-Markowitz Portfolio Comparison............................................................................................... 37
Figure 20-Asset Composition of the Efficient Frontier with Stock, Bond, Commodity, Infrastructure and
Real Estate .................................................................................................................................................. 38
Figure 21-PureProperty, REIT and NPI Historic Index (1999-2011) ......................................................... 39
Figure 22-PureProperty, REIT and NPI Annual Returns (1999-2011)....................................................... 40
Figure 23-Markowitz Portfolio Comparison Including PureProperty ........................................................ 41
Figure 24- Downside Markowitz Portfolio Comparison ............................................................................ 43
Figure 25-Data List for VaR Test ............................................................................................................... 47
Figure 26-One Random History ("Drawing") of the Aggregate Market Value History ............................. 49
Figure 27-One Random History ("Drawing") of the Asset Classes' Histories ............................................ 51
Figure 28-Selected Asset Simulation Run Scatterplot ................................................................................ 51
Figure 29-Histogram of Simulated Maximum Loss within Any One Year ................................................ 53
Figure 30-Histogram of Simulated 5-year Gain ......................................................................................... 54
Figure 31-Cumulative Distribution Graph .................................................................................................. 54
Figure 32-Histogram of Simulated Maximum Loss within Any One Year (Downside Risk) .................... 55
Figure 33-Histogram of Simulated 5-year Gain ......................................................................................... 55
Figure 34-Cumulative Distribution Graph (Downside Risk) ...................................................................... 56
Figure 35-Histogram of Simulated Maximum Loss within Any One Year (Traditional Portfolio) ........... 57
Figure 36-Histogram of Simulated 5-year Gain (Traditional Portfolio) ..................................................... 57
Figure 37-Cumulative Distribution Graph (Traditional Portfolio) ............................................................. 58
Figure 38-Individual REITs indexes vs Composite REIT index ................................................................ 61
Figure 398- Equity REIT Monthly Index (2000-2010)............................................................................... 62
Figure 40- Equity REIT Index Monthly Return.......................................................................................... 63
Figure 41- Equity REIT Annual Return ...................................................................................................... 63
Figure 42-Illustrative Rebalancing Graph................................................................................................... 66
Figure 43-Sample Turnover Summery of 22 Constituents of REITs.......................................................... 67
Figure 44-Investment Management Group Structure Sample .................................................................... 71
Figure 45-Summary of Portfolio Analysis .................................................................................................. 79
Figure 46-Value at Risk Analysis Summary............................................................................................... 84
9
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10
Chapter 1
1.1
Introd
duction
Ressearch Motiv
vation
Investors have always been adapting
g and adjustin
ng to the channging econom
mic environmeent. Managing
portfolioss of assets hass been a majorr challenge fo
or investmentt managers onn both instituttional and
individuall levels. Whille there are vaarious ways to
o look at a miixed portfolioo, this thesis ddelves into thee
institution
nal level to stu
udy the frameework of an in
nvestment poortfolio that deerives from thhe traditionallly
equity-bond heavy com
mposition and
d explores the roles of diffeerent real asseets that providde capital
appreciatiion and stablee income streaam and differrent risk outloook as part off a mixed porttfolio.
onal investorss have placed little weightss on real assetts such as reaal estate,
Traditionaally, institutio
infrastructture, and com
mmodity when
n managing a large pool off assets. The ffollowing diaagram shows tthe
typical alllocation framework for insstitutional inv
vestors.1
Figure 1-In
nstitutional Plaan Sponsor Assset Allocation Framework
F
Allocation by Percen
ntage
Other
O
4%
Real Estate
E
Cash
10%
1%
Hedge Funds
F
7%
%
Private Equity
E
8%
8
Bond
26%
Stocks
44%
0%
10%
20%
30%
Alloocation Percenttage
40%
50%
Source: 2012
2
Institutionaal Real Estate Inv
vestor Survey
Traditionaally, institutio
onal investorss have allocateed around 10 % to real estaate. While traaditional equitty and
fix-incom
me assets still account
a
for th
he majority off institutionall investors’ poortfolio, is theere a potentiaal to
examine real
r estate and
d other real asssets’ roles in
n a mixed porttfolio from a different persspective? Even
1
2012 Instittutional Real Estate investor Surrvey
11
ng the financiial crisis and w
was a catalysst for the derivvative markett
though real estate was hit hard durin
wn, it has histo
orically been providing staable income thhat is comparrable with bonnds as well ass a
crack dow
volatility that is much less
l than stocck. Certain inffrastructure asssets also act on par with rreal estate. Soo the
question comes
c
down to
t how a seriees of real asseets such as reaal estate, infraastructure, annd commodityy
perform in
n a portfolio that
t includes traditional sto
ocks and bondds. How do thhese real asseets act in term
ms of
inflation hedging,
h
diversification, an
nd return enhaancement rolees?
1.2
Ressearch Outline
or structures th
he thesis in a framework th
hat synergizees the differennt quantitativee models to teest out
The autho
the compaatibility of theese models. At
A the same tim
me, the thesiss looks into thhe associated issues concerrning
certain asset classes to revisit the mo
odel. Then th
he thesis adds in a qualitative interview--based researcch to
he findings in
n managing a mixed
m
investm
ment portfolioo and intendss to explore thhe explanationn of
solidify th
the disparrity between model
m
results and actuality
y.
While chaapter 1 introdu
uces of the th
hesis motivation and questiions, chapter 2 studies the traditional
allocation
n framework for
f institution
nal investors and
a looks at thhe characters of real assetss. It also tracees
back to paast industry publications an
nd academic researches
r
whhich focus onn asset allocattion and portffolio
managem
ment. Chapter 3 goes into th
he detailed po
ortfolio analyssis. A correlattion analysis looks at the
relationsh
hip among diffferent assets, including sto
ock, bond, com
mmodity, infr
frastructure, innflation, oil, aand
real estatee. This part allso explores th
he effect of th
he financial crrisis on these correlations and intends too find
implicatio
ons based on different
d
timee frames. Then the thesis fo
focuses on thee portfolio anaalysis using
Modern Portfolio
P
Theo
ory to test outt roles of seleccted real asseets and compaare these moddels with dow
wnside
risk modeels as well. Ch
hapter 4 takess the portfolio
o analysis furtther and creattes Monte Caarlo simulationn
using the portfolio weights developeed in chapter 3 to look at th
the Value-at-R
Risk of the poortfolio and
d risk model and downsidee risk model. Then the thessis takes a typpical institutioonal
compares both standard
nt portfolio an
nd applies thee Value-at-Rissk model to itt and comparees the potentiial loss factorr
investmen
among thee selected porrtfolios. Chap
pter 5 intends to study the lliquidity issuee using the PuureProperty inndex
and explo
ores the relatio
onship betweeen rebalancin
ng individual aassets within the REIT inddex and the
transaction/managemen
nt cost effect.. Chapter 6 taakes a step bacck and reflectts the thesis topic on
institution
nal investors. This chapter is interview based
b
and invvolves open ddiscussion reggarding topicss on
investmen
nt strategy. Ch
hapter 7 conccludes the anaalysis and disccussion.
12
Below is a diagram showing the structure of this thesis:
Figure 2-Thesis Structure
Examining global real asset allocation frameworks for institutional investors
Traditional Allocation
Framework
Innovative Allocation
Framework
Industry and Academic
Study
Correlation Analysis
Liquidity
Study
Allocation Framework
Open Discussion
Portfolio Analysis
Interview-based research
Analysis Review
Downside Risk Portfolio
Analysis
Allocation Strategy
Diversification Strategy
Investment Criteria
Liquidity Strategy
Value-at-Risk Analysis
Conclusions
13
Chapter 2
2.1
An Ov
verview of Real Asset forr Institutionaal Investors
Deffinition
Broadly speaking, real asset is defin
ned as “actuall, tangible assset (such as vvaluable antiquue or art, builldings,
mmodity, macchinery and equipment, staamp collectionn) as opposedd to financial assets (such aas
coins, com
bonds, deb
bentures, shares).” 2 Such assets usually
y have a utilitty for their ow
wners. They aare generally hheld
to protect investors from inflation. Some
S
argue th
hat “real assetts ought to bee valuable forr portfolio
diversificaation becausee widely diverrsified portfollios of stocks and bonds arre strongly neegatively
correlated
d with inflatio
on. Thus real assets
a
are imp
portant partlyy because theyy might provide a kind of
inflation insurance
i
for other assets in
i the portfoliio.”3
ut not all real estate types aare. In a 2011 PREA Quartterly article,
Real estatte is a type off real asset, bu
panels4 sp
pecifically disscussed the caategory of reaal assets and sstated that corre real estate bbelongs to reaal
asset, but opportunisticc properties su
uch as distresssed commerccial propertiess don’t providde a long stannding
e
to their holders. Rath
her, these prop
perties don’t uusually have a correlation to the normaal
hedging effect
investmen
nt portfolios. They providee high alpha liiquidity if invvested as oppoortunistic assets, but not beta
contributiion. Thereforee, real estate here
h refers to core real estaate with incom
me generatingg function.
Harvard Business
B
Scho
ool professor Kenneth A. Froot
F
noted thhat even thouggh real estate is a real asseet, the
propertiess of real estatee are not often
n compared with
w other reaal assets such as commodityy-linked equiities
or CPI-lin
nked bonds.
2.2
Rea
al Asset Classsification
a
can be categorized
c
as real assets? Investors’ unnderstanding of a real assett can be diffeerent.
So what assets
Here the term
t
“real” haas two distincct financial meanings: real return after aadjusting for iinflation; reall asset
that is tan
ngible with inttrinsic value. 5 We can see that physicallly tangible asssets such as railroads, tolll
roads, and
d real estate properties
p
can be real assetss. Commodityy, including aagricultural prroducts such as
corn, sugaar, rice, and bean
b
can also be real assetss. The futures commodity m
market has geenerally been
volatile accross time. Naatural resourcces are also a major categoory of real asssets. This inclludes energy
2
Definition
n from “businessd
dictionary.com””.
Kenneth A.
A Froot, “Hedging Portfolios wiith Real Assets”,, Journal of Porttfolio Managemeent, Summer 19995
4
Cate Pollo
oeys, Bryan Deck
ker, John N. Dally, J. J. Carlson, “Real Assets in an Institutional Portfolio: A Rooundtable Discusssion”,
PREA Quaarterly, Fall 2011
5
Hossein Kazemi,
K
Thomas Schneeweis, Ricchard Spugin, George
G
Martin, “R
Real Assets in Innstitutional Portffolios: the Role of
Commoditiees”. Alternative Investment Anaalytics Research, 2007
3
14
oal, and oil. There
T
are assetts that bear am
mbiguous deffinition of reaal
resources such as electtricity, gas, co
t
appear in
n between reaal asset and fin
nancial assetss. REITs, for example, are securitized aassets
assets as they
that can gain
g entry to th
he physical co
ontext of reall estate. Infrasstructure holdding companies are also traaded
in the stocck market butt are directly linked
l
to the infrastructuree assets such aas toll roads aand shipping
facilities. Therefore, th
here is a broad
d definition off real asset annd many assetts can be withhin this categoory.
o the charactter of the asseet than on induustry associatted with it.
The criterrion is more on
2.3
Assset Allocation
n Frameworrk Review
orks for instituutional investtors? Pensionn Real Estate
What are the traditionaal asset allocaation framewo
on conducted
d an annual su
urvey to show
w the real estatte investmentt activities in the context of
Associatio
public and
d private retirrement plans, foundations, and endowm
ments. The surrveyed instituutions are thosse
who invesst in real estatte either direcctly or indirecctly.
Asset Allo
ocation by Types
T
s
the plan sponsor assset allocation framework inn 2010. “The real estate hooldings (privaate
Figure 3 shows
and publicc) for the repo
orting group were
w $2.36 biillion, or 9.8%
% of the invesstor total asseets.” 6 Traditioonally,
equity and
d fix-income have taken up
p the majority
y of the inves tment. Real eestate has been in the rangee
between 5%
5 and 10% in
i the past decade. Stock has
h been in th e 50% range whereas bondd is 25%. Thee
allocation
n framework has
h been consstant in the paast decade witth minor channges among aasset classes.
Figure 3-PREA Surveyed
d 2010 Plan Sp
ponsor Asset Allocation
A
2010 Plan
P Sponsor Asset Allocattion
Real Esttate
Equityy
7%
Otherr Assets
9%
9
Bon
nds
25
5%
Caash
2%
%
Stocks
57%
Real
R Estate Eq
quity
Cash
Stocks
Bonnds
Other A
Assets
Source: PREA Investor Report, July 2011
6
PREA Inv
vestor Report, Ju
uly 2011
15
ocation by Strategies
Asset Allo
Figure 4 shows
s
the priv
vate real estatte investmentts by strategy for all plans in 2010. Evenn though coree
strategy taakes half of th
he total investtment volumn
n, it has been decreasing frrom around 770% since 20004
according
g to PREA. Op
pportunisitic strategy has been
b
increasinng from less tthan 15% to oover 25% in 22010.
The figurees have been stable from 2008
2
to 2010, which are alll post financiaal crisis numbbers. 25% of
institution
nal investors have
h
more thaan 10% allocaation to real eestate.
Figure 4-PREA Surveyed
d 2010 Plan Sp
ponsor Asset Allocation
A
(by sstrategy)
2010 Plan Sponsorr Asset Allocaation (by Straategy)
Opportunistic,
41,368, 29%
Core, 74
4,083,
52%
%
Value-Added,
V
26,985, 19%
Core
Vaalue-Added
Opportunistiic
Source: PREA
P
Investor Report,
R
July 2011
c
assets as they provide safe and heallthy income despite lowerr
It appearss that investorrs still favor core
returns. There
T
was a ph
hase when inv
vestors increaase their invesstment in oppportunistic asssets before thee
financial crisis
c
but hav
ve been stablizzing after the crisis. Investtors have sincce been cautioous about
opportuniistic investing
g because of th
he risk and th
he uncertaintyy.
Figure 5 shows
s
the com
mparison betw
ween investorrs plan size am
mong the threee private reall estate investtment
strategies..7 Larger plan
ns tend to dom
minate the opp
portunistic invvestment as ssuch investmeent requires certain
risk toleraance and largee capital scalee. Smaller plaans have a leaad on the coree investment aas there is a laarge
diversity in
i core markeet.
7
Excludes debt
d and investm
ments not readily
y allocable by strrategy.
16
Figure 5-D
Distribution of Private
P
Real Estate Investmen
nts by Strategyy—By Plan Sizze
2010 Distribution of Priivate Real Esttate Investmeents by
Strategy
y—By Plan Size
S
Total Investment Ammount
(in billion dollars)
70
60
50
40
30
20
10
0
Coree
Vallue-Added
Opportunistiic
>445bn <45bn
n
Source: PREA
P
Investor Report,
R
July 2011
2.4
Litterature Reviiew
In 1995, Harvard
H
Busin
ness School professor
p
Ken
nneth A. Froo t examined thhe hedging prroperties of reeal
assets for a series of diversified porttfolios. The reesearch foundd out that “lonng exposures to a number of
RB index, com
mmodity-link
ked equities, aand, particulaarly, broad reaal estate indexxesreal assetss-gold, the CR
provide reelatively weak
k hedges for broadly
b
diverrsified portfollios.” This finnding poses ann interesting
perspectiv
ve to the curreent thesis topiic as we targeet to find out oover the past 30 years, whhat role has real
estate perfformed in a ty
ypical investm
ment portfolio
o. What strateegies do instittutional invesstors use to
navigate the
t weights am
mong differen
nt real assets??
8
uddin Ansari,, Erasmus Un
nivesity Rotterrdam, studiedd the shippingg role in portffolio risk
Zia Mohiu
managem
ment in a reseaarch paper con
nducted in 20
006. Categorizzed as a real aasset, shippinng is an imporrtant
class for large
l
institutio
onal investorss in managing
g their portfollio risk. The ppaper examinned the risk annd
return trad
deoffs of ship
pping stocks by
b looking at it with regardd to a set of portfolio manaagement issuees and
by studyin
ng the industrry cycles. Thee paper conclu
uded that the systematic riisk factors of shipping secuurities
are dependent upon com
mpany speciffic factors and
d other macroo and micro ecconomic factors, which is
indicativee of how invesstment institu
utions should strategize thee asset allocattion to shippinng. 9
8
Kenneth A.
A Froot, “Hedging Portfolios wiith Real Assets-w
which real assetss can help protecct financial portffolios?” 1995
Zia Mohiu
uddin Ansari, “Porfolio Risk Maanagement in Shiipping Real Asseets and Shippingg Security Assetts”, Erasmus
University Rotterdam,
R
2006
6
9
17
In 2007, Alternative Investment Analytics studied the role of commodities in an institutional portfolio as
a real asset by examining the optimal asset allocation for a target return to several selected real assets
including TIPS, NCREIF, Bond, and Equity. The research found out that “real assets may contribute
substantially to traditional stock and bond portfolios.” And that “certain real assets, such as the BCI
commodity index may serve as a hedge against inflation risk.” 10
In a 2011 research by Mellon Capital Research, real asset is defined as those that “retain purchasing
power when paper currencies lose buying power. That is, real assets increase in nominal price at least as
fast as inflation.” The author concluded from the research that the real assets including physical real estate,
land, physical commodities, and TIPS have hedging and diversification roles in stock and bond heavy
portfolios but managing diversified physical real estate and land requires operational skill, which leads to
a liquidity concern in this thesis research that intends to take into account. 11
In a 2011 academic research by Technische Univsersitat Munchen, Rothballer and Kaserer studied the
infrastructure’s role in risk management and found out that infrastructure stocks on average exhibit
significantly lower market risk than other equities while there is large variation among different
infrastructure classes. The study concluded that infrastructure can be characterized as an ‘average
volatility, low beta’ business, implying that infrastructure firms have a similar level and an even higher
share of idiosyncratic risk compared with other equities. 12
In a 2012 research paper published by J.P. Morgan Asset Management, Joseph, Dessner, and Santiago,
together with the research team, explored the inflation issue in asset allocation strategy by examining
historic performance of different assets with respect to their effectiveness against inflation. The paper also
looked at the active and passive strategies in managing inflation risks to study what is a practical way to
react to inflation change in both downturn and growth periods. 13
More recently in April 2012, J.P. Morgan published a research on asset allocation framework for global
real assets that includes real estate, infrastructure, natural resources and transport. By examining such
10
Hossein Kazemi, Thomas Schneeweis, Richard Spurgin, George Martin, “Real Assets in Institutional Portfolios: the role of
Commodities.” 2007
11
Kenton K. Yee, “Keeping Real Assets Real”, Mellon Capital Research, 2011
12
Christoph Rothballer, Christoph Kaserer, “Is infrastructure really low risk? An empirical analysis of listed infrastructure firms”,
Technische Universitat Munchen, Germany, 2011
13
Maddi Dessner, Katherin Santiago, Joseph Simonian, “Keepin’ it real-Inflation risk as an asset allocation problem”, J.P.
Morgan Asset Management, 2012
18
factors as growth, inflation, return structure, risk profile, diversification, and the nature of asset classes,
the study boldly suggests that in the next decade or so, there is possibility that portfolio allocations to real
assets could increase to as much as 25%.14
In June, 2012, McKinsey & Company published a study on alternative investments and explores the
growth potential within the asset management community. The research explores the resurgent demand
for alternative investments by examining the alternatives allocation in the global institutional market. The
study also predicts that asset managers are expected to increase their alternative-like products by 5
percentage points in the next several years.15
14
Joseph K. Azelby, Michael C. Hudgins, “The Realization-A new world. A new normal. A tectonic shift.”, J.P. Morgan Asset
Management, 2012
15
Onur Erzan, Kurt MacAlpine, Nancy Szmolyan, “The mainstreaming of alternative investments-fueling the next wave of
growth in asset management”, McKinsey & Company, June 2012
19
Chapter 3
Correelation Analy
ysis
Ob
bjective
3.1
This chap
pter is structurred on a set off quantitative models to stuudy the correelations betweeen different aasset
returns. The
T models aree designed to test the hedg
ging potential of different aassets againstt stock and
inflation, the impact off financial crisis on diversiification, and the long term
m co-movemeent between
a
different assets.
Data Resource and Method
dology
3.2
In order to
o study the assset allocation
n, the author identifies
i
seveeral asset classses to constrruct typical
investmen
nt portfolios comprising
c
sto
ock and bond
d.

Real
R estate ind
dex (NCREIF Property Ind
dex, 1978 to 22009), to repreesent US reall estate annuaal
in
ndex 16

In
nfrastructure (A
( compositee index comprrised of S&P 500 Railroadd, S&P 500 U
Utility and S&P
Healthcare,
H
1975-2009), a running
r
averaage of the totaal returns of thhe three indexxes to represeent
in
nfrastructure annual
a
index. 17

Commodity
C
(S
S&P GSCI, 19
975-2009), fo
ormerly the G
Goldman Sachhs Commodityy Index, to
reepresent the overall
o
commo
odity price in
ndex.

Sttock (S&P 50
00 index, 1975-2009), to reepresent US sstock market aannual index..

Bond
B
(US 10-y
year Treasury
y Bond, 1975--2009), to reppresent US lonng-term Treasury bond annnual
in
ndex.

Risk-free
R
rate (USA
(
Total Return
R
T-Bill Index, 1975--2009), to reppresent the aveerage risk-freee rate
in
n the portfolio
o analysis.
Additionaal data:
In order to
o compare diffferent asset classes’
c
co-movement, the study also inncludes the foollowing addittional
data sets in
i the addition
nal analysis:
16
NCREIF Property Index started in 1978
Historic index
i
for healthccare only started in 1987. The thesis creates an aaverage of S&P 5500 Railroad andd S&P 500 Utiliity
represent the index from 1975-1986, averag
ge of all three ind
dexes total returnn to represent thhe composite inddex from 1987-20009.
17
20

Oil
O price (Wesst Texas Interm
mediate Oil Price,
P
1975-20009), to repreesent annual pprice index off oil.

CPI
C index (US
SA Consumer Price Index, 1975-2009), to represent ooverall consuumer price in tthe
US.
U

Farmland (NCREIF Farmlaand, 1992-200
09), to represeent farmland aannual index as a subcateggory
off real estate.

Timberland
T
(N
NCREIF Timb
berland, 1987-2009), to reppresent timbeerland annual index as a
su
ubcategory off real estate.
3.3
Meethodology
In this chaapter, the auth
hor constructss correlation analysis
a
to tesst out how different asset cclasses move in
relation to
o others. The models exam
mine how these assets link tto stock moveement, how thhey hedge agaainst
inflation, and what imp
pact the finan
ncial crisis hass on these asssets. The struccture of the m
models is baseed on
s
of time period and different
d
pairin
ng possibilitiees. Stock and bond are set as the base too be
different spans
compared
d with, then in
nflation is testted against several selectedd assets, and several addittional assets aare
tested as well.
w
3.4
Correlation An
nalysis
c
meaan and standaard deviation of
o several asssets. Real estaate and US long term Treasury
Figure 6 compares
bond sharre similar charracter as they
y both exhibit low risks. Onn the other haand, they havee relatively loow
returns as well. Infrastrructure has slightly higher mean than sttock with a coomparable rissk as stock.
Commodiity, in generall, has a high volatility.
v
Oil price, as a suubcategory off commodity, has an even
higher vollatility than commodity beecause of its risk
r associatedd with global market uncerrtainty.
21
Figure 6-Scatter Plot of Returns and Risks among Selected Assets
Scatter Plot Of Returns And Risks Among Selected Assets
16%
14%
Composite Infra
SP500Stk
WTI Oil
Annual Return
12%
NCREIF NPI
10%
USTBnd 10-yr
8%
SP GSCI
6%
Inflation
4%
2%
0%
0
0.1
0.2
0.3
0.4
0.5
Risk
Source: Global Financial Data, NCREIF, U.S. Bureau of Labor Statistics
Correlation Analysis
First, we compare stock and bond with commodity, infrastructure and real estate for the period from 1975
to 2009 to include complete economic cycles.18
Correlation for 1975-2009
S&P 500 Stock
US 10-year
Treasury Bond
S&P GSCI
Composite
Infra
NCREIF NPI
0.10
0.76
0.14
-0.18
0.24
-0.05
Both real estate and commodity have low correlation with stock. Infrastructure has a high correlation with
stock. Both real estate and commodity have negative correlations with long term Treasury bond.
Infrastructure also has a higher correlation with stock than real estate and commodity do.
Then we add in the data from 2010 to 2011 to see how the past two years affected the correlation.
Correlation for 1975-2011
18
The selection of time frame is based on the decision to include both the periodic peak and bottom points in the history of stock,
bond, commodity, infrastructure and real estate.
22
S&P 500 Stock
US 10-year
Treasury Bond
S&P GSCI
Composite
Infra
NCREIF NPI
0.11
0.75
0.13
-0.19
0.24
-0.04
The extension of data presents similar results. Both real estate and commodity have low correlation with
stock and negative correlations with long term Treasury bond. Infrastructure maintains a high correlation
with stock and bond.
Then we take out the data from 2008 to 2011 to look at the historic movement from 1975 to 2007.
Correlation for 1975-2007
S&P 500 Stock
US 10-year
Treasury Bond
S&P GSCI
Composite
Infra
NCREIF NPI
-0.14
0.72
0.06
-0.12
0.37
-0.23
Now something interesting happens. Commodity goes negatively correlated to stock.
NCREIF NPI moves even further less correlated to stock from 1975 to 2007 than from 1975 to 2009/2011,
and goes much more negatively correlated to Treasury bond. The impact of financial crisis helps bring
real estate and commodity down together with stock.
COMMODITY VS STOCK
Figure 7-Stock and Commodity Historic Index (1975-2011)
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Total Index 1974=100
Stock and Commodity Historic Index (1975-2011)
Year
SP500Stk
SP GSCI
Source: Global Financial Data
23
Figure 7 shows how stock index moves with commodity. Notice how stock fluctuates in the past 20 years
to several major cycles and how commodity has minor but more frequent fluctuations.
Figure 8-Stock and Commodity Historic Annual Return (1975-2011)
Stock and Commodity Historic Annual Return (1975-2011)
60%
Annual Return
40%
20%
0%
-20%
-40%
-60%
Year
SP500Stk
SP GSCI
Source: Global Financial Data
Co-movement:
From the correlation matrix for 1975-2009, commodity has a correlation of 0.10 with US stock.
From the correlation matrix for 1975-2011, commodity has a correlation of 0.11 with US stock.
However, if we take out the data during financial crisis from 2008 to 2009, we get a correlation of -0.14
between S&P 500 and S&P GSCI. This result indicates that historically, commodity is negatively
correlated with US stock market. But with the financial crisis, both the stock market and commodity price
moved down significantly and in the past two years, both asset classes move together.
24
REAL ESTATE VS STOCK
Figure 9-S&P 500 and NCREIF NPI Historic Index (1975-2011)
S&P 500 and NCREIF NPI Historic Index (1975-2011)
Total Index 1974=100
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
Year
SP500Stk
NCREIF NPI
Source: Global Financial Data
Figure 9 shows how stock index moves compared with real estate. Real estate has been growing steadily
before late 2007. It also took a hit slightly before the financial crisis.
Figure 10-S&P 500 and NCREIF NPI Historic Annual Return (1975-2011)
S&P 500 and NCREIF NPI Historic Annual Return (1975-2011)
50%
40%
Annual Return
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%
Year
SP500Stk
NCREIF NPI
Source: Global Financial Data
25
Co-movement:
From the correlation matrix for 1975-2009, real estate has a correlation of 0.14 with US stock.
From the correlation matrix for 1975-2011, real estate has a correlation of 0.13 with US stock.
If we take out the data during financial crisis from 2008 to 2011, we get a correlation of 0.06 between
S&P 500 and real estate. This result indicates that historically, real estate has low correlation with US
stock market. But with the financial crisis, both the stock market and real estate moved down in
somewhat close ties in the past two years. Different from the movement of commodity, real estate
exhibits some kind of lag.
INFRASTRUCTURE VS STOCK
Figure 11-S&P 500 and Composite Infrastructure Historic Index (1975-2011)
Total Index 1974=100
S&P 500 and Composite Infrastructure Historic Index (1975-2011)
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
Year
SP500Stk
Composite Infra
Source: Global Financial Data
Figure 11 shows how infrastructure moves compared with stock. Infrastructure took a smaller hit the late
1990s internet bubble than stock, and also has a smaller drop during the financial crisis and now bounces
back further than stock.
26
Figure 12-S&P 500 and Composite Infrastructure Historic Annual Return (1975-2011)
Annual Return
S&P 500 and Composite Infrastructure Historic Annual Return
(1975-2011)
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%
Year
SP500Stk
Composite Infra
Source: Global Financial Data
Co-movement:
From the correlation matrix for 1975-2009, infrastructure has a correlation of 0.76 with US stock.
From the correlation matrix for 1975-2011, infrastructure has a correlation of 0.75 with US stock.
If we take out the data during financial crisis from 2008 to 2011, we get a correlation of 0.72 between
S&P 500 and infrastructure. This indicates that even with the financial crisis starting in 2008, both
transportation and stock move very closely. Therefore, infrastructure is highly correlated with the US
stock market regardless of the financial crisis impact.
COMMODITY, REAL ESTATE AND INFRASTRUCTURE VS INFLATION
Then, we examine how several real assets react to CPI in order to understand their hedging role against
inflation. 19
Correlation for 1975-2011
Inflation
S&P GSCI
Composite Infra
NCREIF NPI
19
-0.01
0.22
0.40
NCREIF data started from 1978
27
Correlation for 1975-2009
Inflation
S&P GSCI
Composite Infra
NCREIF NPI
-0.02
0.22
0.42
Correlation for 1975-2007
Inflation
S&P GSCI
Composite Infra
NCREIF NPI
-0.03
0.25
0.36
Co-movement:
From the correlation matrix for 1975-2007, 1975-2009, 1975-2011, commodity has a negative correlation
of between -0.03 and -0.01 with US Inflation.
Infrastructure and real estate both have a relatively high correlation between 0.22 and 0.42, which
indicates that infrastructure and real estate may be good hedges against inflation.
Figure 13-Annual Returns of Selected Assets VS Inflation Movement
Annual Returns (1975-2011)
15.00%
Annual Return
40%
10.00%
20%
5.00%
0%
0.00%
-20%
-5.00%
-40%
-10.00%
-60%
-15.00%
Year
SP GSCI
NCREIF NPI
Composite Infra
Inflation
Source: Global Financial Data, NCREIF, U.S. Bureau of Labor Statistics
28
Annual Inflation
60%
Figure 13 show the annual return of commodity, infrastructure and real estate compared with inflation
adjusted for scale of movement. Commodity has been consistently volatile throughout the period.
Infrastructure appears less volatile than commodity, but still higher than real estate.
OIL VS INFLATION, STOCK AND COMMODITY
What about oil’s correlation with stock and inflation? We construct the same correlation analysis, below
is the result.
Correlation for 1975-2011
SP500Stk
SP GSCI
Inflation
WTI Oil
0.09
0.62
0.07
Correlation for 1975-2009
SP500Stk
SP GSCI
Inflation
WTI Oil
0.09
0.62
0.07
Correlation for 1975-2007
SP500Stk
SP GSCI
Inflation
WTI Oil
-0.13
0.61
0.19
From the correlation matrix for 1975-2007, oil has a correlation of -0.13 with US stock.
From the correlation matrix for 1975-2009, oil has a correlation of 0.09 with US stock.
From the correlation matrix for 1975-2011, oil has a correlation of 0.09 with US stock.
Oil price was impacted by the financial crisis and moved somewhat closer with the stock market,
although not enough to indicate any strong co-movement with stock.
From the correlation matrix for 1975-2007, 1975-2009, 1975-2011, oil always has a high correlation with
commodity, which indicates that oil, as a type of commodity, exhibits similar characteristic to general
commodity in terms of annual index fluctuation.
29
Interestingly, oil price has relatively lower correlation with inflation than do real estate and infrastructure,
indicating that oil may not be as good an inflation hedge as real estate and infrastructure. 20
REAL ESTATE, FARMLAND, TIMBERLAND VS STOCK
Then, we take the three sub asset classes within real estate to look at the performance of each individual
asset with stock. Quarterly return is used in place of annual return for the period between 1992 and 2012.
The correlation matrix is as below:
1Q1992-1Q2012
NCREIF
NPI
0.19
SP500Stk
NCREIF
Farmland
0.11
NCREIF
Timberland
0.02
Figure 14-Scatter Plot of Returns and Risks among Selected Assets (1989-2011)
Scatter Plot Of Returns And Risks Among Selected Assets
(1989-2011)
3.00%
NCREIF Farmland
2.50%
NCREIF
Timberland
NCREIF NPI
USTBnd 10-yr
Return
2.00%
SP500Stk
1.50%
1.00%
0.50%
0.00%
0
0.02
0.04
0.06
0.08
0.1
Risk
Source: Global Financial Data, NCREIF
Risk-return analysis (return is expressed as quarterly return)
Mean
Risk
20
SP500Stk
USTBnd
10-yr
NCREIF
NPI
NCREIF
Farmland
NCREIF
Timberland
2.37%
8.35%
1.79%
4.38%
2.00%
2.45%
2.77%
3.26%
2.51%
3.93%
Kenneth A. Froot, Hedging Portfolios with Real Assets-which real assets can help protect financial portfolios? 1995
30
From the Mean-variance graph, we can see that traditional real estate index (NCREIF NPI) has lower
average quarterly return than stock, and slightly higher average quarterly return than Treasury bond. The
quarterly risk is less than half of stock for real estate NPI. Both farmland and timberland have higher
returns than stock and less than half the risk of stock. Stock market is much more volatile in terms of
quarterly performance than the three real estate indices. Among the three real estate categories, farmland
and timberland have shown attractive return-variance prospect.
Figure 15- Quarterly Historic Index (1992-2011)
Total Iedex 4Q1991=100
Quarterly Historic Index (1992-2011)
1000
900
800
700
600
500
400
300
200
100
0
SP500Stk
NCREIF Farmland
NCREIF NPI
NCREIF Timberland
Source: Global Financial Data, NCREIF
From the quarterly index, we can see that real estate was more severely affected by the financial crisis
than farmland and timberland. Farmland was not as driven down as timberland during the financial crisis.
Farmland has been surging throughout the data period. However, no indication of industry background
was given at this stage of the thesis.
31
Figure 16-Quarterly Returns (1992-2011)
Quarterly Returns (1992-2011)
30%
25%
Quarterly Return
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
-25%
SP500Stk
NCREIF NPI
NCREIF Farmland
NCREIF Timberland
Source: Global Financial Data, NCREIF
If the numbers hold and we assume this 20-year period data somehow represents the market in general,
then by including real estate, especially farmland and timberland in a stock and bond portfolio will largely
reduce the portfolio risk. Farmland and timberland also offer competitive return prospect. However, the
data is only based on a period spanning 20 years, which may not cover an economic cycle that is of
appropriate scale to ensure the application of the result.
Conclusion
The correlation analysis among different assets provides some interesting results to look at the
diversification and hedging potential of different assets. Both commodity and real estate have low or
negative correlations with stock, but both were hit by the financial crisis in 2008, which increases their
correlation with stock. Infrastructure, on the other hand, has a very high correlation with stock and moves
alongside the stock during the financial crisis.
Both infrastructure and real estate have a notable correlation with inflation, indicating that these assets
can be held to at least partly hedge against inflation. Commodity, however, has a negative correlation
with inflation, which is interesting to note. Oil price has little correlation with stock and inflation, and has
a high correlation with commodity. As oil is also a type of commodity, it somehow exhibits a similar
character as commodity index.
32
Farmland and Timberland in the NCREIF index, interestingly, show a similar character to NCREIF NPI
except that the effect of financial crisis has limitation on these two assets. Both of these two assets have
comparable returns as stock, but less volatile than stock. They have great potential for those investors who
seek high return and comparable risk level as real estate. The data history for farmland and timberland is
relatively short, which may not cover the full spectrum of the market.
33
Chapter 4
4.1
Portfo
olio Analysis
Litterature Reviiew
P
Theo
ory was devellopment in thee 1950s throuugh 1970s. Thhe theory is developed to
Modern Portfolio
maximizee portfolio exp
pected return for a given am
mount of port
rtfolio risk by comparing aand adjusting tthe
portions of
o various assets in the porttfolio. Moderrn Portfolio T
Theory, althouugh has its lim
mitation in thee
financial industry,
i
has been widely recognized
r
ass a common ttool in the porrtfolio managgement fields.
MPT was first introducced by Harry Markowitz in
n a 1952 articcle later was inncluded in a bbook21.
kes the assum
mption that inv
vestors are risk averse. Givven different pportfolios witth the same
MPT mak
expected return,
r
investtors would alw
ways prefer th
he least risky one. As expeected return inncreases, inveestors
will have to take on ad
dditional risk. However, in real world, ddifferent invesstors have diff
fferent investm
ment
ways choose a portfolio w
with high returrn and
strategies.. But MPT asssumes that a rational invesstor would alw
low risk. In a portfolio
o, the combination of assetts can be diffeerent. There aare correlationns between anny
nvestors can hhold a portfollio that can taake advantagee of
two assetss which affectts the portfoliio variance. In
diversificaation by reducing overall portfolio
p
risk.. Markowitz aand Andrew B
Brennan havee developed suuch
theories along
a
the way.
ns including stock and bond are construccted in compaarison with a starting case that
Groups off composition
only inclu
udes stock and
d bond. The comparison
c
iss to look at hoow commoditty, infrastructuure and real eestate
impact thee overall variance given a certain targett return and hoow bringing ddifferent real estate indexees
affect the efficient fron
ntiers.
21
According
g to Wikipedia.
34
4.2
ata and Meth
hodology
Da
folio analysis uses
u a series of
o compositio
ons to represeent an investm
ment portfolioo and intends to
The portfo
compare different
d
roless certain real assets play. The
T author divvides the pairring to two sets: one with tthe
standard volatility
v
anallysis, using sttandard deviattion of each aasset class as the risk; the ssecond set usees the
downside risk in place of standard volatility
v
to teest out how doownside risk compares witth standard risk.
Figure 17-P
Portfolio Analy
ysis Data List
Asset
bucket
Real
Asset
NCREIF
F
Start
Year
1978
End
Y
Year
22009
Total Return
FTSE
1975
22009
Total Return
GFD
1975
22009
Total Return
GFD
1975
22009
Total Return
Total Return
GFD
GFD
1975
1975
22009
22009
A
Average Return
GFD
1975
22009
Data Catego
ory
Data Source
Notes
Provideer
Real Estate
Real Estate
y22
PureProperty
US
U NCREIF NPI
Total Return
REITs Index
Commoditty
S&P GSCI
Infrastructurre23
Stocks
T-Bond
00 Railroad, S&P
P 500
S&P 50
Utility and S&P Health
hcare
S&P 500
US 10--year Treasury Bond
B
T-Bill
USA Tottal Return T-Billl Index
Stocks &
Bonds
nts:
Constrain
1. The
T sum of all weights amo
ong different assets
a
within a portfolio eqquals 1.
=1
Where
W
n denottes the total nu
umber of asseets modeled iin the analysiss
A the weightss for asset claasses are non--negative.
2. All
3. Portfolio mean
n return is exp
pressed as thee mean of eachh asset multipplied by the w
weight of thatt
assset:
=
∗ ( )
4. Portfolio variaance is expressed as the weeight of each aasset multipliied by the weiight of the
co
omparing asset, multiplied
d by the covarriance betweeen these two aassets:
22
Here the author
a
uses FTSE NAREIT Equ
uity REIT index to
t represent PureeProperty with hhaircut modificaation as noted in the
preceding paragraphs.
23
Historic index
i
for healthccare only started in 1987. The thesis creates an aaverage of S&P 5500 Railroad andd S&P 500 Utiliity
represent the index from 1975-1986, averag
ge of all three ind
dexes total returnn to represent thhe composite inddex from 1987-20009.
35
=
,
Where n denotes the number of asset classes within this specific portfolio
,
=
Where
asset i, and
,
,
denotes the covariance between asset i and j,
,
denotes the standard deviation of
denotes the correlation coefficient between assets i and j’s annual returns.
Figure 18-List of Pairing Tests
Standard Volatility
Downside Risk
1
Stock, Bond
Stock, Bond
2
Stock, Bond, Real Estate
Stock, Bond, Real Estate
3
Stock, Bond, Commodity, Infrastructure
Stock, Bond, Commodity, Infrastructure
4
Stock, Bond, Commodity, Infrastructure,
Real Estate
Stock, Bond, Commodity, Infrastructure, Real
Estate
5
Stock, Bond, Commodity, Infrastructure,
Real Estate, PureProperty
Stock, Bond, Commodity, Infrastructure, Real
Estate, PureProperty
36
4.3
ysis
Porrtfolio Analy
d Volatility
Standard
First, we run
r the efficieent frontier fo
or standard vo
olatility.
Below is the
t result of portfolio
p
com
mpositions at different
d
meann targets:
Figure 19-M
Markowitz Porrtfolio Comparrison24
MEAN-VARIANCE SP
PACE
E[r]
12%
7%
0%
5%
5
S+B
B+R
10%
15%
OLATILITY
VO
S+B+C+I
200%
S+B
B+C+I+R
Source: Global Financiaal Data, NCREIF
F
g to the efficient frontier diagram,
d
we can see that reeal estate has a better diverrsification rolee than
According
infrastructture and commodity. With
h the same target return, thee stock/bond portfolio withh real estate hhas
lower porrtfolio volatiliity than that with
w commodiity and infrastructure. The portfolio witth stock, bondd and
real estatee will still ben
nefit from including comm
modity and inffrastructure frrom a diversiffication
perspectiv
ve, especially when target return
r
is high
h. This is becaause real estatte has a relatiive low returnn but
also a low
w volatility meeasured by staandard deviattion. In contraast, infrastruccture has a higgh mean returrn,
which, wh
hen added to the
t portfolio, increases thee target returnn.
24
S: stock, B: bond, C: com
mmodity, I: infrastructure, R: reaal estate.
37
Figure 20 shows area chart
c
of efficieent frontier att different tarrget returns.
Figure 20
0-Asset Comp
position of thee Efficient Frrontier with S
Stock, Bond, Commodity, Infrastructurre and
Real Estatte
Source: Glo
obal Financial Daata, NCREIF
n see that with
h the introducction of real eestate, commoodity and
From the above compaarison, we can
Stock is stablle
infrastructture, for a cerrtain mean tarrget, real estate and infrasttructure have major roles. S
throughou
ut the differen
nt target return
n points. Treaasury bond haas also a stablle role until taarget return
increases close to the highest
h
possib
ble for the porrtfolio. Comm
modity contribbutes relatively little to thee
mposition. Wh
hy is it so?
optimum portfolio com
k back at the mean-volatilit
m
ty matrix for these assets:
Let’s look
Mean
Volatility
SP
P500Stk
0.132
0.175
USTBnd
U
10-yr
0.089
0.108
SP
P GSCI
0.094
0.236
mposite
Com
Infra
0.139
0.178
NC
CREI
F NPI
00.091
00.083
ghest volatilitty of 0.236 wiith the lowestt average meaan among the five assets. T
To
Commodiity has the hig
minimize the portfolio variance with
h a target porrtfolio return, commodity sstarts with som
me weights ass the
r
is fixed
d low from th
he start, but ass the expectedd return increases, infrastruucture starts tto
expected return
take away
y weights.
But what about correlaation?
38
Correlation for 1975-2009
S&P 500 Stock
US 10-year
Treasury Bond
S&P GSCI
Composite
Infra
NCREIF NPI
0.10
0.76
0.14
-0.18
0.24
-0.05
Commodity has a relatively low correlation with stock and a negative correlation with bond. This enables
commodity to take at least some weights in the portfolio. But so does real estate. We look at the
composition of all five assets in Figure 20, and we can see that real estate overshadows commodity in the
optimum portfolio composition from the start. Why? From the correlation matrix, real estate has a similar
correlation with stock and bond as commodity does, the average mean is also similar to commodity, but
has a much lower volatility than commodity does. Therefore, to minimize the portfolio variance with a
target mean, real estate overtakes commodity in that with the same contribution to mean, real estate has a
lower volatility to contribute to the portfolio. With the inclusion of infrastructure, the high return and
similar volatility as stock provides a very plausible alternative in an investment portfolio.
PureProperty
At last, we include PureProperty index in the overall portfolio analysis. Since PureProperty has only
around 10 years of data, the author compares it with REITs index as PureProperty is the stock market's
valuation of the REITs' properties as indicated by REIT equity share prices.
Figure 21-PureProperty, REIT and NPI Historic Index (1999-2011)
Annual Index (1999=1000)
PureProperty, REIT and NPI Historic Index (1999-2011)
4500
4000
3500
3000
2500
2000
1500
1000
500
0
PureProp
EqREIT Indx
NPI
Source: NCREIF, FTSE, NAREIT
39
We construct the correlation among the three indexes from 1999 to 2009:
PureProperty
NPI
18.88%
REITs
99.18%
Figure 22-PureProperty, REIT and NPI Annual Returns (1999-2011)
PureProperty, REIT and NPI Annual Returns (1999-2011)
50%
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%
PureProp
NPI
EqREIT Indx
Source: NCREIF, FTSE, NAREIT
And the mean-volatility matrix from 1999-2009:
Mean
Volatility
PureProperty
9.76%
11.17%
NPI
9.53%
11.05%
REITs
14.24%
22.42%
As PureProperty has an almost perfect correlation with REITs (as it is derived from original REITs index),
the model uses REITs data from 1975-2009 to represent PureProperty and made the following haircut:
Mean: PureProperty has a risk premium at 1/2 the REITs risk premium
Volatility: PureProperty has ½ of the volatility of REITs
Correlation: we use the same correlation of REITs to other assets as the correlation of PureProperty to
other assets as PureProperty has an almost perfect 100% correlation with REITs.
Below is the area chart of efficient frontier at different target returns including both NCREIF NPI and
PureProperty.
40
Source: Glo
obal Financial Daata, NCREIF
Figure 23-M
Markowitz Porrtfolio Comparrison Including
g PurePropertyy25
MEAN-VA
ARIANCE SPACE
E[r]
12%
7%
5%
5
0%
S+B+R
10%
VOLATILITY
V
S+B+C+II
15%
S+B+C
C+I+R
200%
S
S+B+C+I+R+P
P
Then the mean-return
m
matrix
m
is as fo
ollows:
SP
P500Stk
25
USTBnd
U
10-yr
SP
P GSCI
mposite
Com
Infra
S: stock, B: bond, C: com
mmodity, I: infrastructure, R: reaal estate, P: PureP
Property.
41
NC
CREI
F NPI
PureProp
erty
Mean
Volatility
0.132
0.175
0.089
0.108
0.094
0.236
0.139
0.178
0.091
0.083
0.117
0.091
We can see from the mean-return matrix, PureProperty has an average return between real estate and
infrastructure, while the volatility is on par with real estate and Treasury bond. By including PureProperty
in the overall portfolio, it reduces the overall variance while maintaining a healthy return target. That
explains why PureProperty dominates the composition chart throughout.
However, because our assumption is based on the high correlation between PureProperty and REITs with
limited data history (10 years versus the 34 years the model uses), there is limitation in how much the
result can be applicable.
As PureProperty eliminates the leverage effect that REITs usually depend on, the returns on PureProperty
appears different than those on REITs. However, REITs returns are still higher than NCREIF NPI index.
The financial crisis forced REITs to recapitalize at the downturn of the market as REITs had to issue more
diluting stock shares to repay the debt they owe before the financial crisis. This repayment did not affect
as much on the properties assets as the REITs themselves, but they did hurt the return on equity.26
Downside Risk
In this analysis, downside risk calculation is as follows 27

Semi-deviation defined by
1
( )=(
( − [ ]) 1
=
1 ≤ [ ]
0
[ ]
Where 1

[ ]
[ ]
)
is an indicator function, i.e.
Below target semi-deviation for target t defined by
( , )=(
( − ) 1
)
Downside correlations are calculated as:
26
27
David Geltner, Professor of Real Estate Finance, Center for Real Estate, MIT, 2012
Definition from Wikipedia
42
Corr (A, B) downside , where A represents the series of difference between annual return that is below overall
average return and the average return, while where annual return is above average return, this difference
is expressed as zero. The same rule applies to asset B.
First, we run the efficient frontier and market portfolio for downside risk.
Below is the result for portfolio compositions at different mean targets with standard risk on the left and
downside risk on the right:
Figure 24- Downside Markowitz Portfolio Comparison
MEAN-VARIANCE SPACE
E[r]
12%
7%
0%
5%
S+B+R
10%
VOLATILITY
S+B+C+I
15%
20%
S+B+C+I+R
Source:Global Financial Data, NCREIF
On the downside, the portfolio including commodity and infrastructure perform slightly better than the
one including real estate. The inclusion of real estate on top of commodity and infrastructure does not
change much of the Markowitz portfolio volatility.
Let’s look back at the mean-semi-volatility matrix for these assets:
Mean
Standard Deviation
Semi Deviation
SP500Stk
0.132
0.175
0.200
USTBnd 10yr
0.089
0.108
0.090
SP
GSCI
0.094
0.236
0.282
43
Composite
Infra
0.139
0.178
0.178
NCREI
F NPI
0.091
0.083
0.083
PureProp
erty
0.117
0.091
0.105
n in the semi deviation,
d
Treeasury bond has
h a semi devviation lower than standardd deviation, w
which
As shown
increases its weights in
n the portfolio
o composition
n for all that inncludes Treassury bond.
h a semi deviation higheer than standarrd deviation, its weights arre reduced in all compositee
As stock has
portfolioss.
4.4
Conclusions
The portfo
folio analysis results
r
shown
n in this chaptter indicates tthat in a mixeed portfolio, ccertain real assets
outperform
m others in th
he efficient fro
ontier, while in the Sharpee-Markowitz pportfolio, certtain assets losse
power agaainst tradition
nal stock and bond.
b
Diversificcation
NCREIF NPI
N index red
duces the porttfolio volatiliity when addeed to the tradiitional stock aand bond porttfolio
in the effiicient frontierr while maintaaining a healthy portfolio rreturn.28 Wheen comparing diversificatioon
roles, reall estate perforrms better thaan the combin
nation of comm
modity and innfrastructure in terms of reeturnover-risk ratio. When real
r estate is combined
c
witth commodityy and infrastruucture in a stoock-and-bondd
ver-risk ratio is
i slightly imp
proved over tthe portfolio tthat just incluudes real estatte,
portfolio, the return-ov
ver, when com
mparing a porrtfolio that in cludes stock, bond and reaal estate with a
stock and bond. Howev
t includes stock, bond, commodity
c
an
nd infrastructture, the form
mer one has a hhigher returnportfolio that
over-risk ratio than thee latter one. Adding
A
commo
odity and infrrastructure in the Sharpe pportfolio has
limited diversification effect.
mber of assetss in the portfo
olio, the weigh
ght of stock gooes down in thhe Sharpe
As we inccrease the num
portfolio. Bond, on thee other hand, has
h a major ro
ole in this porrtfolio in all tthe compositioons as we inccrease
n the portfolio
o. When introduced to the pportfolio mixx, PurePropertty shows som
me
the numbeer of assets in
interesting
g potential. In
n the Sharpe portfolio
p
and the Markowittz model, PurreProperty haas had consistent
major rolees. The high return
r
and com
mpatible risk with NCREI F NPI enablees PureProperrty to stay at a
prominentt portion in th
he optimizatio
on model.
urn Strategy
Risk-retu
28
A reducttion of around 10% return.
44
Because infrastructure has high average returns with a comparable risk level with stock, it does provide
return potential for investors who are more risk tolerant. When investment managers set up relatively high
target returns, infrastructure’s role in a mixed portfolio improves. Commodity, interestingly, has little
weight in all the portfolio compositions and all the downside risk portfolio compositions. This is mainly
due to the high volatility of commodity and relatively low average return. However, the high volatility of
commodity also attracts hedge funds and high-frequency traders who are more risk tolerant than
traditional institutional investors who are more long-term players.
Real Asset Roles
Both infrastructure and real estate have major roles in the efficient frontier. It depends on the investor’s
risk aversion and specialty to decide on the portfolio structure. Real estate index dominates infrastructure
in the Sharpe-Markowitz portfolio. Within real estate, PureProperty is slightly more dominant than
private real estate in that it offers similar risk and higher return, which explains the high weights in the
Sharpe-Markowitz portfolio in the standard, downside risk model, and the ones with haircuts regarding
liquidity. When PureProperty and NPI are put together in a portfolio of assets, both of them have notable
weights. Commodity has little weight in the efficient frontiers in both standard and downside risk models.
The high volatility of commodity attracts short-term investors, but may not take a major role in the longterm investor’s portfolio.
45
Chapter 5
5.1
Value at Risk Ana
alysis
Deffinition
V
at Risk??
What is Value
It is a meaasure of poten
ntial loss of a specific valu
ue on a defineed time periodd and a given confidence
interval. VaR
V focuses on
o the potentiial losses for investors
i
to eestimate the pprobability of a certain
percentilee confidence level.
l
For exaample, a 1 milllion VaR at 55% at one dayy level meanss there is a 5%
%
chance thaat there is mo
ore than 1 milllion of loss an
nd there is a 995% chance tthere will be 1 million gainn in
any one day.
d
o a typical Value-at-Risk
V
are:
Thereforee, the factors of
1. Sp
pecified amou
unt of loss vaalue
2. Defined
D
time period
p
over which
w
the risk is assessed
3. A confidence interval
i
R is most often
o
adopted
d by commercial and investtment banks iin recent decaades to identiffy the
Value at Risk
potential loss
l
of their trraded portfoliios over a speecific period. Companies aare tested to aassess their losss
potential in
i order to ev
valuate their ability
a
to recov
ver without riisking the firm
m at large.
5.2
Data and Methodology
In order to
o examine thee VaR for ourr optimization
n portfolio, thhe author usess the data from
m previous
chapters that
t was origin
nally designeed to test out the
t optimizatiion model. Siix asset classees are used in here
for the composition of an investmen
nt portfolio. Risk-free
R
rate is calculated using the aveerage return oof Tx.
Bill Index
46
Figure 25-Data List for VaR Test
NCREIF
Start
Year
1978
End
Year
2009
Total Return
FTSE
1975
2009
Total Return
Total Return
GFD
1975
2009
GFD
1975
2009
Total Return
Total Return
GFD
GFD
1975
1975
2009
2009
Average Return
GFD
1975
2009
Data Category
Data Source
Notes
Provider
Real Estate
Real Estate
PureProperty29
Commodity
US NCREIF NPI
Total Return
REITs Index
S&P GSCI
S&P 500 Railroad, S&P
500 Utility and S&P
Healthcare
S&P 500
US 10-year Treasury Bond
USA Total Return T-Bill
Index
Infrastructure30
Stock
Bond
T-Bill
Below is a summary of the return and risk-premium of listed assets:
Risk-free rate is the average of government T-Bill annual returns from 1975 to 2009
= 5.71%
Market beta is defined as the difference between asset risk premium divided by the market risk premium.
=
[ ]
[
−
]−
Here we assume a market risk premium of 6%, that is
Asset E[r]
Asset Risk Premium
Market Beta
[ ]
= 6%
S&P 500
LTBond
Composite
Infra
Commodity
PureProperty
NPI
13.2%
7.5%
1.25
8.9%
3.2%
0.54
9.4%
3.7%
0.62
13.9%
8.2%
1.37
10.5%
4.8%
0.80
9.1%
3.4%
0.56
In order to create a simulation of the portfolio return, we take the Sharpe-Markowitz portfolio weights
from the portfolio analysis as the expected weights for each asset class within the portfolio. Then we
have:
29
Here the author uses FTSE NAREIT Equity REIT index to represent PureProperty with haircut modification as noted in the
preceding paragraphs.
30
Historic index for healthcare only started in 1987. The thesis creates an average of S&P 500 Railroad and S&P 500 Utility
represent the index from 1975-1986, average of all three indexes total return to represent the composite index from 1987-2009.
47
=
∗ ( )
Now we run another efficient frontier analysis and market portfolio with some haircuts to each asset to
net out the transaction cost effect. 31 Below are the market portfolio weights
SP500Stk
9%
USTBnd
10-yr
27%
SP GSCI
3%
Composite
Infrastructure
0%
PureProp
37%
NCREIF NPI
24%
In order to create a random history of the aggregate market value, we introduce the following variables:
1. Risk-free interest rate
The assumption is that the portfolio value will increase at a fixed annual interest rate at 3%.
2. Market risk premium
The assumption that the market value increases at a fixed risk premium rate on top of the risk-free
interest rate at 6%.
3. Random volatility
This is a normal (Gaussian) distribution. By introducing this random variable, we add in the
possibility of increasing or decreasing at a random percentage (between 0% and 100%) of the
assigned volatility of 15%. Volatility is realized in each period, so that this risk outcome
accumulates in the history of market value levels.
4. Auto Regression
We also introduce an Auto Regression factor at 0.2 upon previous year’s return to reflect the
lagging effect. This in turn, will also affect the annual volatility.
5. Circles
We introduce another deterministic factor as circles of 10 years to reflect the circular effect of
asset performance.
6. Black-swan effect
31
This composition is slightly different from the Sharpe-Markowitz weights derived from the previous chapter as certain haircuts
are applied to each asset to net out the transaction cost. 100 bps for real estate, 50 bps for PureProperty, 20 bps for stock, bond,
infrastructure, and commodity.
48
Lastly,
L
a Black
k-swan effect is added to factor
fa
in the faat-tail of certaain asset perfoormance. Thiis is
allso a random generation fo
or the assigned period for tthe purpose of simulation.
Then we run
r a 50-year simulation of the single assset factoringg in the differeent combinatiions of the abbove
variables. The starting value of the asset
a
is fixed at 1 $. Below
w is the result..
Figure 26-One Random History
H
("Draw
wing") of the Aggregate
A
Markket Value Histoory
Mkt Asset Value Level / Init $ Investment
100
One Rando
om History ("D
Drawing") of th
he Aggregate
e Market Value History
10
1
0
5
10
15
5
20
25
30
Risk
kless Asset
Mkt Trrend
(rf+RPM)
Years
Mkt Inclu Blk Swan
n
MktNo
oCycle
35
40
45
50
MktIncluC
Cycle
n see from thee simulation that
t both risklless asset andd market trendd assets increaase at a fixed trend
As we can
whereas random
r
factorrs impact the asset perform
mance by eitheer lagging, creeating circless or creating
inertias. Since
S
this is on
nly a random
m generation, no
n specific hiistoric data is yet applied too the model. IIn the
next part, the thesis will explore thee actual assetss performancee under the M
Monte Carlo siimulation.
5.3
Porrtfolio Simullation
f
in the assets
a
we selected as previo
ously introducced. There arre six assets too composite tthe
Now we factor
portfolio. First, we takee a look at thee individual asset’s
a
perform
mance using tthe Monte Caarlo simulatioon.
49
Below is a list of factors we take into account:
Differential Trend
Idiosyncratic
Volatility
Beta with Market
AR Parameter
S&P 500
LTBond
Composite
Infra
Commodity
PureProperty
NPI
1.51%
-2.79%
-2.30%
2.19%
-1.18%
-2.61%
10.00%
1.25
0.30
5.00%
0.54
0.00
15.00%
0.62
0.20
10.00%
1.37
0.20
10.00%
0.80
0.20
10.00%
0.56
0.20
Differential Trend is a deterministic trend component apart from the "Market" trend determined on the
preceding sheet. Normally in equilibrium per the CAPM this would equal the Beta of the asset class with
respect to the market minus 1.
We make the assumption of the idiosyncratic volatility for stock, commodity, PureProperty and NCREIF
NPI 10%, infrastructure’s idiosyncratic volatility at 15% and T-bond’s idiosyncratic volatility at 5%.
Market beta of each asset is calculated as
=
[ ]
[
−
]−
We also assign the Auto Regression factor of 0.3 to stock, 0 to T-bond, and 0.2 to infrastructure,
commodity, PureProperty and NPI. This will inject auto regression into the asset class returns. The auto
regression will induce a lag or smoothing in the asset class.
Then, we create a Monte Carlo simulation of 50 years’ price index for each asset and the market. The
indexes all start at 1.
50
Figure 27-One Random History ("Drawing") of the Asset Classes' Histories
One Random History ("Drawing") of the Asset Classes' Histories
Asset Classes Value Levesl / Init $ Value
1,000
100
10
1
0
0
5
10
Riskless Asset
Commodity
15
20
S&P 500
Mkt
25
30
Years LTBond
PP2
35
40
45
50
Infra
NPI
The 50-year simulation shows that since we factor in the market beta using CAPM, there is certain degree
of co-movement with the market. The individual assets also somehow drift away from the riskless asset.
Economic cycles are factored in to reflect the ups and downs of certain period within an assumed cycle.
Below is the scatter plot for the returns of four asset classes versus market returns:
Figure 28-Selected Asset Simulation Run Scatterplot
51
Commodity Simulation Run Scatterplot
60%
80%
50%
60%
40%
40%
Market Returns
Market Returns
Infrastructure Simulation Run Scatterplot
30%
20%
10%
-20%
-10%
0%
-40%
0%
-40%
20%
0%
20%
40%
60%
-60%
-30%
Infrastructure Returns
-80%
Commodity Returns
60%
25%
50%
40%
Market Returns
20%
15%
10%
5%
0%
30%
20%
10%
0%
-20% -10% 0%
-40%
0%
20%
40%
60%
NCREIF NPI Simulation Run Scatterplot
30%
-5%
40%
Simulation Run Scatterplot
Linear (Simulation Run Scatterplot)
Source: Global Financial Data, FTSE
PureProperty Simulation Run Scatterplot
Market Returns
20%
-20%
Source: Global Financial Data
-20%
0%
-20%
-40%
Simulation Run Scatterplot
Linear (Simulation Run Scatterplot)
-40%
-20%
60%
20%
40%
60%
-20%
-10%
-30%
-15%
PureProperty Returns
-40%
NPI Returns
Simulation Run Scatterplot
Linear (Simulation Run Scatterplot)
Source: Global Financial Data, NCREIF
Simulation Run Scatterplot
Linear (Simulation Run Scatterplot)
Source: Global Financial Data, FTSE
At last, we construct 5000 simulations of portfolio returns and indexes using the following assumption:
= 5.71%
Riskless rate from T-bond average return:
Market beta is calculated as:
=
[ ]
[ ]
Here we assume a market risk premium of 6%, that is
Asset E[r]
Asset Risk Premium
Market Beta
[ ]
= 6%
S&P 500
LTBond
Infra
Commodity
PureProperty
NPI
13.2%
7.5%
1.25
8.9%
3.2%
0.54
9.4%
3.7%
0.62
13.9%
8.2%
1.37
10.5%
4.8%
0.80
9.1%
3.4%
0.56
52
We take the Sharpe-Markowitz portfolio weights from the portfolio analysis as the expected weights for
each asset class within the portfolio. Then we have:
=
∗ ( )
Where
is listed below:
SP500Stk
9%
USTBnd
10-yr
27%
Composite
Infra
0%
SP GSCI
3%
PureProp
37%
NCREIF NPI
24%
Now we set the maximum loss of the portfolio to the minimum return in all the 50-year simulations.
Portfolio value is calculated as
portfolio value in year t-1,
=
∗ (1 + ) where
is the portfolio value in year t,
is the annual return in year t. Here we assume
year b. Therefore, year 5 gain is expressed as
,
,
=
,
,
is the year a return on
− 1.
Next, we run 5000 simulations of the maximum loss, year 5 gain, portfolio mean, and volatility.
Below is a histogram summary of one-year maximum loss.
Figure 29-Histogram of Simulated Maximum Loss within Any One Year
Histogram Simulated Max Loss Any 1 yr
300
Frequency (out of 2000)
250
200
150
100
50
0
-46%
-37%
-29%
-20%
Source: Global Financial Data, FTSE, NCREIF
53
-12%
-3%
is the
s
of portfolio gain in year five.
Below is a histogram summary
Figure 30-H
Histogram of Simulated
S
5-yeear Gain
Histo
ogram Simulated Portfolio Yr 5 Gain
n
30
00
Frequency (out of 2000)
25
50
20
00
15
50
10
00
50
5
0
-16%
-6%
4%
14%
23%
33%
Below is the
t cumulativ
ve distribution
n graph
Figure 31-Cumulative Diistribution Graaph
100
0%
90
0%
Probability
80
0%
70
0%
60
0%
50
0%
40
0%
30
0%
20
0%
10
0%
0%
0
-50%
-40%
%
-30%
-20%
%
-10%
0%
%
10%
2
20%
30%
40%
Portf Returrn %
MaxLossAny1yr Distn
D
Yr5 Gain/yr Mean
n
MaxLossAny1yr
M
M
Mean
1-yr
1 loss VaR at 5%
%
Y
Yr5 Gain/yr Distn
5--yr gain VaR at 5%
%
Source: Global
G
Financial Data, FTSE, NC
CREIF
The averaage maximum
m 1-year loss is
i at 21% of to
otal portfolio value. A 5%
% Value at Rissk in any one year
is around 32% of total portfolio valu
ue. That is theere is 5% probbability that tthe portfolio w
will lose more
than 32% of total valuee in one year.
54
i 2% loss of total
t
portfolioo value. That is there is 5%
% probability that
A 5% Vallue at Risk in 5-year gain is
the portfo
olio will loss 2%
2 in year fiv
ve.
5.4
Downside VaR
r
the asset weights with
w the minim
mum-variancee Downside pportfolio weigghts. The weigghts
Now we replace
are the ressults from Ch
hapter 3, Portffolio Analysiss. These are thhe haircut forr downside m
models.
d
USTBnd
10-yrr
45.6%
%
SP500Sttk
11.0%
%
SP GSC
CI
0.79%
%
Composite
Infrastruccture
4.65%
PureP
Prop
29 .4%
NCRE
EIF NPI
8.51%
Then we apply
a
the weiights to the Monte
M
Carlo Siimulation moodel and below
w are the resuults:
Histogram
m summary off one-year maaximum loss:
Figure 32-H
Histogram of Simulated
S
Max
ximum Loss wiithin Any One Year (Downsiide Risk)
Histogra
am Simulate
ed Max Loss
s Any 1 yr
350
Frequency (out of 2000)
300
250
200
150
100
50
0
-47%
-38%
-30%
-21%
-12
2%
Source: Global
G
Financial Data, FTSE, NC
CREIF
m summary off portfolio gaiin in year fivee.
Histogram
Figure 33-H
Histogram of Simulated
S
5-yeear Gain
55
-4%
%
Histog
gram Simula
ated Portfolio
o Yr 5 Gain
350
0
Frequency (out of 2000)
300
0
250
0
200
0
150
0
100
0
50
0
0
-18%
-7%
3%
%
13%
23%
33%
Below is the
t cumulativ
ve distribution
n graph
Figure 34-Cumulative Diistribution Graaph (Downside Risk)
Cumulattive Distn Fcn: ex po
ost Max1yrrLoss &
Yr5 Gain (across simullation runs)
100%
%
90%
%
80%
%
Probability
70%
%
60%
%
50%
%
40%
%
30%
%
20%
%
10%
%
0%
%
-60%
-40%
-
-2
20%
MaxLossAny1yr
M
Distn
Yr5
Y Gain/yr Distn
1-yr
1 loss VaR at 5%
%
0%
0
20
0%
40%
%
Portf Retu
urn %
MaxLossAn
ny1yr Mean
Yr5 Gain/yrr Mean
5-yr gain V
VaR at 5%
Source: Global
G
Financial Data, FTSE, NC
CREIF
5.5
Tra
aditional Porrtfolio Modell
r
the asset weights with
w the traditiional portfolio model weigghts from PRE
EA’s survey. Here
Now we replace
we keep real
r estate a to
otal of 10% with
w PureProperty at 6% annd NCREIF N
NPI at 4%. Wee assign anothher %
56
to other real assets including 4% to commodity and 4% to infrastructure. 32 Then we assign 25% to bond
as is shown in the sponsor survey by PREA and the rest 57% to stock. Now we run the Monte Carlo
simulation again with the following weights:
USTBnd
10-yr
25%
SP500Stk
57%
SP GSCI
4%
Composite
Infrastructure
4%
PureProp
6%
NCREIF NPI
4%
Below are the results:
Histogram summary of one-year maximum loss
Figure 35-Histogram of Simulated Maximum Loss within Any One Year (Traditional Portfolio)
Histogram Simulated Max Loss Any 1 yr
350
Frequency (out of 2000)
300
250
200
150
100
50
0
-70%
-56%
-43%
-29%
-15%
-2%
Source: Global Financial Data, FTSE, NCREIF
Histogram summary of portfolio gain in year five.
Figure 36-Histogram of Simulated 5-year Gain (Traditional Portfolio)
32
Here commodity and infrastructure are set at the same percentage for simplicity.
57
Histogram Simulated Portfolio Yr 5 Gain
350
Frequency (out of 2000)
300
250
200
150
100
50
0
-35%
-19%
-2%
15%
31%
48%
Cumulative distribution graph
Figure 37-Cumulative Distribution Graph (Traditional Portfolio)
Cumulative Distn Fcn: ex post Max1yrLoss &
Yr5 Gain (across simulation runs)
100%
90%
80%
Probability
70%
60%
50%
40%
30%
20%
10%
0%
-60%
-40%
-20%
0%
20%
Portf Return %
MaxLossAny1yr Distn
Yr5 Gain/yr Distn
1-yr loss VaR at 5%
MaxLossAny1yr Mean
Yr5 Gain/yr Mean
5-yr gain VaR at 5%
Source: Global Financial Data, FTSE, NCREIF
58
40%
on, the 5% Vaalue-at-Risk ffor the Markoowitz optimizaation portfolio is
Based on the Monte Caarlo simulatio
1% of loss, the 5% Value-aat-Risk for thee downside opptimization pportfolio is aroound 30% of loss,
around 31
and the 5%
% Value-at-R
Risk for the traaditional porttfolio is arounnd 45% of losss.
kowitz optimization portfollio has a much less magnittude of loss att the 5% perccentile than thhe
The Mark
traditional portfolio wiithin one yearr.33 The average mean of looss for Markoowitz is arounnd 21% withinn any
m
of loss for
f downside portfolio withhin any one yyear is aroundd 20%, and thhe
one year, the average mean
average mean
m
of loss for
fo traditional portfolio is at
a around 30%
%.
a bond with
h the increasee in real estatee within a porrtfolio accordding to
The decreease in stock and
optimizatiion portfolio reduces the magnitude
m
of potential
p
losss within the anny one-year pperiod accordiing to
the Value-at-Risk mod
del. This migh
ht indicate thee potential of increasing real estate in a mixed portfolio to
protect ag
gainst losses.
5.6
Conclusions
Based on the VaR mod
del, the Sharp
per-Markowitzz portfolio shhows less risk at a target loss within onee year
weights on reall estate. The
than the trraditional porrtfolio with heeavy weights on stock andd bond, low w
magnitudee of the 5-yeaar gain for traaditional portffolio structuree at 5% percenntile probabillity is comparrable
with that in
i the Sharpe-Markowitz portfolio.
p
Thee average 5-yeear gain at 5%
% percentile pprobability forr
traditional portfolio strructure is also
o comparable with the Sharrpe-Markowitz portfolio.
It’s imporrtant to noticee that there is limitation in Value at Riskk. VaR makess assumptionss about returnn
distributio
ons and we arre assuming th
he historical return
r
distribuution can reprresent the disttribution of
returns looking forward
d. This could be an assump
ption that hass limitation inn its applicabiility. The effeect in
the correlaation error is magnified in the Monte Carlo simulatioons, which prroves anotherr potential erroor in
34
the VaR model.
m
The simplicity of VaR model is also anotherr risk becausee the model m
makes assumpptions
that may not
n represent the actual perrformance off the assets. Thhere is a narroow focus wheen creating thhe
VaR mod
del.35
33
Assumptiions for tradition
nal portfolio are made
m
for the purrpose of simplic ity.
Skintzi, V.
V D., G. Kiadou
ubpoulous and A.P.N.
A
Refenes, 2005,
2
The Effectt of Misestimatinng Coelation on Value at Risk, JJournal
of Alternativ
ve Investments, Spring 2005.
35
Adam Od
dar, “Value at Riisk (VAR)”, NYU
U Stern School of Management
34
59
Chapter 6
6.1
Quanttifying Liquiidity for Reall Asset Tran saction in a M
Mixed Portfoolio
bjective
Ob
Certain reeal assets appeear to be less liquid than trraditional asseets such as stoock and bondd because of thheir
nature. Th
he link to phy
ysical plant orr properties, as
a well as the pproduction/coonstruction riisk prevents thhese
assets to be
b traded at a frequent and instant mann
ner. This chappter takes PurreProperty as a sample indeex to
explore th
he rebalancing
g turnover as a measure off illiquidity annd intends to ttake the
transaction/managemen
nt cost as a risk premium associated
a
witth PureProperty. This is allso an attemptt to
bring real estate investm
ment through
h a more liquid channel intto the asset m
management prrocess.
6.2
Data Resource and Method
dology
o examine ho
ow rebalancing affects PureeProperty tradding, the authhor uses data ffrom FTSE foor
In order to
PurePropeerty index. Th
here are two sets
s of data. One
O is annual price index oof all individuual REITs andd
annual priice index of FTSE
F
NAREIIT REITs. The other is PurreProperty inddividual REIT
T price index and
individuall weights. Here we use thee data of 109 individual
i
RE
EITs’ one-dayy prices and w
weights. The ddata
is from Ap
pril, 2nd to rep
present a typiical rebalancin
ng day.36 Theen we comparre the two datta sets to see w
what
impact reb
balancing hass on the differrent methodollogies and seee if there is a middle grounnd to quantifyy the
rebalancin
ng cost.
6.3
Reb
balancing An
nalysis
Risk Ana
alysis
First, we take
t
a look att the individuaal REITs’ perrformance thrroughout the ttime starting from 2000 ass
compared
d with Equity REIT index. We use the data
d for the 100-year period from 2000 too 2010. The
starting in
ndexes are adjjusted to 1.
36
Rebalancing happens eveery month on thee second Friday according
a
to FTS
SE.
60
m
mean-volatility forr individual R
REITs and RE
EIT Index
Figure 37 show the scaatter plot of monthly
Scatter Pllot of Monthly Mean-Volaatility of Indivvidual REITs and REIT
Ind
dex
6.0
00%
5.0
00%
Monthly Return
4.0
00%
3.0
00%
2.0
00%
1.0
00%
0.0
00%
0.00%
-1.0
00%
5.0
00% 10.00% 15.00% 20.000% 25.00% 30.00% 35.000% 40.00% 45.00%
-2.0
00%
-3.0
00%
Risk
Individual REIT
Ts mean-volatiliity scatter plot
Source: FTSE, MIT CRE
E
REIT Inddex
Figure 38 shows both composite
c
Eq
quity REIT ind
dex (as shownn in dark boldd line) and inndividual REIT
Ts
indexes.
Figure 38-IIndividual REIITs indexes vs Composite RE
EIT index
Source: FTSE
E, MIT CRE
61
The graph shows the movement of all individual REITs indexes within the 10-year period. Certain REITs
have outperformed other REITs because of the idiosyncratic nature of these specific REITs. But we can
see that the REITs market started to slow down starting in late 2006 and took a bigger hit from the
financial crisis in 2008. Some REITs recovered faster than others, and the majority of the REITs have
been recovering more mildly. After the financial crisis, there are several REITs which diverged further
away, causing a “black swan event” with a “fat tail”, adding additional noise to the REITs index.
Then, we look at the REITs index from two risk perspective:
1. The volatilities of the composite REIT index
2. The idiosyncratic risks of individual REITs away from the composite index
Here as the second risk is the idiosyncratic risk away from the composite index volatility, we assume that
the volatilities of the composite REITs are independent of the idiosyncratic risk away from the composite
index. The relationship is expressed as below:
( + ) =
( ) +
( )
( , ) = 0
If X and Y are uncorrelated, that is
In other words, let’s use
,
to represent the return of REIT i at time t, and
,
to represent the excess
return of this REIT i at time t on top of the composite index return expressed as
,
, then the
correlation of the average return and the excess return at time t is zero.
,
And
,
=0
,
=
,
,
+
,
Then,
Here
=
,
,
,
+
(
,
)
denotes the average variance of all individual REITs,
of the composite REIT index, and
(
,
,
denotes the variance
) denotes the variance of the excess returns of all individual
REITs.
Figure 398- Equity REIT Monthly Index (2000-2010)
62
Monthly Return
-10%
20010131
20010531
20010928
20020131
20020531
20020930
20030131
20030530
20030930
20040130
20040528
20040930
20050131
20050531
20050930
20060131
20060531
20060929
20070131
20070531
20070928
20080131
20080530
20080930
20090130
20090529
20090930
20100129
20100528
20100930
Source: FTSE/NAREIT, MIT CRE
Figure 41- Equity REIT Annual Return
63
-20%
-30%
-40%
IndxNAREIT(equity REITs capital)
Source: FTSE/NAREIT, MIT CRE
Equity REIT Monthly Index
Index volatility is showed in the diagram below:
Figure 40- Equity REIT Index Monthly Return
Equity REITs Index Return (monthly)
40%
30%
20%
10%
0%
Dec-10
Jul-10
Feb-10
Sep-09
Apr-09
Nov-08
Jun-08
Jan-08
Aug-07
Mar-07
Oct-06
May-06
Dec-05
Jul-05
Feb-05
Sep-04
Apr-04
Nov-03
Jun-03
Jan-03
Aug-02
Mar-02
Oct-01
May-01
Dec-00
Total Index (1999=1)
Equity REIT Monthly Index (2000-2010)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Equity REIT Index Return (annual)
50%
40%
30%
Annual Return
20%
10%
0%
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
-10%
-20%
-30%
-40%
-50%
Equity REIT Annual Return
Source: FTSE/NAREIT, MIT CRE
Average monthly return of the composite REIT index is 0.67%.
Monthly volatility of the composite REIT index is 7.19%.
Average monthly volatility of all individual REITs is 9.99%.
Annual volatility of the composite REIT index is denoted as:
= √12 ∗
Therefore, annual volatility of the composite index is
And annual variance of the composite index is
Annual volatility of individual REITs is
=
,
= 6.2%
,
= √12 ∗ 9.99% = 34.6%
,
(
And annual variance of the individual REITs is
= √12 ∗ 7.19% = 24.9%,
,
,
)=
,
= 12.0%
According to the variance equation
We get
,
=
,
+
,
=
(
,
−
,
)
,
= 12.0% − 6.2% = 5.8%
=
Therefore, annualized idiosyncratic volatility is
64
,
= 24.1%
Monthly idiosyncratic volatility is
=
√
=
. %
√
= 6.9%
What does this imply?
The drift of individual REITs away from the composite index has an annual volatility of 24.1%, or a
monthly volatility of 6.9%, no matter how the composite index moves.
The idea of rebalancing is to recalculate each individual REIT’s new weight from regression for trading,
and these new weights are new levels of indexes for individual REITs which need to either sell or buy to
rebalance to these new levels. The following part will delve into the rebalancing process to reexamine the
risk analysis in this part.
Then we switch from standard deviation (volatility) to average absolute deviation by multiplying
approximately 0.8 times the standard deviation.
=
∗ 0.8=6.9%*0.8=5.52%
This is the average absolute monthly drift for the average REIT index.
Monthly Turnover Analysis
PureProperty index has a review process for weights of all individual REITs each month. Below is the
basic process of rebalancing according to FTSE:
1. Monthly review
For the total of more than one hundred individual REITs, FTSE reports daily weights based on trading.
Every month the new weights are applied to each individual REIT of the PureProperty Index Series.
2. Rebalance once a month
The weights for individual REITs are recalculated on the second Friday of each month following the
algorithm regression models using data available as of the close of business on the previous day.
3. Weights change
Weight changes resulting from the monthly review will be implemented at the close of business on the
third Friday of each month.
To illustrate the data:
65
The author uses PureProperty price index and weights. Here we use data of 109 individual REITs’ oneday prices and weights. The data is April, 2nd to represent a typical rebalancing day.37 The following
graph demonstrates one individual REIT’s rebalance movement.
Figure 42-Illustrative Rebalancing Graph
This diagram is a demonstration of daily weight movement compared to target monthly weight movement.
As weights drift away from target level, the weights will be readjusted to the target level each month,
causing either short or long position. For all REITs included in the FTSE NAREIT PureProperty index,
there are 109 (the total number of listed REITs) such pairs of weight movement lines throughout time.
The weights are percentage numbers. Then, we sum up all the absolute differences in weights to get daily
turnover percentage of individual constituents.
Results:
The following table shows the turnover of all 22 constituents on a typical day of April 2nd, 2012. 38
37
According to FTSE, rebalancing happens every month on the second Friday according to FTSE, trading happens after the third
Friday every month.
38
Here we use single-day data to represent a typical day of rebalancing.
66
Figure 43-Sample Turnover Summery of 22 Constituents of REITs
Row Labels
FNPAPD
FNPERAPD
FNPEROPD
FNPERPD
FNPERRPD
FNPHCPD
FNPHPD
FNPIPD
FNPMRAPD
FNPMROPD
FNPMRPD
FNPMRRPD
FNPOPD
FNPRPD
FNPSRAPD
FNPSROPD
FNPSRPD
FNPSRRPD
FNPWRAPD
FNPWROPD
FNPWRPD
FNPWRRPD
Grand Total
Sum of Turnover
0.010285235
0.061947647
0.059565884
0.056272018
0.07829051
0.017652481
0.021422112
0.025784902
0.204960462
0.48375106
0.177340221
0.27260383
0.0209419
0.020292819
0.045233486
0.187553978
0.126305879
0.39230771
0.02713054
0.074567894
0.056507023
0.149526836
2.570244426
The average turnover rate of all constituents is 11.68%. As rebalancing happens once a month, we
multiply the monthly turnover rate by 12 to get the annual turnover rate, 140%.
Now let’s look back at the idiosyncratic drift from the previous analysis.
The average monthly absolute deviation is about 0.8 times the standard deviation.
=
∗ 0.8=6.9%*0.8=5.52%. For simplicity, we use 6% here.
The average normal weight of trading individual REITs is 2.4% across all 22 constituents based on the
analysis of the data of April 2nd 2012 and the average normal weight of trading individual REITs for all
eight tradable REITs is 1.75%.39 Therefore, we apply the “tradable” rate to the all 109 individual REITs
with the monthly drift rate of 6%, then we get the monthly rebalancing turnover of
6%*1.75%*109=11.45%. Multiply the monthly turnover of 11.45% by 12, we get the annual turnover
rate, 137%.
39
The top eight constituents are considered “tradable” here in the analysis according.
67
ng the two meethodologies, we get comparable annuall turnover ratees. Assume a typical tradinng
Comparin
cost for reebalancing is 25 basis poin
nts.40 Now forr around 137%
% or 140% off rebalancing,, we get arounnd 35
basis poin
nts. Factoring in other riskss associated with
w real estatte trading, a 550 basis points risk premiuum for
PurePropeerty seems reaasonable.
One thing to keep
k
in mind iis that the shoort positions aarise only in tthe
Brad Casee from FTSE noted that “O
property type,
t
region, or
o type/region
n combination
n indices—noot in the “headdline” all-prooperty, all-reggion
index. An
nother issue, aside
a
from thee treatment off dividends onn short holdinngs, is the de--levering
piece. Essentially, the ETF manageer is trying to hold a (long)) fixed-incom
me position thaat replicates thhe
xed-income po
osition of the REITs. The index is baseed on REIT-leevel data on tthe book valuue of
(short) fix
total debt,, plus an assu
umed industry
y-wide averag
ge cost of debtt that we estim
mate from Feederal Reservee H15
data on co
ommercial paaper and Mood
dy’s Baa corp
porate bond raates. So you’’re trying to hhold a fixedincome po
osition that geenerates the same income (return)
(
as thaat assumed avverage cost off debt times tthe
aggregatee book value of
o total debt across
a
all REITs.”
6.4
Conclusions
n of the directt drive for thee weight flucttuation, the R
REIT price driift has
Although there is no cllear indication
somehow impacted thee portfolio weeights change. The new tarrget weights aare regressed bby calculationn,
i
RE
EIT to rebalan
nce to the new
w target weighht each monthh. This appliees to
and this reequires each individual
any portfo
olio that attem
mpts to track an
a index with
h fixed weightts differing frrom simple m
market-cap-bassed
weights. However,
H
therre could also be some kind
d of Browniann drift term thhat affects thee weights drifft.
The resultt shows that a risk premium
m of around 50
5 basis pointts may be dedducted from thhe historic retturns
of the PurreProperty ind
dex regarding
g rebalancing cost. This facctors in the addditional mannagement costt of
the real esstate index. As
A REITs appeears more trad
dable than phhysical real esstate assets, thhis is an attem
mpt to
treat certaain real assets through a mo
ore liquid way
y and in turn,, certain risk ppremiums aree considered.
40
Accordin
ng to FTSE.
68
Chapter 7
7.1
Institu
utional Invesstors’ Asset Allocation
A
Sttrategy
Ob
bjective
pter further loo
oks into the real world praactice of invesstment managgement on assset allocation
This chap
strategies.. This is an in
nterview-baseed research inttended to gainn understandiing from instiitutional investors’
perspectiv
ve. This chaptter includes open-ended
o
co
onversations w
with investmeent professionnals from diffferent
global maarkets. The fraamework of th
he discussion
n is flexible inn terms of discussion topiccs with a geneeral
focus on portfolio
p
struccture and inveestment strateegy in order too understand disparity amoong different
investors and common goals as welll.
7.2
Intterviewee Sellection
The selecttion of interviewee is based on the diversity of indusstry and geogrraphic exposuure. This grouup
includes international institutional
i
in
nvestors with
h heavy internnational expossure and stronng placement on
real estatee and other real assets. Inteerviewee list includes
i
top aasset managem
ment companny in the Uniteed
States by AUM (asset under
u
manageement), top so
overeign weaalth fund locatted in Asia (eex China), topp
nt managemen
nt firm locateed in the US, top
t asset mannagement firm
m in China byy AUM (assett
investmen
under man
nagement), on
ne of the largest asset man
nagement firm
ms in the worlld by AUM annd leading
financial indices
i
company in Europee. Other interrviewees partiicipate partiallly or informaally in the
interview. All interview
wees are proteected of their identities in tthis thesis.
7.3
Ressearch Methodology
views were co
onducted either in person, over the phonne or emails.
The interv
The interv
views were co
onducted from
m May, 2012 to July, 20122.
Typical in
nterviews took
k 30-60 minu
utes.
Interviews include opeen discussion on recent inv
vestment manaagement trendds and strateggies, specific
n on asset allo
ocation to reaal estate and other
o
real asseets, and open discussion onn globalization of
discussion
investmen
nt.
69
7.4
uestionnaire
Qu
t intervieweee as a base fo
for discussionn, and some off the questionns
The questtionnaire was provided to the
were not directly
d
targetted during thee interview. Therefore,
T
thiss chapter doees not depend on the collecction
of intervieewees’ respon
nses as any reepresentative of quantitativve data. Ratheer, the organizzation of the
interview is intended to
o engage asseet managers in
n an open disccussion of thee general porttfolio strategyy and
align such
h strategies in
n the understan
nding of the applicability
a
oof our analysis.
uestions 41
Sample qu
1. Assett allocation crriterion and sttrategy
-What do you think is the typicall asset allocattion frameworrk to real estaate for a globaal institutionaal
investtor? What abo
out to commo
odity and infraastructure?
-When
n deciding on the investmen
nt portfolio, is
i there a basee model for innvestors to coompare with? What
are th
he limitations//issues in seleecting real assset classes succh as real estaate, infrastruccture and
comm
modity?
2. Liquidity for real estate
e
and oth
her real asset classes
c
-When including reeal estate and
d other real asset classes inn an investmennt portfolio, hhow do you seee
liquid
dity issue as you
y make allocation decisio
ons?
-Havee industry pro
ofessionals qu
uantified liquiidity cost wheen comparingg weights? If sso, which asseet
classees do you thin
nk require the most in cost managementt? If not, how
w do they deal with illiquidiity
risk?
-Do you
y see real esstate as a sepaarate asset claass from otherr real assets ssuch as infrasttructure and
shippiing, or do you
u see it as an alternative in
nvestment for an institutionnal investor?
3. Globaal diversificattion
xt, what criteeria do you see is most impportant in goinng
-When investing reeal estate in a global contex
into a foreign mark
ket?
-Do you
y see investting in real esttate as a diverrsification heedge or a majoor investmentt in a portfolioo
mix?
-How
w do investors decide wheth
her to directly
y or indirectlyy invest in reaal assets such as real estatee,
infrasstructure, and shipping?
41
These queestions are open
n-ended, not all questions
q
are ansswered. The sam
mple questions prrovided interview
wees a backgrouund of
the research
h topic.
70
7.5
pics and Disccussion
Top
Selection of Portfolio Assets
p
of assets institutional investors couuld look at and there are nuumerous wayss to
While theere is a large pool
test out po
ortfolio perforrmance, certaain institution
nal investors teend to have sspecific targetts and base m
models
to comparre with. Usually, investmen
nt committees make the innitial investmeent decision aas to which
industriess to go in baseed on the com
mpany’s speciffic preferencee and experiennce. After thee decisions haave
been mad
de, fund / inveestment manag
gers will cond
duct the reseaarch and execcute the investtment strategyy.
nce, a typical base model could
c
be 75% equity and 255% fix-incom
me. Research ddepartment att the
For instan
front officce are assigneed to conduct analysis of different portfo
folio composittions and com
mpare the
return/risk
k with the 75//25 stock-bon
nd portfolio. While
W
there arre various ressults, investorrs usually set up
their targeet number of assets
a
to inveest in and are carefully targgeting the exppected return.
monstrates ho
ow a typical investment grroup works:
The diagrram below dem
Figure 44-IInvestment Maanagement Gro
oup Structure Sample
S
Investm
ment Committeee
Sets
S up targett goals/types of assets
to
t invest
Investm
ment Manager
Determine
D
Poortfolio Choicce
Researcch Team
Conduct
C
Quaantitative Reseearch
Give
G Investm
ment Suggestioons
y have differeent internal teeam structuress. For instancce, the decisioon making body
Different investors may
prise of both senior
s
executtives and fund
d managers inn some regionns while in som
me other areaas
may comp
fund manaagers have less priority in the decision making
m
proceess.
a increasing focus on com
mmodity as infflation is goinng strong in thhe recent yeaars, which pusshes
There is an
some com
mmodity pricees to go up. Fo
or example, energy
e
prices have gone upp due to inflattion movemennt.
71
Real Estate’s Role in Investment Portfolio
While real estate can provide inflation hedges, it is usually not a major investment for institutional
investors. The typical allocation to real estate is less than 20% and many institutional investors allocate
less than 10% to real estate.
The key role real estate plays in a mixed investment portfolio is the foremost physical form of asset with a
long-term hold strategy. Real estate is usually considered a channel to store values and is good for
inflation hedging. However, real estate is a very illiquid asset which requires specific expertise to run.
This is a character that many investors consider risky which puts up a hurdle for them to cross.
Real Estate Investment Strategy
Real estate investment is a specialized investment in many ways. Many institutional investment
companies set up real estate investment divisions to directly invest in properties. Some set up funds and
issue bonds while others allocate their own equity in real estate. No specific criteria were given as to who
tend to invest directly with their own equity and who tend to manage outside funds to invest in real estate.
However, some traditional banks tend to lean on management of outside sources of funds that invest in
real estate while some sovereign wealth funds prefer equity investment. Because investing in real estate
requires real estate expertise, only certain numbers of institutional investors see real estate as a major
investment or key business. There are many real estate investment management companies who just
specialize in this industry.
When investing in real estate, institutional investors tend to look at several factors:
-Nature of the property
-Location
-Inflation
-Growth factor
-Management cost
-Property depreciation
-Local debt market
When investing in a local market, investors have addressed the importance of local knowledge, which
includes local partners with past working experience and connection in the local market.
72
Global Market
Real estate is often considered a local business. When institutional investors look at foreign markets, they
tend to be very careful about the selection of properties. Institutional investors look at the big market
indicators such as GDP growth, maturity of the real estate market, and local debt market. Developed
countries provide stable income stream when investors hold core assets whereas developing countries
provide lucrative growth potential. Another key factor is government control and lending policy. This is
specifically important in some Asian markets such as China and Vietnam.
While the real estate market in China is getting more mature, investors also seek growth potential in high
growth markets. For instance, institutional investors mainly focused on the residential sector in the past
decade. With residential market cooling down and stabilizing, investors are beginning to look outside the
residential market. Service centers started to provide growth potential for investors as demand is growing
in both first-tier cities and secondary cities. Such service centers include office and shopping centers,
which are still growing in countries like China. In markets like India where there is strict government
control, investors also look into the IT industry, such as high-tech campus properties where there is large
demand and growth.
Further, investors look at the sustainability of the market, the growth of the middle class demographic,
and the urbanization movement.
Transparency in developing countries is also a concern that global investors expressed. In some regions of
the world, deals takes longer time to invest in these countries because of the intermediate process
associated with permitting.
Illiquidity
Liquidity is one major issue for real estate investors. However, for institutional investors who manage a
portfolio of assets that sees real estate as one of their investment channels, liquidity is more of a concern
on investment of the physical properties than a decisive hurdle. Certain institutional investors tend to act
as long-term holders of real estate portfolios and some don’t usually seek opportunistic assets as these
assets tend to act as short term investments. Macro-level knowledge of the economic trend and
geographic character is more predominant in decision making than individual liquidity.
73
While some institutional investors expressed the concern with illiquidity in real estate investment, they
have not largely quantified the illiquidity. Some investors strategically add certain risk premium, but the
strategy is more tactic than quantitative, which readdresses the riskiness of real estate investment. As for
liquidity in infrastructure, institutional investors also mentioned that the associated risk premium is higher
than other assets because it is less frequently traded than equity and bonds due to its nature.
Investment Strategy
There are several key strategies in the real estate investment. While core assets never go down due to their
stable performance and key geographic and demographic feature, there are core-plus assets, which are
emerging into a new market because of their growth potential with a character that is close to core asset.
Opportunistic assets provide the highest potential return, but it requires the ability to finance as typical
Loan-to-value ratio is usually as high as 60%-80%.
Market Trend
Some institutional investors stated that real estate has been traditionally outperformed the equity market
and provides certain degree of diversification thanks to the relatively low correlation with stock. For
instance, real estate has a different cycle from stock before the financial crisis in 2008, which allows
investors to strategize their investment portfolio accordingly. Further, there is a lag in real estate because
of transaction process. Residential real estate was hit hard during the financial crisis. However,
commercial real estate has been recovering and has come back relatively strong in the recent period,
especially those high quality assets which provide stable healthy income.
Infrastructure Investment
Infrastructure is a broad term and could include a large scope of assets. While airline and automobile
companies can be categorized as infrastructure assets, what institutional investors focus on lately are more
tangible assets. Both open-ended and closed-ended funds are used for infrastructure investment as certain
assets tend to have different life spans for investment purpose.
Below are the four typical infrastructure assets institutional investors focus on:
74
Infrastructure




Regulated Utilities
Communication Assets
Social Infrastructure
Transportation
Regulated Utilities here refer to companies that own utilities that are regulated. Electric power plant is
one regulated utility. Here utility has municipal finance and tax exemption, which provides cheap cost to
capital that private equity cannot compete with. Utilities also own a lot of private plants.
Communication Assets refer to materials designed to communicate messages to certain audience, either
hardcopy or electronic. “Asset types can range from raw materials (imagery, copy blocks, logos, legal text
and the like) to a more finished product that combines raw materials to create corporate brochures, sales
collateral, annual reports, press releases, contracts/agreements, technical documentation, education &
training materials, employee or customer newsletters and more.” 42
Social Infrastructures refer to social facilities such as healthcare facilities including hospitals, healthcare
financing, educational system including schools and universities, military facilities and prison. 43
Transportation here refers to railroads, toll roads, and shipping assets.44
There is no perfect infrastructure index that can represent such a broad scope. However, generally
speaking, infrastructure here refers to hard assets with long-term income stream and mostly
geographically fixed capital.
Therefore, investing in infrastructure has the advantage of stable income stream, relative low risk, and
stable growth. However, a lot of the infrastructure assets have limited accessibility, and are highly
regulated by governmental departments. If privatization or partial opening to private investment are
encouraged or expanded, there is great potential in investing in infrastructure.
Some institutional investors expressed the shift from investing in infrastructure before the financial crisis
to shying away from infrastructure due to the following reasons:
42
SHIFT, http://www.shift365.com/?q=node/13
Wikipedia: http://en.wikipedia.org/wiki/Infrastructure#Social_infrastructure
44
Airports and highways are rarely privatized for trading. Here transportation doesn’t typically refer to the physical assets for
airports and highways in the US.
43
75
-In some key markets, infrastructure was not deal flow because of the magnitude of the asset and the
holding strategy some investors have.
-Vague definition of infrastructure has prevented investors from strategizing a sustainable investment
within certain time period.
Infrastructure that includes toll roads and railways is usually capital intensive with long-term holding
period, which puts up a hurdle for some private equities. Some institutional investors don’t do direct
investment in infrastructure because of illiquidity and entry hurdle.
Commodity Investment
Commodity usually includes natural resources such as electricity, water, oil, raw metal and agricultural
products. Institutional investors have expressed their concern with investing in commodity as it is a tricky
asset due to its high volatility. Take oil for example, the oil price fluctuates much more dramatically than
stock, which shows that commodity exhibits instability and uncertainty for long-term investors. There are
commodity ETFs45 daily priced that are linked to the price of crude. If such commodity indexes are linked
to miners or plants, then investing in such commodity indexes induce risks due to the equipment
associated with them.
Hedge funds and high-frequency traders, however, have shown interest in investing in commodity as it
provides both short and long opportunities within a relatively short period. As an alternative investment in
a mixed portfolio, commodity has not been a major diversifier according to some institutional investors.
45
Exchange-Traded-Funds. Here refers to ETFs that hold commodity.
76
Chapter 8
Final Conclusions
The broad scope of real assets has span over various industries and markets. Certain real assets provide
investors both growth potential and uncertainty in the investment community. This thesis intends to
explore the roles of several selected real assets in a traditional stock-and-bond portfolio, and the findings
have indicated the challenges as well as potential in the dynamic management of investment portfolio.
Real estate and infrastructure exhibit certain inflation hedging potential thanks to their high correlations
with inflation. Commodity and real estate may also contribute to the diversification of an investment
portfolio due to their low correlation with stock. The high correlation of infrastructure with stock and a
comparable return has somehow prevented infrastructure from dominating in a Sharpe-Markowitz
portfolio. However, infrastructure may appeal to those investors who are less risk averse as it provides a
more attractive return compared with other real assets.
In a stock and bond portfolio, fix-income asset such as long-term government bond has a stable role in all
the possible pairing tests and all the Sharpe portfolios. This is in consistence with industry practice.46
When real estate and infrastructure are included in the portfolio, both assets have major roles. These two
real assets may have potential in a diversification role. Within real estate, PureProperty, as a separate
index, acts slightly more dominant than the private real estate represented by NCREIF NPI. Infrastructure
provides attractive return for investors as target return increases in the portfolio structure. However,
infrastructure may not perform well in the Sharpe portfolio according to the portfolio analysis. The reason
can be attributed to the high volatility of infrastructure and its high correlation with stock.
Commodity, on the other hand, has little weight in all of the portfolio pairings and has relatively small
contribution in the portfolio despite good diversification effect. The low return and high volatility of
commodity may have hampered commodity in the Sharpe efficient portfolio. The high risk of the
production process associated with commodity also concerns certain long term investors. And just
because of this, commodity provides potential in the derivatives market as hedge fund and other high
frequency traders seek out hedging opportunities in the high volatile markets, which is not suitable for
those long-only institutional investors. Therefore, commodity should have some specific roles in other
kinds of portfolio structures linked to both long and short investors and may not appeal to traditional
institutional investors. Another challenge for selecting assets is that historic returns of some assets may be
46
According to the 2012 PREA Investor Report’s plan sponsor asset allocation framework from 1996 to 2010, bonds take up
around 25%-30% of the total portfolio.
77
either too high or too low compared with recent figures. Some investors expect that the short-term returns
of these assets won’t go back to historic average. The data to include in the selection process will slightly
differ per investors’ preference and this will impact the portfolio choice. There are criteria more
complicated than purely looking at historic numbers as several recent data reveal changing trends in some
industries. This reflects back to the thesis analysis in that the models have their limitations, as the analysis
is mostly based on historic data and not on expected targets. However, the analysis looks at portfolios
from a long term perspective and examines the diversification for a longer history than what some
investors are looking at. A theoretic methodology can definitely adjust to specific investor preference at
some point.
Using the three different portfolio structures as described in the previous chapters to test out the Value at
Risk results, the traditional portfolio with around half the weight to stock, a quarter of weight to long term
government bond and the rest distributed to the several real assets47, exhibits a larger loss quantity at 5%
percentile of probability within any one year period than the Sharpe portfolios including both standard
risk and downside risk. The latter two portfolios have substantial weights on real estate (including NPI
and PureProperty), much smaller weight on stock, and a comparable weight on bond with the traditional
portfolio.
As the Value-at-Risk metrics here are left-hand tail measure, they lean towards the type of risk measures
that is similar to downside portfolio optimization analysis. The Downside-risk optimization portfolio
weights show slightly better results according to VaR than the Markowitz optimization portfolio. The
traditional portfolio created in the VaR is only based on the survey which allocation targets set by real
world investors, which are not directly derived through the optimization process based on data used in
this thesis. Therefore, it is reasonable to see the result that the traditional portfolio used here does not
perform as well as the two optimization models in the Value-at-Risk test.
The thesis also intends to quantify the transaction/management cost effect for PureProperty as a way to
take in real estate illiquidity in a dynamic investment world. The analysis results in certain haircut to the
risk premium associated to real estate and other assets to net out the operating cost. It is an attempt to
bring these real assets through more liquid channels in the understanding of the more interactive
management of investment portfolios.
47
Using the traditional portfolio model weights from PREA’s survey. 25% to bond as is shown in the sponsor survey by PREA ,
55% to stock, and 5% to real estate, PureProperty, commodity and infrastructure for simplicity.
78
It is important to note that with the short time period and limited access to data, there are not yet perfect
indexes to represent certain assets such as infrastructure and commodity, which shows the limitation of
the research conducted through this thesis. The real world practitioners have more measures to take when
managing their investment portfolios. However, the author attempts to take steps further to explore the
potential portfolio management strategies and is in hope of contributing some valuable addition to the
asset management research field in the days to come!
Figure 45 shows how weights change as expected return increases according to different pairings of assets
in a stock-and-bond portfolio. It also shows the Sharpe-Markowitz portfolio structure for each pairing. 48
Figure 45-Summary of Portfolio Analysis
48
The histogram on the right of the composition diagram demonstrates the Sharpe-Markowitz portfolio weights for the
corresponding pairing.
79
1.
Stan
ndard Risk
Sttock, Bond
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
2.
10.42%
0.51
8.14%
00.56
6.8%
0.63
7.2%
00.59
Sttock, Bond, Com
mmodity, Infrastrructure
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
4.
Downsside Risk
Sttock, Bond, Reall Estate
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
3.
VS
8.0%
0.58
9.7%
0.54
Sttock, Bond, Com
mmodity, Infrastrructure, Real Esttate
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
6.8%
0.64
77.3%
00.60
80
Stan
ndard Risk
Stock, Bond
d
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
1. Sttock, Bond, Reall Estate
VS
Downsiide Risk
10.42%
0.51
88.14%
00.56
Sharpe-Marrkowitz Volatilitty
6.8%
Sharpe Ratio
0.63
2. Sttock, Bond, Com
mmodity, Infrastrructure
7.2%
00.59
Sharpe-Marrkowitz Volatilitty
9.7%
0.54
Sharpe Ratio
mmodity, Infrastrructure, PurePro
operty
5. Sttock, Bond, Com
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
8.0%
0.58
6.99%
0.64
6.9%
0.66
81
Stan
ndard Risk
Stock, Bond
d
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
1.
10.42%
0.51
88.14%
00.56
6.8%
0.63
7.2%
00.59
Sttock, Bond, Com
mmodity, Infrastrructure
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
6.
Downsside Risk
Sttock, Bond, Reall Estate
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
2.
VS
8.0%
0.58
9.7%
0.54
Sttock, Bond, Com
mmodity, Infrastrructure, PurePro
operty, Real Estaate
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
5.6%
0.74
6.5%
0.66
82
3.
Standard
S
Risk
VS
mmodity, Infrastrructure, Real Esttate
Sttock, Bond, Com
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
5.
6.8%
0.64
77.3%
00.60
Sttock, Bond, Com
mmodity, Infrastrructure, PurePro
operty
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
6.
Dow
wnside Risk
6.9%
0.66
6.99%
0.64
Sttock, Bond, Com
mmodity, Infrastrructure, PurePro
operty, Real Estaate
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
5.6%
0.74
6.5%
0.66
V
at Risk
k models perfo
orm among sttandard Sharppe-Markowitzz portfolio,
Figure 46 shows how Value
Markowitz po
ortfolio49, and
d the traditionnal portfolio.
downside risk Sharpe-M
49
Using the Sharpe portfolio
o weights from the
t composition of Stock, Bond,, Commodity, Innfrastructure, PurreProperty, Reall Estate
83
Figure 46-V
Value at Risk Analysis
A
Summ
mary
Sttandard Risk
VS
V
Sharpe-Marrkowitz Volatilitty
Sharpe Ratio
5% VaR maax 1-year loss
5% VaR 5-y
year gain
Downsidee Risk
5.9%
0.64
-32%50
-1.7%51
6.8%
0.60
- 30%
-1.7%
Traditional Porrtfolio52
5% VaR maax 1-year loss
5% VaR 5-y
year gain
50
51
- 44.6%
-5.0%
Negativee number deno
otes absolute lo
oss.
Negativee number deno
otes absolute lo
oss.
52
Using thee traditional porttfolio model weights from PREA
A’s survey. 25% to bond as is shhown in the sponnsor survey by PR
REA ,
55% to stock, and 5% to reaal estate, PurePro
operty, commod
dity and infrastruucture.
84
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87
APPENDIX
Appendix 1-NPI, REIT-based PureProperty & CPPI Indices (2000-2012)
Composite Capital Value Indices 2000-2012
REIT-based PureProperty & Private Market-based Transactions Price Indices
1950
1900
1850
1800
1750
1700
1650
1600
1550
1500
1450
1400
1350
1300
1250
1200
1150
1100
1050
1000
950
900
Moodys/RCA CPPI Natl Composite
6/15/2012
12/16/2011
6/17/2011
12/17/2010
6/18/2010
12/18/2009
6/19/2009
12/19/2008
88
6/20/2008
Source: FTSE, MIT CRE
12/21/2007
NPI (CF based appraisal)
6/22/2007
12/22/2006
6/23/2006
12/23/2005
6/24/2005
12/24/2004
6/25/2004
12/26/2003
6/27/2003
12/27/2002
6/28/2002
12/28/2001
6/29/2001
12/29/2000
6/30/2000
12/31/1999
PureProperty Composite
x 2-Summary
y of Sharpe-M
Markowitz Portfolio
P
Appendix
Stock+Bon
nd+Real Estatee
Rm
10.0%
StD
6.8%
SHA
ARPE
0.63
Shaarpe
SP50
00Stk
22%
0.43
nd
USTBn
10--yr
29
9%
0.3
30
NCREIF
NPI
49%
0.41
Stock+Bon
nd+Real Estatee (haircut)
Rm
9.7%
StD
7.21%
SHA
ARPE
0.5
54718
Sharp
pe
SP500
0Stk
27%
2
0.42
0
d
USTBnd
10-y
yr
33%
%
0.28
8
NCREIF
NPI
40%
0.29
89
Stock+Bon
nd+Real Estatee (downside)
Rm
10.0%
StD
7.23%
SHA
ARPE
0.59354
Shaarpe
SP50
00Stk
24%
0.38
nd
USTBn
10--yr
53
3%
0.3
36
NCREIF
NPI
23%
0.33
Stock+Bon
nd+Real Estatee (downside haaircut)
Rm
9.9%
StD
7.58%
SHA
ARPE
0.5
54649
Sharp
pe
SP500
0Stk
28%
2
0.37
0
d
USTBnd
10-y
yr
59%
%
0.33
3
NCREIF
NPI
13%
0.24
90
Stock+Bon
nd+Commodity
y+Infrastructurre
nd
USTBn
10--yr
44
4%
0.3
30
SP
GSCI
12%
0.16
Composite
Infra
16%
0.46
Stock+Bon
nd+Commodity
y+Infrastructurre (haircut)
nd
USTBn
00Stk
10--yr
Rm
StD
SHA
ARPE SP50
10.8%
9.71%
0.52302
28%
44
4%
Shaarpe
0.42
0.2
28
SP
GSCI
12%
0.15
Composite
Infra
16%
0.45
Rm
11.0%
StD
9.71%
SHA
ARPE
0.54362
Shaarpe
SP50
00Stk
28%
0.43
91
Stock+Bon
nd+Commodity
y+Infrastructurre (downside)
nd
USTBn
00Stk
10--yr
Rm
StD SHARPE SP50
10.3%
7.98%
0.57790
21%
64
4%
Shaarpe
0.38
0.3
36
SP
GSCI
5%
0.13
Composite
Infra
9%
0.46
Stock+Bon
nd+Commodity
y+Infrastructurre (downside haircut)
h
nd
USTBn
SP
00Stk
10--yr
GSCI
Rm
StD
SHA
ARPE SP50
10.1%
7.98%
0.55285
21%
64
4%
5%
Shaarpe
0.37
0.3
33
0.12
Composite
Infra
9%
0.45
92
Stock+Bon
nd+Commodity
y+Infrastructurre+Real Estatee
nd
USTBn
00Stk
10--yr
Rm
StD
SHA
ARPE SP50
10.1%
6.80%
0.63926
16%
28
8%
Shaarpe
0.43
0.3
30
SP
GSCI
4%
0.16
Stock+Bon
nd+Commodity
y+Infrastructurre+Real Estatee (haircut)
d
USTBnd
SP
0Stk
10-y
yr
GSCI
Rm
StD
SHA
ARPE SP500
9.8%
7.31%
0.5
56535
19%
1
31%
%
6%
Sharp
pe
0.42
0
0.28
8
0.15
93
Composite
Infra
7%
0.46
Composite
Infra
10%
0.45
NCREIF
NPI
45%
0.41
N
NCREIF
NPI
34%
0.29
Stock+Bon
nd+Commodity
y+Infrastructurre+Real Estatee (downside)
nd
USTBn
SP
00Stk
10--yr
GSCI
Rm
StD
SHA
ARPE SP50
10.1%
7.32%
0.60361
16%
49
9%
3%
Shaarpe
0.38
0.3
36
0.13
Composite
Infra
10%
0.46
Stock+Bon
nd+Commodity
y+Infrastructurre+Real Estatee (downside haiircut)
nd
USTBn
SP Geltner00Stk
10--yr
GSCI Li Infra
Rm
StD
SHA
ARPE SP50
10.0%
7.69%
0.55865
18%
55
5%
4%
11%
Shaarpe
0.37
0.3
33
0.12
0.45
94
NCREIF
NPI
22%
0.33
NC
CREIF
NPI
11%
0.24
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty
nd
USTBn
00Stk
10--yr
Rm
StD
SHA
ARPE SP50
10.2%
6.85%
0.65802
10%
33
3%
Shaarpe
0.43
0.3
30
SP
GSCI
6%
0.16
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty (haircut)
d
USTBnd
SP
0Stk
10-y
yr
GSCI
Rm
StD
SHA
ARPE SP500
9.9%
6.95%
0.6
60756
13%
1
33%
%
6%
Sharp
pe
0.42
0
0.28
8
0.15
95
Composite
Infra
0%
0.46
Composite
Infra
0%
0.45
PureProp
51%
0.53
P
PureProp
48%
0.48
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty (downside)
nd
USTBn
SP Composite
Infra
00Stk
10--yr
GSCI
Rm
StD
SHA
ARPE SP50
10.1%
2%
6.87%
0.63851
12%
50
0%
1%
Shaarpe
0.38
0.3
36
0.13
0.46
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty (downside hhaircut)
d
USTBnd
SP Composite
Infra
0Stk
10-y
yr
GSCI
Rm
StD
SHA
ARPE SP500
9.9%
7.02%
0.5
59541
13%
1
50%
%
1%
4%
Sharp
pe
0.37
0
0.33
3
0.12
0.45
96
PureProp
35%
0.46
P
PureProp
31%
0.41
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty+Real Estatee
d
USTBnd
SP Composite
Infra
0Stk
10-y
yr
GSCI
Rm
StD
SHA
ARPE SP500
9.8%
5.61%
0.7
73523
6%
24%
%
2%
0%
Sharp
pe
0.43
0
0.30
0
0.16
0.46
P
PureProp
36%
0.53
NC
CREIF
NPI
31%
0.41
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty+Real Estatee (haircut)
d
USTBnd
SP Composite
Infra
0Stk
10-y
yr
GSCI
Rm
StD
SHA
ARPE SP500
9.5%
5.85%
0.6
64249
9%
27%
%
3%
0%
Sharp
pe
0.42
0
0.28
8
0.15
0.45
P
PureProp
37%
0.48
NC
CREIF
NPI
24%
0.29
97
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty+Real Estatee (downside)
nd
USTBn
SP Composite
Infra
00Stk
10--yr
GSCI
Rm
StD
SHA
ARPE SP50
10.0%
3%
6.46%
0.65971
9%
40
0%
0%
Shaarpe
0.38
0.3
36
0.13
0.46
PureProp
31%
0.46
N
NCREIF
NPI
17%
0.33
Stock+Bon
nd+Commodity
y+Infrastructurre+PureProperrty+Real Estatee (downside haaircut)
nd
USTBn
SP Composite
Infra PureProp
00Stk
10--yr
GSCI
Rm
StD
SHA
ARPE SP50
10.0%
3%
6.46%
0.65971
9%
40
0%
0%
31%
Shaarpe
0.38
0.3
36
0.13
0.46
0.46
N
NCREIF
NPI
17%
0.33
98
Appendix 3-Value at Risk Test Summary
Sharpe-Markowitz Portfolio
Simulation Results Statistic:
MaxLossAny1yr
Mean
-20.9%
Maximum
-3.3%
95%ile
-11.3%
5%ile
-32.1%
Minimum
-47.8%
Std.Dev
6.3%
Yr5Gain/Yr
10.1%
37.5%
21.9%
-1.7%
-13.6%
7.4%
Mean
11.3%
20.8%
15.5%
7.1%
2.3%
2.5%
Volatility
15.3%
21.6%
18.4%
12.4%
9.9%
1.8%
Yr5Gain/Yr
9.7%
32.2%
21.1%
-1.7%
-12.4%
7.0%
Mean
10.6%
18.7%
14.4%
6.6%
1.2%
2.4%
Volatility
14.4%
20.7%
17.2%
11.8%
8.6%
1.6%
Yr5Gain/Yr
13.4%
53.2%
30.9%
-5.0%
-21.1%
10.9%
Mean
15.0%
29.0%
21.1%
8.8%
2.8%
3.7%
Volatility
21.0%
29.8%
25.3%
17.1%
13.9%
2.5%
Downside risk Portfolio
Simulation Results Statistics:
MaxLossAny1yr
Mean
-19.7%
Maximum
-3.0%
95%ile
-10.7%
5%ile
-30.0%
Minimum
-48.7%
Std.Dev
5.9%
Traditional Portfolio
Simulation Results Statistics:
MaxLossAny1yr
Mean
-29.2%
Maximum
-5.1%
95%ile
-16.0%
5%ile
-44.6%
Minimum
-63.9%
Std.Dev
8.9%
99
©2012 Xiangyu Li
100