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Présentation réalisée en janvier 2009 dans le cadre du séminaire de recherche de DEV.
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Introduction and FactsTheoretical Framework
ImplementationConclusion
Asset Allocation for Commodity-BasedSovereign 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 FactsTheoretical Framework
ImplementationConclusion
Outline
1 Introduction and Facts
2 Theoretical Framework
3 Implementation
4 Conclusion
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
INTRODUCTIONMotivation and some Basic Facts
SWF concept: investment vehiclewith high foreign asset exposure,nonstandard liabilities and long(intergenerational) time horizon.
Assets: Long-term, active,diversified investments. Main assetclasses: bonds, equity, alternatives.
Size: Low commodity-price/exportsscenario has affected some, but stillresilient (11.6 tr to 9.5 tr estimateNov. 08)
Incentives and purpose
Stabilisation vs. SavingsPreventive/oriented strategy
Challenges for current SWFs
National strategy vs.commercial returnPassive vs. active policyStabilizing financial marketsvs. market jeopardy
Source: COFER database and International Financial Statistics
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
INTRODUCTIONContext, related research at the Centre
Today: Financing needs for the shortrun are important. Dry global liquidityscenario and contraction ofcross-border capital flows
Home bias / Regional bias →
Challenge to stimulate domesticeconomies. Affects allocation?
Standard portfolio approach (CAPM,VaR, etc.) insufficient for activemanagement.
Some related work at the Centre:
H. Reisen. "Commodity andnon-commodity SWFs" DeutscheBank WP, "Fonds souverains etéconomie du développement"Revue d’Economie Politique.J. Santiso "SovereignDevelopment Funds", Revued’Economie Financière.Avendano, Reisen, Santiso."Macro Management ofCommodity Booms".
External
financing
needs 2009
FX Reserves
(Dec 08)
Gap between reserves and
financing needs
Argentina 6.4 12.7 6.3
Brazil 6.7 12.5 5.8
Chile 18.6 12.1 -6.5
Colombia 8 9.7 1.7
Ecuador 6.8 11.3 4.5
Mexico 6.1 7.7 1.6
Hungary 23.3 13.8 -9.5
Kazakhstan 18.4 16.3 -2.1
Nigeria 2.7 24.3 21.6
Poland 19.7 11.9 -7.8
Russia 9.4 26.2 16.8
South Africa 16.8 10.9 -5.9
Turkey 15.8 9.4 -6.4
Ukraine 25.9 18.9 -7
China 0.3 43 42.7
India 8.8 71.9 63.1
Indonesia 10.3 10.8 0.5
Korea 21.1 24.3 3.2
Malaysia 4.5 46.6 42.1
Thailand 19.2 38.5 19.3
Source: Credit Suisse, "Are EM funding needs driving financial market\nprices?", Dec.08.
Financing Needs and International Reserves -2009
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
How are commodity-SWF assets allocated?Considerations for Asset Allocation
1 Traditional reserves approach
Multiple, conflicting investmentobjectives: liquidity, peg, foreigndebt, trade.Tranching facilitates management.Difficult to optimise "as a whole"
2 Country-specific criteria:
Size of reservesTransparency or accountabilityPurpose of holding reservesObjectives in managing reservesConstraints and risk-return profile
3 Market related criteria
Structural changesCyclical changes
Criteria for public sector (Reisen2008, van der Ploeg 2007):
Depleting: Hotelling,steady-stateSaving: Hartwick,commodity priceDomestic investment:Excess return, constructionprice smoothingRetiring debt: Excess costof public debt over globalreturn
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
Variables/constraints for SWF 6= reservesObjectives and Constraints
Constraints
Non-financial risk:reputation, operationCurrency/Asset classexposureDerivative usageInstitutional: Frequencydisclosure, benchmark
Risk-return preferences
Time HorizonUnit of accountNominal vs real returnFinding distribution by returnor risk
Risk-return expectations
Forward-looking of risk,return, asset classesSome approaches
Markowitz mean-varianceMonte Carlo simulationAsset-LiabilityOECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
Approaches to SWF Asset AllocationStudies on Reserves, Asset Management and SSA
1 Management of Reserves
International currencies → Linder (1969), Hartmann (1998),Eichengreen (2005).Jeanne and Rancière (2008) → Optimal level of reserves foremerging countries.Portes et al. (2006) → Optimal Currency Shares inInternational Reserves
2 Portfolio Choice
Dynamic stochastic optimisation (Claessens and Kreuser2004).Monte Carlo Simulation → (Weiberger and Golub 2007)Portfolio choice → Campbell 2003, Scherer 2008.
