Strategic Asset Allocation for Endowment Funds

Independent Auditor’s Report

The current structural low-interest environment is encouraging institutional investors to rethink their asset allocation strategies and increase their exposure towards alternative asset classes, such as real estate and infrastructure. This is particularly the case for insurance companies, which come under pressure to reduce their investment in currently low-yielding government bonds, given the high interest rate guarantees associated with obligations from existing life insurance contracts. As a consequence, insurance companies may be forced to rearrange their assets appropriately. However, the forthcoming Solvency II Directive could counteract this scenario. After Solvency II comes into effect, insurance companies will be subject to higher capital requirements aimed at ensuring insurance-protection even in the case of macroeconomic shocks. The Solvency Capital Requirements (SCR) varies by asset class and is determined by the Solvency II standard formula, which also determines the methodology used to aggregate the respective required equity position. Therefore, it may become necessary for insurers to minimize the SCR by means of their asset allocation, depending on their capitalization, profitability and the general competitive dynamics. If the results of such an optimization are not in accordance with those of conventional optimal asset allocation, Solvency II will lead to inefficient capital allocation in practice. As a result, the portfolio risk would increase, which contradicts the original purpose of the regulation. Furthermore, changing investment behavior from insurers might also have direct effects on the pricing and the product range offered on the capital markets, e.g. for real estate investments.We therefore analyze, whether the Solvency II standard formula could indeed cause the abovementioned incentive incompatibility with respect to the asset allocation process. The main focus lies on the potential shift of direct real estate and direct infrastructure weights within the portfolios of insurers under the Solvency II regime.

The case for alternative unlisted investments is based on offering stable and high income yields, low market volatility and enhanced riskadjusted returns. Such investment attributes have enticed increased allocation of capital to property and infrastructure investments in Australia. In turn, there has been a proliferation of investment products in the alternative space by fund managers. This paper examines the proposition of an investment framework which offers a combined allocation to alternatives investments; specifically to unlisted property and infrastructure. This is based on the premise that investment performance can be enhanced via diversification benefits and by managing and mitigating the joint risk pertaining to both assets classes. The concept of jointly allocating across alternative asset classes is supported via the construction of efficient portfolio frontiers. A frontier with both unlisted property and infrastructure delivers a superior enhanced investment outcome than frontiers with individual exposure to alternative asset classes. Furthermore, an investment framework is presented which offers an integrated process which jointly considers the relative risk/return value proposition and portfolio construction issues across alternative asset classes.

Does it pay to pay performance fees? Empirical evidence from Dutch pension funds

This paper provides the methodology of estimating the risk-return relationship of alternative asset investments within the mean-variance framework. While conducting strategic asset allocation, most of the institutional investors do not take into account the risk-return relationship of alternative assets, or use arbitrary policy numbers that do not properly reflect the characteristics of alternative assets. This paper borrows the concept of reference portfolio in developing the methodology of estimating the risk-return relationship of alternative investments. The reference portfolio is the benchmark portfolio used in strategic asset allocation by pension funds. This can serve as the opportunity costs of alternative investments. We use the realized IRR’s from actual investments, and estimate the risk-return characteristics of alternative investments. We find that by properly estimating the mapping relationship between the reference portfolio and alternative asset classes, we can incorporate the risk-return profile of these non-market assets within the mean-variance framework together with the other traditional asset classes.

Real estate is an important asset class, but what specifically does real estate contribute to improve diversified stock–bond portfolios? The author decomposes real estate investment trust returns into their factor betas to show that real estate is a hybrid asset class, with returns explained by a rich mix of compensated risk factors plus uncompensated sector risk. He shows that the same is true for private real estate, but with the additional contribution to risk from misappraisals. It is the rich mix of common factors contained in real estate that can improve the Sharpe ratios of diversified, multiasset portfolios. He discusses the implications for asset allocation from the perspectives of mean–variance optimization of asset classes, the capital asset pricing model with efficient markets, and factor-based asset allocation. <b>TOPICS:</b>Real estate, portfolio construction

Because the 60–40 allocation is very simple to implement (subject to rebalancing rules)—and because it has generated strong absolute and relative risk-adjusted returns over time (i.e., it had a significantly higher Sharpe ratio than did an allocation to either equities or bonds alone)—it has long been a benchmark asset allocation method for endowment and pension fund managers. Thus, it is likely that endowment and pension funds will perform well if the 60–40 allocation performs well (and vice versa). This article provides evidence that the drivers of US equity market returns have changed over time. Specifically, while equity returns are more rate driven in earlier periods (i.e., lower rates are better for equities), they are more economically driven in more recent periods (i.e., a stronger economy is more compatible with equities). Therefore, if the US economy remains sluggish and rates are still low, the 60–40 allocation is likely to perform more poorly in the future than in the past, and, by extension, so are endowment and pension funds. <b>TOPICS:</b>Foundations &amp; endowments, portfolio theory, portfolio construction, performance measurement <b>Key Findings</b> ▪ In the future, endowment and pension fund returns will be primarily driven by the performance of risk assets. It follows that endowment and pension funds will only be able to achieve their objectives if the future environment is favorable to equities. An analysis of recent years’ returns reveals that the only environment in which equities are expected to perform well in the future is one of sustained strong economic growth coupled with accommodative monetary policy. In the absence of such an environment, endowments and pension funds may be unable to sustain planned spending levels. ▪ Individual investors’ future asset allocation choices will necessarily involve real trade-offs. In the past, the 60-40 allocation generated equity-like returns with bond-like volatility, so investors who chose the 60-40 allocation had ‘it all’: high returns and low volatility. However, in the future, the 60-40 allocation will likely generate sharply lower volatility but also meaningfully lower returns, relative to a 100% equity allocation. Investors focused on reducing risk may still find the 60-40 allocation useful, but investors with funding needs and higher returns targets will need to increase their allocation to risk assets. ▪ Individual investors allocating to illiquid risk assets need to be aware that endowments and pension funds will likely significantly increase their allocations to illiquid risk assets in an attempt to achieve their required returns. The magnitude of these institutional inflows may lead to lower returns for illiquid risk assets, as is often the case when large amounts of capital chase a more limited opportunity set.

In this article, the author creates a tool to help investors categorize an investment in a diversifying, alternative asset class. Using standard portfolio mathematics, he obtains a precise analytical solution to the proportion of the new investment that should be considered stock-like and the proportion that should be considered bond-like. These proportions rely on whether the alternative asset is funded from stocks or bonds. The resulting formula can readily incorporate the parameter ambiguity that often surrounds alternative assets. While the approach is reductionist, categorical thinking about alternative assets can help investment decision making, particularly by non-specialists. <b>TOPICS:</b>Security analysis and valuation, real assets/alternative investments/private equity, portfolio construction

The authors conduct an analysis of diamonds as an investable asset. They discuss the literature of alternative asset class investing and how diamonds fit into this investment framework. After examining the correlation of diamonds with several financial indices and a set of metal commodities, they demonstrate the ability of diamonds to enhance portfolio returns through diversification. They conclude by demonstrating the ability of a diamond return index to provide superior risk-adjusted returns. <b>TOPICS:</b>Commodities, portfolio construction, performance measurement

This thesis examines the topic of sovereign wealth fund (SWF) sustainability and its relationship with a country’s strategic resources. When the relevant literature on SWFs is reviewed, it is found that these topics are seldom examined. There are studies that focus on the financial performance of SWFs, but the sustainability of SWFs did not attract enough attention in the academic and business circles. This thesis fills this conceptual gap by developing measures of SWF sustainability and country-level strategic resources and then connecting them in a novel conceptual model of SWF sustainability. Towards this aim, three sustainability measures are identified. These measures are investments in alternative asset classes, the employment of external fund managers, and spending on social and environmental projects and causes. When these measures are reviewed through different theories, it is found that the resource-based theory can justify the employment of external fund managers, while the natural resource-based theory justifies the spending on social and environmental projects and causes, and the portfolio diversification theory can be used to justify the investments in alternative asset classes. The resource-based theory is also used in establishing the new conceptual model of SWF sustainability. This theory argues that the resources and capabilities that companies possess matter for their competitive advantage and sustainability. However, this theory does not take country-level factors into account. In this context, the thesis extends the resourcebased theory to the SWFs domain and incorporate the country-level strategic resources into the new model. So, this model of SWF sustainability postulates that countries’ strategic resources would positively affect their SWFs’ sustainability. 3 To test the relevant research hypotheses of the SWF sustainability model, the thesis uses firm-level data from 56 SWFs, covering the period of 2007-2017. There are four independent variables (i.e., the human development index, the innovation index, the reputation index, and FDI inflows) representing the country-level strategic resources and three dependent variables (i.e., the dummy for alternative asset classes, the dummy for employing external fund managers, and the dummy for social and environmental expenditures). The relationships between these dependent and independent variables are examined quantitatively using descriptive statistics, pairwise correlations, scatter plots, t-tests, and finally logistic and ordered logistic regressions. The results from different quantitative methods usually support each other. Overall, these quantitative results show that there is strong empirical support for the first two dimensions of SWF sustainability, which are investing in alternative asset classes and employing external fund managers. However, empirical evidence for the third SWFs dimension is relatively weak.

The mean???variance model is widely acknowledged as the foundation of portfolio allocation because it provides a framework for analyzing the trade-off between risk and return for gaining diversification benefits. Despite the well-known shortcomings of the model, it is often the starting point for making asset allocation decisions. In this article, the authors briefly review mean???variance optimization and approaches for resolving its limitations by demonstrating backtest results on asset allocation. Feedback from asset managers is also included to explain how optimization methods are applied in practice.

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods in which diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: It is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article, the authors highlight a fundamental flaw in the way some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples in which both assets perform poorly, they ignore instances of successful diversification (i.e., periods in which one asset’s gains offset the other’s losses). The authors propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full-sample correlation. The authors review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes. <b>TOPICS:</b>Portfolio theory, portfolio construction, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ There is strong empirical evidence that asset class correlations are asymmetric, which poses complications in portfolio construction. ▪ Investors prefer diversification when a portfolio’s main growth engine performs poorly and unification when it performs well. ▪ To measure correlation asymmetry caused by nonnormality, investors must adjust for changes in correlation that arise mathematically when part of a sample is excluded. ▪ Unlike prior research, investors should condition correlations on the performance of a single asset, not two assets.

In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated. We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks. The framework will be useful for financial risk management, portfolio construction, and asset allocation.

Following the recent financial crisis, the need for diversification cannot be over emphasized. This notwithstanding, some concerns have been raised regarding the efficacy of the conventional asset allocation methods. These traditional strategies encompass equal weighting, minimum variance, and mean- variance optimization based on modern portfolio theory (MPT). MPT remains the most widely used method despite its inherent estimation error in the determination of expected returns and correlations. Portfolio construction using MPT in real estate presents similar challenges where naive diversification is predominantly used to diversify portfolios. Thus, these conventional strategies have been questioned especially in the wake of the financial crisis due to poor performance of portfolios.This paper introduces an alternative strategy of asset allocation called 'risk parity' to real estate portfolios by using the framework from mainstream finance. In contrast with the traditional strategies, the aim of risk parity is equal risk allocation across asset classes in a portfolio. To achieve the broad aim, the paper relies heavily on a systematic analysis of listed as well as direct real estate to ascertain whether risk parity provides a better alternative to the traditional allocation strategies employed in the real estate market. The paper argues that compared to the traditional allocation strategies, a risk parity based portfolio is likely to produce superior risk-reward tradeoff. Utilizing risk parity strategy in constructing a portfolio does not require the creation of expected return assumptions as only the asset class covariances are needed. These covariances can be more accurately estimated from historical data than expected returns. In lower risk portfolios, leverage can be employed in order to increase return expectation while derivatives can be used as a way to attain desired market exposures more safely and inexpensively.

Investors have been increasing their allocations to private assets, seeking higher returns and better portfolio diversification. However, as this allocation increases, the liquidity characteristics of their portfolios change. The authors create a framework that links bottom-up private asset investing with top-down asset allocation. Private asset cash flows are consistently modeled together with public asset returns and risk that, in turn, drive portfolio construction. This helps investors analyze how allocations to illiquid private assets, in combination with their commitment strategy, may affect their portfolio’s ability to respond to various liquidity demands. By measuring the potential trade-off among asset allocations, total portfolio performance, and the frequency of certain liquidity events with different severities, this framework can help investors quantify the interaction between their portfolio structure and performance and formalize their decision making around portfolio liquidity choices. <b>TOPICS:</b>Real assets/alternative investments/private equity, portfolio construction, performance measurement, tail risks <b>Key Findings</b> ▪ We present a framework to systematically address various liquidity issues for multi-asset portfolios. ▪ Liquidity requirements have a clear impact on portfolio structure and expected performance. ▪ Commitment pacing of private investments plays a crucial role in the trade-off between liquidity and performance.