- published on 13/05/2013
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In research conducted at EDHEC-Risk Institute as part of the research chair on asset liability management (ALM) techniques for sovereign wealth funds (SWF) management supported by Deutsche Bank, we propose a quantitative dynamic asset allocation framework for sovereign wealth funds, modelled as large long-term investors that manage fluctuating revenues typically emanating from budget or trade surpluses in the presence of stochastic investment opportunity sets. The optimal asset allocation strategy takes into account the stochastic features of the sovereign fund endowment process (where the money is coming from), the stochastic features of the sovereign fund’s expected liability value (what the money is going to be used for), and the stochastic features of the assets held in its portfolio.
We find that the optimal asset allocation strategy for a sovereign state fund involves a state-dependent allocation to three building blocks, a performance-seeking portfolio (PSP), typically heavily invested in equities, an endowment-hedging portfolio (EHP), customised to meet the risk exposure in the sovereign wealth fund endowment streams, and a liability-hedging portfolio (LHP), heavily invested in bonds for interest rate hedging motives, and in assets exhibiting attractive inflation-hedging properties, when the implicit or explicit liabilities of the SWFs exhibit inflation indexation, as well as separate hedging demands for risk factors impacting the investment opportunity set.
While the first PSP building block is the standard highest riskreward component in any investor’s portfolio, the EHP and LHP building blocks must be customised to meet the tailored needs of each specific sovereign wealth fund.
In an application to oil-based sovereign funds with inflationlinked benchmarks, we conduct an empirical analysis of the oil- and inflation-hedging properties of several traditional and alternative asset classes that can be used as ingredients within this building block using a restricted vector autoregressive (VAR) model. Overall, it appears that the development of an assetliability management analysis of sovereign wealth funds has potential important implications in terms of the emergence of new forms of financial engineering techniques for the design of customised building blocks aiming at facilitating the implementation of genuinely dedicated ALM and risk management solutions for these longterm investors. The PSP/EHP/LHP approach can in fact be seen as the extension to sovereign wealth funds of the liability-driven investing (LDI) paradigm developed in the pension fund industry.
In terms of implementation, a number of challenges remain, including the need to reconcile the top-down asset allocation decisions with bottom-up security selection decisions. Indeed, the asset allocation decisions analysed in our research relate to the design of the long-term strategic allocation for SWFs, with an associated optimal exposure to rewarded risk factors.