Multi-asset Portfolio Research Articles

Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches

The Black–Litterman model aims to enhance asset allocation decisions by overcoming the problems of mean-variance portfolio optimization. We propose a sample-based version of the Black–Litterman model and implement it on a multi-asset portfolio consisting of global stocks, bonds, and commodity indices, covering the period from January 1993 to December 2011. We test its out-of-sample performance relative to other asset allocation models and find that Black–Litterman optimized portfolios significantly outperform naïve-diversified portfolios (1/N rule and strategic weights), and consistently perform better than mean-variance, Bayes–Stein, and minimum-variance strategies in terms of out-of-sample Sharpe ratios, even after controlling for different levels of risk aversion, investment constraints, and transaction costs. The BL model generates portfolios with lower risk, less extreme asset allocations, and higher diversification across asset classes. Sensitivity analyses indicate that these advantages are due to more stable mixed return estimates that incorporate the reliability of return predictions, smaller estimation errors, and lower turnover.

The effectiveness of international diversification: whole markets versus sectors

A hedging approach is used to examine the effect of sectoral factors on the effectiveness of international diversification. By using data covering seven countries and various sectors, we find that international diversification is more effective when assets from developed markets only are used and when multiasset portfolios are used instead two-asset portfolios. The results also reveal that international diversification across whole markets is more effective than diversification across sectors. These results reflect the pattern of return correlation.

A Class of its Own? The Role of Sustainable Real Estate in a Conditional Value at Risk Multi-Asset Portfolio

Current research on sustainable property investment focuses mainly on property-level profitability of green buildings along with the development and implementation of eco-certifications. A second strand of studies investigates the company-level financial implications of corporate social responsibility agendas. This paper seeks to expand the current literature by analyzing the effect of Socially Responsible Investments (SRI) within a multi-asset portfolio optimization model. It also attempts to bridge the existing gap in the real estate literature between sustainability principles and investment analysis. To this aim, listed real estate companies with an active sustainability agenda, identified through the MSCI ESG database, represent the sustainable real estate asset class. Applying a number of robust optimization techniques, we establish empirically whether diversification benefits can be achieved by investing in companies with a proven track record in sustainability. The results of the study highlight the potential contribution of listed real estate companies with high sustainability ratings to an institutional investor´s portfolio taking into account differences in investment style and risk aversion.

Robust Risk Budgeting Algorithms in R

In this paper we reviewed some numerical algorithms, implemented in R language which solve the Risk Budgeting (RB) allocation problem. We demonstrated that the well known Sequential Quadratic Programming (SQP) whose objective function is not strictly convex, fails to converge for high dimensional baskets. On the contrary, the new explored algorithms tackle this issue by transforming the objective function into a strictly convex one. Amongst them, the ”Spinu Algorithm” proves to be the most robust and fastest algorithm, both for large equity and small multi-asset portfolios. Surprisingly, the promising Cyclical Coordinate Descent (CCD) algorithm proposed by Griveau et.al. (2013) which suits well for risk budgeting on a large basket of equity stocks, lacks robustness when performed on a multi-asset universe. Our results confirm that the ”Spinu Algorithm” is the most robust framework to implement risk-budgeting portfolios for any type of investment universe.

Performance and significance of UK-listed infrastructure in a mixed-asset portfolio

Purpose – This paper aims to examine the performance of UK-listed infrastructure over a unique investment period covering the global financial crisis and investigates the significance of UK infrastructure in a multi-asset portfolio. The analysis reveals the level of correlation of UK infrastructure with other major assets classes and substantiates the potential diversification benefits of including UK infrastructure within a mixed-asset portfolio. Design/methodology/approach – The study uses monthly investment return indices obtained from Thomson Reuters DataStream over a ten-year period (2001-2010). The paper analyzed the UK-listed infrastructure investment return characteristics including average annual return, annual risk, Sharpe indices, mean variance portfolio and maximum return portfolio and computes the efficient portfolio frontiers using the risk-solver optimization tool. Findings – The performance results show that UK infrastructure produced better risk-return trade-offs than those of UK property, private equity, hedge funds and UK stocks over 2001-2010. Overall, for the ten-year period, UK Water was the best performing asset class, outperforming all other asset classes having the highest Sharpe ratio of 0.75. Practical implications – Using the monthly return indices over the ten-year period, UK-listed infrastructure investment was found to play a consistently significant role in the optimality of mixed-asset portfolios. However, the diversification benefits were more return enhancing than risk reducing, offering investors a platform for matching investment objectives with expectations resulting from a better understanding of the characteristics of UK-listed infrastructure investments. Originality/value – As investors seek better understanding of the performance of infrastructure across the globe, with most previous studies focusing on Australia, USA and China, the paper makes significant contribution to the body of knowledge by focusing on UK, a promising investment space for infrastructure industry. Also, given the debate surrounding the emergence of infrastructure as a separate asset class, the paper particularly projects the potential benefits of investing in UK-listed infrastructure, offering investors a distinctive platform to launch into a vibrant asset class.

Semiparametric estimation of multi-asset portfolio tail risk

When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The estimator captures simultaneously the information contained in each individual asset return that composes the portfolio, and the interrelation between assets. Noticeably, the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases. The implementation is as easy for a large number of assets as it is for a small number. We estimate the probability distribution of large losses for the American stock market considering portfolios with ten, fifty and one hundred assets of stocks with different market capitalization. In either case, the approximation for the portfolio tail risk is very accurate. We compare our results with well known benchmark models.

Cross-market volatility index with Factor-DCC

This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, since it provides a simple way to hedge multi-asset portfolios with derivative contracts written on the CMIX.

International Real Estate Review

We test relative illiquidity, exemplified through a temporary lock-up, as a partial explanation for the gap between theoretical and empirical weights for real estate in a multi-asset portfolio. Since asset correlations are known to increase in bear markets, which reduce their diversification benefits, the ex-ante knowledge of a lock-up in an asset class that offers diversification benefits in bull markets (Hung et al., 2008) may reduce the optimal weight that an investor wishes to put in it ex-ante. By using dynamic multiperiod portfolio policies by Brandt and Santa-Clara (2006), and introducing a lock-up in line as per de Roon et al. (2009), we study the effects of a partial lock-up on the weight for REITs in a U.S. stock and bond portfolio. We find support for our prediction, in the form of lower weights for the illiquid asset once a lock-up is introduced.

Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies

Abstract An essential motive for investing in commodities is to enhance the performance of portfolios traditionally including only stocks and bonds. We analyze the in-sample and out-of-sample portfolio effects resulting from adding commodities to a stock-bond portfolio for commonly implemented asset allocation strategies such as equally- and strategically-weighted portfolios, risk-parity, minimum-variance as well as reward-to-risk timing, mean-variance and Black–Litterman. We analyze different commodity groups such as agricultural and livestock commodities that currently are critically discussed. The out-of-sample portfolio analysis indicates that the attainable benefits of commodities are much smaller than suggested by previous in-sample studies. Hence, in-sample analyses, such as spanning tests, might exaggerate the advantages of commodities. Moreover, the portfolio gains greatly vary between different types of commodities and sub-periods. While aggregate commodity indices, industrial and precious metals as well as energy improve the performance of a stock-bond portfolio for most asset allocation strategies, we hardly find positive portfolio effects for agriculture and livestock. Consequently, investments in food commodities are not essential for efficient asset allocation.

Diversification Management of a Multi-Asset Portfolio

It is a well-known fact in finance that classical mean-variance optimization often leads to highly concentrated portfolios. Giving equal weights to all portfolio assets will instead allow for maximum nominal diversification. More sophisticated ways of nominal diversification are the maximum diversification approach proposed by Choueifaty and Coignard (2008) or the equal weighting of total risk contributions known as "risk parity". Instead of looking for nominal diversification, investors may prefer a diversification of the risk factors that drive portfolio returns. In recent papers, risk factors have been modelled by principal components following Partovi and Caputo (2004). Meucci et al. (2013) show that principal components may not be the best way to model risk factors and propose "minimum torsion bets" instead. The present paper discusses different ways of managing diversification and backtests these strategies in a multi-asset portfolio.

The cross-market index for volatility surprise

This article proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, because it provides a simple way to hedge multi-asset portfolios with derivatives contracts written on the CMIX.

소셜네트워크분석 접근법을 활용한 글로벌 금융시장 네트워크 분석

We analyzed the structures and properties of the global financial market networks using social network analysis approach. The Minimum Spanning Tree (MST) lengths and networks of the global financial markets based on the correlation coefficients have been analyzed. Firstly, similar to the previous studies on the global stock indices using MST length, the diversification effects in the global multi-asset portfolio can disappear during the crisis as the correlations among the asset class and within the asset class increase due to the system risks. Second, through the network visualization, we found the clustering of the asset class in the global financial markets network, which confirms the possible diversification effect in the global multi-asset portfolio. Meanwhile, we found the changes in the structure of the network during the crisis. For the last one, in terms of the degree centrality, the stock indices were the most influential to other assets in the global financial markets network, while in terms of the betweenness centrality, Gold, Silver and AUD. In the practical perspective, we propose the methods such as MST length and network visualization to monitor the change of the correlation risk for the risk management of the multi-asset portfolio.

What Determines REIT Returns in Turkey? An Application of Time-Varying Arbitrage Pricing Model in an Emerging REIT Market

This paper investigates the macroeconomic sources of time-varying risk premia in Turkish REIT industry within the arbitrage pricing theory framework. Turkish REIT industry differs substantially from the global REIT market as Turkish REITs do not have to pay out dividends, yet enjoy the exemption from paying corporate taxes, and have highly concentrated ownership structure. These fundamental differences have significant impacts on the performance of REITs compared to other stocks listed on Borsa Istanbul (BIST), especially in terms of the inflationhedging characteristics and time-varying systematic risk behaviour. This article evaluates the Turkish REIT industry by using a time-varying multifactor model, which compares the REIT industry excess returns with various macroeconomic factors, including GDP growth, industrial production growth, inflation risk premium, and stock market risk premium. Our results provide the evidence of time-varying linkages among macroeconomic risks and the conditional first and second moments of excess returns on REITs. We find that among the macroeconomic factors, inflation risk appears to be the major concern in REIT investment. Additionally, Turkish REITs behave more like stocks than real estate. The documented perverse inflation hedges of REITs, the positive correlation between REIT returns and volatility of real economic activity, and the significant influence of ISE equity risk premium on REIT returns can be quoted as the indications of deviation of REITs’ performance from real estate performance. If REITs behave more like stocks than real estate, the diversification benefits of having REITs in a multi-asset portfolio is seriously reduced.

Property Investing for Non-Property Investors

Uncertainty over the fate of the eurozone has dominated European markets over the past three years, and the real estate sector has been no exception. Investors pulled their money out of riskier property assets and opted, in large part, to make allocations to core real estate only in prime markets. Deal flow in southern Europe slowed dramatically. Opportunistic funds struggled to raise cash. Now, however, the situation is changing. Investors are considering different sectors and strategies, motivated largely by lack of yield opportunities (and liability-matching assets) elsewhere. “Our experience has been that more people are coming into property for non-property reasons. Before the crash, you had people coming and they would go into an openended fund that would track an index. Since the crash, we’ve had people look at funds with long-term inflation-linked income, such as property debt,” explains Paul Richards, principal at global investment consultancy Mercer. The year 2012 recorded unexpected interregional flows of capital into Europe, according to research from global real estate services firm Jones Lang LaSalle. This trend has continued in 2013, despite continued anxiety over the euro’s future. Net investment was up 18% in the first quarter of the year, and the firm believes capital will continue to flood into the region throughout 2013. At the same time, a report compiled by the European Association for Investors in Non-Listed Real Estate Vehicles (INREV) revealed that investors’ appetite for risk is growing for the first time since the financial crisis. INREV surveyed 65 institutional investors, 73 fund managers, and 17 fund-of-funds managers globally, and most of those investors expect to increase the share of real estate in their overall multi-asset portfolios.

How much into infrastructure? Evidence from dynamic asset allocation

This paper investigates the role of direct infrastructure in a multi-asset portfolio by employing a US transaction-based index which covers the period Q2 1990–Q2 2010. We determine time-varying asset allocations using a mean-variance as well as a mean-downside risk optimisation algorithm and show that infrastructure plays an important role in both models. It is allocated predominantly to portfolios that exhibit low-to-medium risk with maximum allocations of 32 and 28%, respectively. With increasing investment horizons, infrastructure is also attractive to investors who aim at earning higher returns, and especially to those who wish to protect low-expected-return portfolios from downside risk. As infrastructure and large cap stocks are highly correlated over longer investment horizons, the allocation to infrastructure is sensitive to whether large cap stocks are allocated to the portfolio. Furthermore, we find that infrastructure is not a substitute for real estate.

Risk-Based Allocation of Principal Portfolios

Risk-based asset allocation strategies are mainly used to diversify nominal asset weights. In this paper, we discuss the diversification of risk factors. The analysis is based on the idea of Partovi and Caputo (2004), who use principal component analysis to transform a portfolio into a set of uncorrelated principal portfolios. Risk-based asset allocation strategies can be applied to these uncorrelated sources of risk. A similar route has been taken by Meucci (2009) with his idea of a maximum entropy portfolio. We discuss the relation of this approach with the concept of principal risk parity. Both strategies are backtested against nominal diversification strategies in a multi-asset portfolio. We find no evidence that risk diversification does outperform nominal diversification and discuss possible reasons for this.

Specifying and Managing Tail Risk in Multi-Asset Portfolios -- A Summary

Constructing a multi-asset portfolio with a constraint of tail risk aversion is challenging because (1) the individual asset classes have poor tail risk characteristics and (2) diversification between asset classes is minimal. A better portfolio can be achieved using a multi-strategy framework for the allocation process, whereby different methods of asset and risk allocation co-exist as independent strategies within the same portfolio. This framework creates strategy diversification, allows allocation to be done at multiple investment horizons, and helps to manage tail risk of the portfolio.Conventional tail risk measures, which use only the end-of-horizon return distribution, fail to capture the real risk that an asset owner has of intra-horizon drawdown. Thus, a tail risk measure that is a composite of intra-horizon and end-of-horizon risk should lead to a portfolio with fewer unexpected outcomes. Finally, a better and more aligned portfolio is created if intra-horizon risk is incorporated into the portfolio construction process, the investment horizon of each asset in the portfolio is chosen, and customized stop-loss levels are implemented at the asset level.

Performance of global listed infrastructure investment in a mixed asset portfolio

This paper seeks to examine global infrastructure investment performance compared with other global asset classes such as stocks, bonds, real estate investment trusts, property, hedge funds and private equity. The paper examines the level of correlation of infrastructure with the other assets classes largely reflecting the diversification potential of infrastructure within a mixed asset portfolio. Monthly return indices obtained from Thomson Reuters DataStream over a 10-year period (2001–2010) facilitated the examination of listed infrastructure investment return characteristics including average annual return, annual risk, Sharpe index, mean variance portfolio and maximum return portfolio. Efficient portfolio frontiers were computed using risk solver platform version 11. From a global perspective, investments in infrastructure show a robust comparative performance over the 10-year timeframe (2001–2010). The study is supportive of the argument that the inclusion of infrastructure in a mixed asset portfolio enhances investment performance. The role of global infrastructure in a multi-asset portfolio is shown to be more risk reduction rather than enhancement of return. From a practical viewpoint, the systematic allocation of between 10 and 17.63% of infrastructure into a global investment portfolio can significantly enhance diversification benefits for investors.

Strategic Asset Allocation: The Global Multi-Asset Market Portfolio 1959-2011

The portfolio of the average investor contains important information for strategic asset allocation purposes. This portfolio shows the relative value of all assets according to the market crowd, which one could interpret as a benchmark or the optimal portfolio for the average investor. We determine the market values of equities, private equity, real estate, high yield bonds, emerging debt, non-government bonds, government bonds, inflation linked bonds, commodities, and hedge funds. For this range of assets, we estimate the invested global market portfolio for the period 1990-2011. For the main asset categories equities, real estate, non-government bonds and government bonds we extend the period to 1959-2011. To our understanding, we are the first to document the global multi-asset market portfolio at these levels of detail for such a long period of time.

Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches

The Black-Litterman model aims to enhance asset allocation decisions by overcoming the problems of mean-variance portfolio optimization. We propose a sample based version of the Black-Litterman model and implement it on a multi-asset portfolio consisting of global stocks, bonds, and commodity indices, covering the period from January 1993 to December 2011. We test its out-of-sample performance relative to other asset allocation models and find that Black-Litterman optimized portfolios significantly outperform naive-diversified portfolios (1/N-rule and strategic weights), and consistently perform better than mean-variance, Bayes-Stein, and minimum-variance strategies in terms of out-of-sample Sharpe ratios, even after controlling for different levels of risk aversion, investment constraints, and transaction costs. The BL model generates portfolios with lower risk, less extreme asset allocations, and higher diversification across asset classes. Sensitivity analyses indicate that these advantages are due to more stable mixed return estimates that incorporate the reliability of return predictions, smaller estimation errors, and lower turnover.