3 Contingent Claims Approach
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 FactsTheoretical Framework
ImplementationConclusion
SWFs and Foreign InvestmentConceptual IssuesLiterature on Asset Allocation for Sovereign Funds
Outline
1 Introduction and Facts
2 Theoretical Framework
3 Implementation
4 Conclusion
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
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 assetsare considered.
Key factor: exploit the correlation between financial and non-financial assets toreduce overall SWF risk.
Advantages:
Timely for resource-rich economiesAddressing the lack of data for SWF asset allocation studies.Similar to asset-liability management approach (both sides of the balancesheet).Defined objective: reduce total wealth volatility.Non-normal returns in short run "controlled" by SWF long-term investmentapproach (utility function)
Extensions:
Application for other commodity fundsApply to a multi-asset context: alternatives, other commodities, infrastructure,real estateLook at exposure to emerging markets
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
The Sovereign investor problemCase 1: Investing in one risky asset
The decision making problem The SWF can invest its financial wealth into a singleasset or cash.
r̃a ∼ N(µa, σ2a)
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:
r̃o ∼ N(µo, σ2o)
and correlate positively with asset returns, i.e.
Cov(ra, r̃o) = ρa,o > 0
.
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 governmentbudget. Therefore:
r̃ = θwr̃a + (1 − θ)r̃o
with 1 − w representing cash holding that carries a zero risk premium and no riskin one period.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
The Sovereign investor problemDecomposing demand
The SWF manager is charged to maximize the utility of total governmentwealth rather than narrowly maximizing the utility for its direct assetsunder management. Utility defined as a (quadratic) function of uncertainwealth. The goverment seeks to maximize the function:
Maxw(θwµa −
λ
2
[θ2w2σ2
a + (1 − θ)2σ2o + 2wθ(1 − θ)ρσaσo
])
Taking first order conditions and solving for w , the optimal assetallocation for a resource based SWF:
w∗ = w∗
s + w∗
h =1θ
µa
λσ2a−
1 − θ
θ
ρσo
σa
Total demand = w∗ = w∗
s︸︷︷︸
Speculative demand
+ w∗
h︸︷︷︸
Hedging demand
In the case of uncorrelated assets and commodity resources the optimalsolution is a leveraged position (with factor 1/θ) in the asset withmaximum Sharpe-ratio (reward/variance).
Observation 1:
Demand for risky assets can be descomposed between speculative andhedging. Risk-return (Sharpe) criteria is not the only one. The optimal weightof risky assets is independent from his wealth level.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
Two types of DemandHegding vs Speculative demand
Hedging demand: the desirability of the asset does not only depend on Sharpe-ratio butalso on its ability to hedge out unanticipated shocks to commodity wealth. Hedgingdemand is given as the product of leverage and commodity asset beta,
βo,a =ρσo
σa
.
This is equivalent to the slope coefficient of a regression of (demeaned) asset returnsagainst (demeaned) commodity returns of the form:
(ro − ro) = βo,a(ra − ra) + ε
Positive correlation between asset and commodity price risk increases the volatility oftotal 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 theallocation to the risky asset.
Observation 2:
Corelation patterns between commodity prices and other assets may depict the bestinvestment profile for the SWF. The best investment profile for SWFs balances returns withhedging against commodity prices. Specific sectors provide hedging against commodityprices.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
Growth and Hedge AssetsCase 2: Optimal portfolio with several assets
Extended case with two assets: one asset hedging asset (i.e. it show negativecorrelation) and another asset provides growth orthogonal to commodity wealthchanges. The setup if summarized with the following distribution:
r̃g ∼ N(µg , σ2g), r̃h ∼ N(µh, σ
2h)
where rg and rh stand for the return of growth and hedge assets with µg > µh.
The correlation assumptions are:
Cov(rh, rg) = ρh,gσhσg > 0, Cov(rh, ro) = ρh,oσhσo < 0, Cov(rg, ro) = 0
The government budget evolves to:
r̃ = θ[wg r̃g + wh r̃h] + (1 − θ)r̃o
where utility is given by
u = E(r̃ ) −λ
2
[E(r̃2) − E2(r̃)
]
And solve for wg and wh:
w∗
g =µg − βg,h · µh
λθ(1 − ρ2g,h)σ
2g−
1 − θ
θ
βg,hρh,oσoσh
(1 − ρ2g,h)
w∗
h =µh − βh,g · µg
λθ(1 − ρ2g,h)σ
2h
−1 − θ
θ
βo,h
(1 − ρ2g,h)
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Tackling the ProblemBasic Model of Asset AllocationOptimal Allocation between Growth Assets and Hedge Assets
Demand for the growth asset can be split again into speculative demandand hedging demand:
Speculative demand will depend on its "alpha", µg − βg,h · µh,versus the hedge asset, i.e. Beta, βg,h, adjusted excess returndivided by the risk not explained by the hedge asset returns.The term ρ2
g,h can interpreted as the R2 of a regression of hedgeversus growth asset returns. If the indirect correlation is set to zero,i.e. ρr ,g = 0 then:
w∗
g =µg
λθσ2g
w∗
h =µh
λθσ2h
− ρh,oσo
σh
(1 − θ)
θ
Observation 3:
The growth asset is entirely driven by the Sharpe-ratio while the hedge assetcombines both speculative and hedge demand.
How does hedge demand change with θ?
dw∗
h
dθ=
−µh + λρh,oσhσo
λσ2hθ2
< 0
Observation 4:
Economies with falling levels of commodity resources should be moreconservative in their investments.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Implementation: "Commodity-Asset Betas"Bond Benchmark and Commodity Price Changes
US Benchmark
DS 2 Years
US Benchmark
DS 3 Years
US Benchmark
DS 5 Years
US Benchmark
DS 7 Years
US Benchmark
DS 10 Years
US Benchmark
DS 30 Years
0.007 0.003 -0.001 -0.003 -0.005 -0.021
0.108 0.050 -0.017 -0.051 -0.080 -0.314
0.013 0.007 -0.002 -0.001 -0.008 -0.027
0.201 0.105 -0.026 -0.013 -0.114 -0.409
0.110 0.096 0.071 0.065 0.048 0.020
1.666 1.455 1.074 0.976 0.729 0.293
0.006 0.005 0.001 -0.002 -0.009 -0.031
0.096 0.074 0.013 -0.024 -0.142 -0.473
0.008 0.004 -0.008 -0.004 -0.019 -0.038
0.118 0.057 -0.117 -0.063 -0.293 -0.574
-0.044 -0.048 -0.078 -0.077 -0.116 -0.133
-0.668 -0.716 -1.172 -1.157 -1.748 -2.018
-0.033 -0.037 -0.040 -0.036 -0.036 -0.054
-0.494 -0.559 -0.595 -0.547 -0.549 -0.817
-0.044 -0.052 -0.056 -0.039 -0.042 -0.070-0.659 -0.782 -0.847 -0.593 -0.626 -1.053
-0.117 -0.128 -0.182 -0.180 -0.213 -0.252
-1.774 -1.944 -2.787 -2.745 -3.285 -3.908
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 itst-value,
Monthly
Quarterly
Yearly
Monthly
Quarterly
Yearly
Monthly
Asset Returns vs Oil Price Changes
Asset Returns vs Copper Price Changes
Asset Returns vs Commodity Index Change
Correlation U.S Benchmark Bonds with Commodity Price Changes
Quarterly
Yearly
Monthly data → No pattern oncorrelations. Reducing datafrequency shows negativecorrelations.
Correlation and significance risewhen decreasing the frequency.Global equities provide no hedgeagainst oil price changes.
Sector equity indexes →
Significant negative correlationfor two sectors (defensiveconsumer and health care) thattend to do well when theeconomy does badly.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Global Equities and CommoditiesGlobal Equities and Commodity Price Changes
World
Global
Equity
World Oil
and Gas
World Basic
Materials
World
Industrials
World
Consumer
Goods
World Health
Care
World
Consumer
Services
World
Telecoms
World
Utilities
World
Financials
0.074 0.046 0.063 0.077 0.059 0.038 0.090 0.082 0.024 0.068
1.116 0.686 0.954 1.167 0.895 0.576 1.361 1.238 0.358 1.0250.152 0.102 0.150 0.161 0.127 0.084 0.177 0.156 0.073 0.155
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
4.873 2.680 3.302 4.943 3.691 2.407 5.299 7.882 1.745 3.673
0.065 0.052 0.062 0.073 0.044 0.040 0.086 0.002 0.013 0.100
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 0.123 0.096 0.191 0.033 0.067 0.228
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
3.270 2.611 3.355 3.866 2.726 0.893 3.626 0.901 0.863 4.878
0.144 0.110 0.142 0.156 0.104 0.115 0.178 0.012 0.071 0.210
2.190 1.666 2.159 2.368 1.566 1.733 2.721 0.179 1.068 3.2360.265 0.218 0.261 0.279 0.190 0.207 0.306 0.057 0.147 0.358
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
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,
Monthly
Quarterly
Yearly
Global Equities and Commodities
Monthly
Quarterly
Yearly
Monthly
Quarterly
Yearly
Global Equity Indexes (per sector) vs Copper Price Changes
Global Equity Indexes (per sector) vs Commodity Price Index Change
Global Equity Indexes (per sector) vs Oil Price Changes
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Emerging Bonds and CommoditiesEMBI and Commodity Price Changes
Brazil Chile China Indonesia Kazakhstan Mexico Malaysia Morocco Poland Russia South Turkey Thailand Venezuela
-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
-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 1.0070.009 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
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 0.341-0.252 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
-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
-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
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
-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
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
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 1.6020.062 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
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
-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,
Emerging Market Bonds with Commodity Price Changes
Bond Returns vs Oil Price Changes
Bond Returns vs Copper Price Changes
Bond Returns vs Commodity Index Change
Yearly
Monthly
Quarterly
Yearly
Monthly
Quarterly
Monthly
Quarterly
Yearly
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Emerging Equity and CommoditiesEmerging Equity Index and Commodity Price Changes
Emerging
Equity
Emerging
Oil and
Gas
Emerging
Basic
Materials
Emerging
Industrials
Emerging
Consumer
Goods
Emerging
Health
Care
Emerging
Consumer
Services
Emerging
Telecoms
Emerging
Utilities
Emerging
Financials
Emerging
Technology
0.085 0.074 0.098 0.100 0.068 -0.050 -0.091 0.051 0.051 0.076 0.196
0.828 0.717 0.955 0.977 0.661 0.487 0.881 0.495 0.498 0.739 1.939
0.204 0.184 0.217 0.217 0.166 -0.140 -0.221 0.147 0.140 0.199 0.380
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
0.625 0.675 0.720 0.673 0.685 0.364 0.552 0.381 0.333 0.611 0.404
0.030 0.042 0.015 0.033 0.024 0.069 0.045 -0.009 0.009 0.019 0.145
0.295 0.410 0.143 0.318 0.237 0.675 0.436 -0.090 0.083 0.189 1.425
0.117 0.148 0.082 0.108 0.078 -0.164 -0.145 0.045 0.054 0.106 0.360
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
0.443 0.544 0.260 0.414 0.332 0.501 0.635 0.119 0.123 0.441 1.278
0.125 0.124 0.110 0.138 0.112 0.114 0.136 0.080 0.097 0.120 0.260
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
1.304 1.388 1.178 1.325 1.084 1.080 1.374 0.885 0.942 1.262 2.840
0.352 0.387 0.293 0.353 0.295 0.233 0.398 0.210 0.246 0.363 0.545
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,
Quarterly
Yearly
Emerging Equities vs Commodities
Emerging Equity Indexes (per sector) vs Oil Price Changes
Emerging Equity Indexes (per sector) vs Copper Price Changes
Emerging Equity Indexes (per sector) vs Commodity Price Index Change
Monthly
Quarterly
Yearly
Monthly
Monthly
Quarterly
Yearly
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Summary: Oil
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Summary: Copper
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Remarks
U.S. bonds provide hedgingagainst oil/copper for highmaturities.
Emerging bonds show lowcorrelation and positive withoil/copper, with exceptions.
Global equities and Emergingequities show a positive beta withcommodities.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
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 hedgingportfolio. 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 isdriven by financial wealth relative to resource wealth.
For young SWF where financial wealth is low relative to resource wealth a more risky assetallocation is optimal. Young SWFs need to invest more aggresively in the beginning, beforeshifting to non-risky assets. Comparable with real data. Mature SWFs with large assetsrelative 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 anddomestic investment.
New asset classes (e.g. infrastructure) can provide risk reduction.
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Next step: Asset Allocation Chile
Implement standard dynamicprogramming for optimalextraction using Bellmanequation where
Vt = Max(
ftξt−φξ2t
)
+1
1 + rVt+1(ot−ξt)
ft : Projected oil price for period t.
ξt : is the level of extraction.
ftξt : Copper revenues.
ot : State variable (copper rvs)
Calibrate for Chile:
Copper reserves= 77 millions tmfin reserves (2008)
Copper price= 6500 U$/MT -A.M. OFFICIAL ME-Copper
Current extraction= 1,665 milliontmf per year.
Copper price growth=3.56%
Risk free rate=4%
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
Introduction and FactsTheoretical Framework
ImplementationConclusion
Thank you
OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds