Asset Allocation For Sovereign Wealth Funds
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Présentation réalisée en janvier 2009 dans le cadre du séminaire de recherche de DEV.

Présentation réalisée en janvier 2009 dans le cadre du séminaire de recherche de DEV.

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Asset Allocation For Sovereign Wealth Funds Presentation Transcript

  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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. quot;Commodity and -5.9 South Africa 16.8 10.9 -6.4 Turkey 15.8 9.4 non-commodity SWFsquot; Deutsche -7 Ukraine 25.9 18.9 Bank WP, quot;Fonds souverains et China 0.3 43 42.7 économie du développementquot; 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 quot;Sovereign Thailand 19.2 38.5 19.3 Development Fundsquot;, Revue Source: Credit Suisse, quot;Are EM funding needs driving financial marketnprices?quot;, Dec. 08. d’Economie Financière. Avendano, Reisen, Santiso. quot;Macro Management of Commodity Boomsquot;. OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
  • 5. 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 quot;as a wholequot; 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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 quot;controlledquot; 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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 quot;alphaquot;, 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
  • 15. Introduction and Facts Theoretical Framework Implementation Conclusion Implementation: quot;Commodity-Asset Betasquot; 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
  • 16. 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
  • 17. 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
  • 18. 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
  • 19. Introduction and Facts Theoretical Framework Implementation Conclusion Summary: Oil OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
  • 20. Introduction and Facts Theoretical Framework Implementation Conclusion Summary: Copper OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. Introduction and Facts Theoretical Framework Implementation Conclusion Thank you OECD Development Centre Asset Allocation for Commodity-Based Sovereign Wealth Funds