Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Asset Allocation for Commodity-Based
Sovereign Wealth Funds
Implications for Emerging Economies
Rolando Avendaño - Javier Santiso
OECD Development Centre, 2008
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Outline
Introduction and Facts
1
Theoretical Framework
2
Implementation
3
Conclusion
4
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
INTRODUCTION
Motivation and some Basic Facts
SWF concept: investment vehicle
with high foreign asset exposure,
nonstandard liabilities and long
(intergenerational) time horizon.
Assets: Long-term, active,
diversified investments. Main asset
classes: bonds, equity, alternatives.
Size: Low commodity-price/exports
scenario has affected some, but still
resilient (11.6 tr to 9.5 tr estimate
Nov. 08)
Source: COFER database and International Financial Statistics
Incentives and purpose
Stabilisation vs. Savings
Preventive/oriented strategy
Challenges for current SWFs
National strategy vs.
commercial return
Passive vs. active policy
Stabilizing financial markets
vs. market jeopardy
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
INTRODUCTION
Context, related research at the Centre
Today: Financing needs for the short
run are important. Dry global liquidity Financing Needs and International Reserves -
scenario and contraction of 2009
cross-border capital flows External
financing FX Reserves Gap between reserves and
needs 2009 (Dec 08) financing needs
Home bias / Regional bias → Argentina 6.4 12.7 6.3
Challenge to stimulate domestic Brazil 6.7 12.5 5.8
-6.5
Chile 18.6 12.1
economies. Affects allocation? Colombia 8 9.7 1.7
Ecuador 6.8 11.3 4.5
Standard portfolio approach (CAPM, Mexico 6.1 7.7 1.6
VaR, etc.) insufficient for active -9.5
Hungary 23.3 13.8
-2.1
Kazakhstan 18.4 16.3
management. Nigeria 2.7 24.3 21.6
-7.8
Poland 19.7 11.9
Some related work at the Centre:
Russia 9.4 26.2 16.8
H. Reisen. \"Commodity and -5.9
South Africa 16.8 10.9
-6.4
Turkey 15.8 9.4
non-commodity SWFs\" Deutsche -7
Ukraine 25.9 18.9
Bank WP, \"Fonds souverains et China 0.3 43 42.7
économie du développement\" India 8.8 71.9 63.1
Indonesia 10.3 10.8 0.5
Revue d’Economie Politique. Korea 21.1 24.3 3.2
Malaysia 4.5 46.6 42.1
J. Santiso \"Sovereign Thailand 19.2 38.5 19.3
Development Funds\", Revue Source: Credit Suisse, \"Are EM funding needs driving financial market\\nprices?\", Dec.
08.
d’Economie Financière.
Avendano, Reisen, Santiso.
\"Macro Management of
Commodity Booms\".
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
How are commodity-SWF assets allocated?
Considerations for Asset Allocation
Traditional reserves approach Criteria for public sector (Reisen
1
2008, van der Ploeg 2007):
Multiple, conflicting investment
objectives: liquidity, peg, foreign Depleting: Hotelling,
debt, trade. steady-state
Tranching facilitates management. Saving: Hartwick,
Difficult to optimise \"as a whole\" commodity price
Domestic investment:
Country-specific criteria:
2
Excess return, construction
Size of reserves price smoothing
Transparency or accountability Retiring debt: Excess cost
Purpose of holding reserves of public debt over global
Objectives in managing reserves return
Constraints and risk-return profile
Market related criteria
3
Structural changes
Cyclical changes
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
Variables/constraints for SWF = reserves
Objectives and Constraints
Constraints
Non-financial risk:
reputation, operation
Currency/Asset class
exposure
Derivative usage
Institutional: Frequency
disclosure, benchmark
Risk-return preferences
Time Horizon
Unit of account
Nominal vs real return
Finding distribution by return
or risk
Risk-return expectations
Forward-looking of risk,
return, asset classes
Some approaches
Markowitz mean-variance
Monte Carlo simulation
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Asset-Liability

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
Approaches to SWF Asset Allocation
Studies on Reserves, Asset Management and SSA
Management of Reserves
1
International currencies → Linder (1969), Hartmann (1998),
Eichengreen (2005).
Jeanne and Rancière (2008) → Optimal level of reserves for
emerging countries.
Portes et al. (2006) → Optimal Currency Shares in
International Reserves
Portfolio Choice
2
Dynamic stochastic optimisation (Claessens and Kreuser
2004).
Monte Carlo Simulation → (Weiberger and Golub 2007)
Portfolio choice → Campbell 2003, Scherer 2008.
Contingent Claims Approach
3
Alfaro and Kanczuk (2003), Caballero and Panageas (2004) →
Contingent reserves management.
Asset-Liability approach: Rudolf and Ziemba (2004),
Rozanov (2006), Binsbergen and Brandt (2006)
Real Capital preservation → (Bonza et al. 2006)
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
SWFs and Foreign Investment
Theoretical Framework
Conceptual Issues
Implementation
Literature on Asset Allocation for Sovereign Funds
Conclusion
Outline
Introduction and Facts
1
Theoretical Framework
2
Implementation
3
Conclusion
4
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
Portfolio Choice and the sovereign investor
Scherer (2008): Risk from non-financial assets can be hedged, at least partially,
through financial assets. Different to classical CAPM, where only financial assets
are considered.
Key factor: exploit the correlation between financial and non-financial assets to
reduce overall SWF risk.
Advantages:
Timely for resource-rich economies
Addressing the lack of data for SWF asset allocation studies.
Similar to asset-liability management approach (both sides of the balance
sheet).
Defined objective: reduce total wealth volatility.
Non-normal returns in short run \"controlled\" by SWF long-term investment
approach (utility function)
Extensions:
Application for other commodity funds
Apply to a multi-asset context: alternatives, other commodities, infrastructure,
real estate
Look at exposure to emerging markets
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
The Sovereign investor problem
Case 1: Investing in one risky asset
The decision making problem The SWF can invest its financial wealth into a single
asset or cash.
2
˜a ∼ N(µa , σa )
r
µa : Expected risk premium (over cash)
σa : Volatility.
The government budget moves with changes on its claim on economic net wealth.
Commodity price changes are also normally distributed:
2
˜o ∼ N(µo , σo )
r
and correlate positively with asset returns, i.e.
Cov (ra , ˜o ) = ρa,o > 0
r
.
Hotelling-Solow rule (indifferent to depletion or keeping commodity) → µo = 0.
Let θ be the fraction of importance of the SWF plays in the economies government
budget. Therefore:
˜ = θw ˜a + (1 − θ)˜o
r r r
with 1 − w representing cash holding that carries a zero risk premium and no risk
in one period.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
The Sovereign investor problem
Decomposing demand
The SWF manager is charged to maximize the utility of total government
wealth rather than narrowly maximizing the utility for its direct assets
under management. Utility defined as a (quadratic) function of uncertain
wealth. The goverment seeks to maximize the function:
λ 2 22
θ w σa + (1 − θ)2 σo + 2wθ(1 − θ)ρσa σo
2
Maxw θwµa −
2
Taking first order conditions and solving for w, the optimal asset
allocation for a resource based SWF:
1 µa 1 − θ ρσo
w ∗ = ws + wh =
∗ ∗
−
2
θ λσa θ σa
Total demand = w ∗ = +
∗ ∗
ws wh
Hedging demand
Speculative demand
In the case of uncorrelated assets and commodity resources the optimal
solution is a leveraged position (with factor 1/θ) in the asset with
maximum Sharpe-ratio (reward/variance).
Observation 1:
Demand for risky assets can be descomposed between speculative and
hedging. Risk-return (Sharpe) criteria is not the only one. The optimal weight
of risky assets is independent from his wealth level.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
Two types of Demand
Hegding vs Speculative demand
Hedging demand: the desirability of the asset does not only depend on Sharpe-ratio but
also on its ability to hedge out unanticipated shocks to commodity wealth. Hedging
demand is given as the product of leverage and commodity asset beta,
ρσo
βo,a =
σa
.
This is equivalent to the slope coefficient of a regression of (demeaned) asset returns
against (demeaned) commodity returns of the form:
(ro − r o ) = βo,a (ra − r a ) + ε
Positive correlation between asset and commodity price risk increases the volatility of
total wealth. A 100% short position in the risky asset helps to manage total risk.
However, in case the correlation was negative it would be necessary to increase the
allocation to the risky asset.
Observation 2:
Corelation patterns between commodity prices and other assets may depict the best
investment profile for the SWF. The best investment profile for SWFs balances returns with
hedging against commodity prices. Specific sectors provide hedging against commodity
prices.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
Growth and Hedge Assets
Case 2: Optimal portfolio with several assets
Extended case with two assets: one asset hedging asset (i.e. it show negative
correlation) and another asset provides growth orthogonal to commodity wealth
changes. The setup if summarized with the following distribution:
2 2
˜g ∼ N(µg , σg ), ˜h ∼ N(µh , σh )
r r
where rg and rh stand for the return of growth and hedge assets with µg > µh .
The correlation assumptions are:
Cov (rh , rg ) = ρh,g σh σg > 0, Cov (rh , ro ) = ρh,o σh σo < 0, Cov (rg , ro ) = 0
The government budget evolves to:
˜ = θ[wg ˜g + wh ˜h ] + (1 − θ)˜o
r r r r
where utility is given by
λ
E(˜2 ) − E 2 (˜)
u = E(˜) −
r r r
2
And solve for wg and wh :
µg − βg,h · µh 1 − θ βg,h ρh,o σo σh
wg =
∗
−
λθ(1 − ρ2 )σg (1 − ρ2 )
θ
2
g,h g,h
µh − βh,g · µg 1−θ βo,h
wh =
∗
−
λθ(1 − ρ2 )σh 2 θ (1 − ρ2 )
g,h g,h
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Tackling the Problem
Theoretical Framework
Basic Model of Asset Allocation
Implementation
Optimal Allocation between Growth Assets and Hedge Assets
Conclusion
Demand for the growth asset can be split again into speculative demand
and hedging demand:
Speculative demand will depend on its \"alpha\", µg − βg,h · µh ,
versus the hedge asset, i.e. Beta, βg,h , adjusted excess return
divided by the risk not explained by the hedge asset returns.
The term ρ2 can interpreted as the R 2 of a regression of hedge
g,h
versus growth asset returns. If the indirect correlation is set to zero,
i.e. ρr ,g = 0 then:
µg
wg =
∗
2
λθσg
µh σo (1 − θ)
wh = − ρh,o
∗
2 σh θ
λθσh
Observation 3:
The growth asset is entirely driven by the Sharpe-ratio while the hedge asset
combines both speculative and hedge demand.
How does hedge demand change with θ?
−µh + λρh,o σh σo
dwh∗
= <0
2
λσh θ2
dθ
Observation 4:
Economies with falling levels of commodity resources should be more
conservative in their investments.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Implementation: \"Commodity-Asset Betas\"
Bond Benchmark and Commodity Price Changes
Monthly data → No pattern on
Correlation U.S Benchmark Bonds with Commodity Price Changes
correlations. Reducing data
US US US US US
frequency shows negative
US Benchmark
Benchmark Benchmark Benchmark Benchmark Benchmark
DS 30 Years
DS 2 Years DS 3 Years DS 5 Years DS 7 Years DS 10 Years
correlations.
Asset Returns vs Oil Price Changes
0.007 0.003 -0.001 -0.003 -0.005 -0.021
Correlation and significance rise
Monthly
0.108 0.050 -0.017 -0.051 -0.080 -0.314
0.013 0.007 -0.002 -0.001 -0.008 -0.027
Quarterly
when decreasing the frequency.
0.201 0.105 -0.026 -0.013 -0.114 -0.409
0.110 0.096 0.071 0.065 0.048 0.020
Yearly
1.666 1.455 1.074 0.976 0.729 0.293
Global equities provide no hedge
Asset Returns vs Copper Price Changes
against oil price changes.
0.006 0.005 0.001 -0.002 -0.009 -0.031
Monthly
0.096 0.074 0.013 -0.024 -0.142 -0.473
0.008 0.004 -0.008 -0.004 -0.019 -0.038
Quarterly
Sector equity indexes →
0.118 0.057 -0.117 -0.063 -0.293 -0.574
-0.044 -0.048 -0.078 -0.077 -0.116 -0.133
Yearly
-0.668 -0.716 -1.172 -1.157 -1.748 -2.018
Significant negative correlation
Asset Returns vs Commodity Index Change
for two sectors (defensive
-0.033 -0.037 -0.040 -0.036 -0.036 -0.054
Monthly
-0.494 -0.559 -0.595 -0.547 -0.549 -0.817
consumer and health care) that
-0.044 -0.052 -0.056 -0.039 -0.042 -0.070
Quarterly
-0.659 -0.782 -0.847 -0.593 -0.626 -1.053
-0.117 -0.128 -0.182 -0.180 -0.213 -0.252
tend to do well when the
Yearly
-1.774 -1.944 -2.787 -2.745 -3.285 -3.908
economy does badly.
Note: Benchmarks for bonds from Thomson Datastream (DS Bemchmark) from January 1990 to
January 2009. The first line is the correlation coefficient, and the second provides its
t-value,
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Global Equities and Commodities
Global Equities and Commodity Price Changes
Global Equities and Commodities
World World World
World Oil World Basic World World Health World World World
Global Consumer Consumer
and Gas Materials Industrials Care Telecoms Utilities Financials
Equity Goods Services
Global Equity Indexes (per sector) vs Oil Price Changes
0.074 0.046 0.063 0.077 0.059 0.038 0.090 0.082 0.024 0.068
Monthly
1.116 0.686 0.954 1.167 0.895 0.576 1.361 1.238 0.358 1.025
0.152 0.150 0.161 0.127 0.177 0.156 0.155
0.102 0.084 0.073
Quarterly
2.313 1.547 2.274 2.452 1.918 1.272 2.705 2.371 1.100 2.361
0.308 0.175 0.215 0.312 0.238 0.158 0.332 0.464 0.115 0.237
Yearly
4.873 2.680 3.302 4.943 3.691 2.407 5.299 7.882 1.745 3.673
Global Equity Indexes (per sector) vs Copper Price Changes
0.065 0.052 0.062 0.073 0.044 0.040 0.086 0.002 0.013 0.100
Monthly
0.981 0.786 0.934 1.102 0.662 0.605 1.305 0.034 0.193 1.511
0.161 0.140 0.157 0.180 0.191 0.228
0.123 0.096 0.033 0.067
Quarterly
2.449 2.129 2.391 2.748 1.858 1.447 2.921 0.499 1.014 3.522
0.213 0.171 0.218 0.249 0.178 0.059 0.234 0.060 0.057 0.309
Yearly
3.270 2.611 3.355 3.866 2.726 0.893 3.626 0.901 0.863 4.878
Global Equity Indexes (per sector) vs Commodity Price Index Change
0.144 0.110 0.142 0.156 0.104 0.115 0.178 0.012 0.071 0.210
Monthly
2.190 1.666 2.159 2.368 1.566 1.733 2.721 0.179 1.068 3.236
0.265 0.218 0.261 0.279 0.190 0.207 0.306 0.147 0.358
0.057
Quarterly
4.124 3.357 4.061 4.367 2.907 3.184 4.830 0.858 2.229 5.766
0.449 0.326 0.397 0.447 0.380 0.320 0.458 0.168 0.222 0.605
Yearly
7.557 5.184 6.512 7.506 6.182 5.075 7.742 2.566 3.422 11.430
Note: Benchmarks for bonds from Thomson Datastream (DS Bemchmark) from January 1990 to
January 2009. The first line is the correlation coefficient, and the second proveds its t-value,
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Emerging Bonds and Commodities
EMBI and Commodity Price Changes
Emerging Market Bonds with Commodity Price Changes
Brazil Chile China Indonesia Kazakhstan Mexico Malaysia Morocco Poland Russia South Turkey Thailand Venezuela
Bond Returns vs Oil Price Changes
-0.014 -0.004 -0.013 0.180 0.695 0.024 0.030 -0.004 0.004 0.077 0.074 0.054 0.027 0.103
Monthly
-0.138 -0.034 -0.126 1.777 9.366 0.237 0.287 -0.038 0.042 0.746 0.721 0.523 0.264 1.007
0.009 0.054 -0.021 0.400 0.953 0.084 0.077 0.008 0.022 0.162 0.144 0.129 0.060 0.235
Quarterly
0.048 0.296 -0.112 2.392 17.184 0.459 0.423 0.046 0.119 0.899 0.795 0.714 0.327 1.324
-0.103 0.095 -0.093 0.207 1.000 0.051 0.069 -0.015 0.048 0.198 0.088 0.024 0.159 0.341
Yearly
-0.252 0.234 -0.228 0.518 N.A. 0.126 0.170 -0.037 0.119 0.495 0.218 0.059 0.394 0.888
Bond Returns vs Copper Price Changes
-0.003 -0.007 -0.012 0.133 0.582 0.025 0.031 0.007 -0.007 0.099 0.090 0.075 0.073 0.151
Monthly
-0.025 -0.064 -0.117 1.300 6.940 0.245 0.302 0.072 -0.064 0.963 0.878 0.730 0.710 1.480
0.024 0.038 -0.038 0.301 0.845 0.076 0.057 0.036 -0.012 0.175 0.144 0.139 0.134 0.281
Quarterly
0.130 0.206 -0.210 1.730 8.655 0.416 0.315 0.195 -0.067 0.974 0.800 0.766 0.741 1.602
-0.101 0.010 -0.275 0.205 1.000 -0.024 -0.051 0.010 -0.151 0.171 0.003 0.005 0.222 0.490
Yearly
-0.249 0.024 -0.700 0.514 N.A. -0.059 -0.126 0.024 -0.373 0.426 0.007 0.012 0.557 1.376
Bond Returns vs Commodity Index Change
0.301 0.845 0.281
0.024 0.038 -0.038 0.076 0.057 0.036 -0.012 0.175 0.144 0.139 0.134
Monthly
0.130 0.206 -0.210 1.730 8.655 0.416 0.315 0.195 -0.067 0.974 0.800 0.766 0.741 1.602
0.062 0.076 0.050 0.646 0.884 0.134 0.126 0.043 0.061 0.222 0.210 0.185 0.101 0.286
Quarterly
0.342 0.415 0.272 4.631 10.384 0.740 0.693 0.235 0.336 1.247 1.179 1.034 0.555 1.638
-0.080 0.033 -0.217 0.719 1.000 0.020 -0.021 0.059 -0.083 0.200 0.019 0.011 0.173 0.479
Yearly
-0.197 0.081 -0.543 2.537 N.A. 0.049 -0.050 0.146 -0.204 0.500 0.046 0.026 0.430 1.336
Note: Benchmarks for Emerging Market bonds from JP Morgan EMBI index (Thomson) from January 2001 to
January 2009. The first line is the correlation coefficient, and the second proveds its t-value,
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Emerging Equity and Commodities
Emerging Equity Index and Commodity Price Changes
Emerging Equities vs Commodities
Emerging Emerging Emerging Emerging Emerging
Emerging Emerging Emerging Emerging Emerging Emerging
Oil and Basic Consumer Health Consumer
Equity Industrials Telecoms Utilities Financials Technology
Gas Materials Goods Care Services
Emerging Equity Indexes (per sector) vs Oil Price Changes
0.085 0.074 0.098 0.100 0.068 -0.050 -0.091 0.051 0.051 0.076 0.196
Monthly
0.828 0.717 0.955 0.977 0.661 0.487 0.881 0.495 0.498 0.739 1.939
0.380
0.204 0.184 0.217 0.217 0.166 -0.140 -0.221 0.147 0.140 0.199
Quarterly
1.140 1.027 1.219 1.219 0.922 0.776 1.243 0.814 0.776 1.114 2.251
0.247 0.266 0.282 0.265 0.269 -0.147 -0.220 0.154 0.135 0.242 0.163
Yearly
0.625 0.675 0.720 0.673 0.685 0.364 0.552 0.381 0.333 0.611 0.404
Emerging Equity Indexes (per sector) vs Copper Price Changes
0.030 0.042 0.015 0.033 0.024 0.069 0.045 -0.009 0.009 0.019 0.145
Monthly
0.295 0.410 0.143 0.318 0.237 0.675 0.436 -0.090 0.083 0.189 1.425
0.360
0.117 0.148 0.082 0.108 0.078 -0.164 -0.145 0.045 0.054 0.106
Quarterly
0.647 0.817 0.452 0.596 0.429 0.912 0.801 0.248 0.296 0.584 2.112
0.178 0.217 0.106 0.166 0.134 -0.200 -0.251 0.049 0.050 0.177 0.462
Yearly
0.443 0.544 0.260 0.414 0.332 0.501 0.635 0.119 0.123 0.441 1.278
Emerging Equity Indexes (per sector) vs Commodity Price Index Change
0.125 0.124 0.110 0.138 0.112 0.114 0.136 0.080 0.097 0.120 0.260
Monthly
1.221 1.214 1.071 1.352 1.092 1.108 1.331 0.775 0.941 1.170 2.606
0.232 0.246 0.210 0.235 0.194 0.193 0.243 0.159 0.170 0.224 0.460
Quarterly
1.304 1.388 1.178 1.325 1.084 1.080 1.374 0.885 0.942 1.262 2.840
0.545
0.352 0.387 0.293 0.353 0.295 0.233 0.398 0.210 0.246 0.363
Yearly
0.923 1.028 0.751 0.925 0.757 0.587 1.061 0.525 0.621 0.953 1.592
Note: Benchmarks for Emerging Market Equity Indexes from Thomson from January 2001 to
January 2009. The first line is the correlation coefficient, and the second proveds its t-value,
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Summary: Oil
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Summary: Copper
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Remarks
U.S. bonds provide hedging
against oil/copper for high
maturities.
Emerging bonds show low
correlation and positive with
oil/copper, with exceptions.
Global equities and Emerging
equities show a positive beta with
commodities.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Conclusion
Principles of portfolio theory apply for SWF, but non-financial assets should be considered.
The SWF decision making problem can be modeled as optimal asset allocation with endowed,
non-tradable wealth.
Allocations can be separated: an optimal growth portfolio and an oil price risk hedging
portfolio. Countries need to look at the commodity fluctuations in the long run
What drives the optimal asset allocation for a SWF over time? The fraction of risky assets is
driven by financial wealth relative to resource wealth.
For young SWF where financial wealth is low relative to resource wealth a more risky asset
allocation is optimal. Young SWFs need to invest more aggresively in the beginning, before
shifting to non-risky assets. Comparable with real data. Mature SWFs with large assets
relative to natural resources should dial back their risks.
Investment in uncorrelated sectors with commodity are beneficial for reducing volatility.
Domestic spending may be beneficial under these circumstances. Implication for SWFs and
domestic investment.
New asset classes (e.g. infrastructure) can provide risk reduction.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Next step: Asset Allocation Chile
Implement standard dynamic
programming for optimal
extraction using Bellman
equation where
1
ft ξt −φξt2 +
Vt = Max Vt+1 (ot −ξt )
1+r
ft : Projected oil price for period t.
ξt : is the level of extraction.
ft ξt : Copper revenues.
ot : State variable (copper rvs)
Calibrate for Chile:
Copper reserves= 77 millions tmf
in reserves (2008)
Copper price= 6500 U$/MT -
A.M. OFFICIAL ME-Copper
Current extraction= 1,665 million
tmf per year.
Copper price growth=3.56%
Risk free rate=4%
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds

Introduction and Facts
Theoretical Framework
Implementation
Conclusion
Thank you
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds