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Purpose – To study the allocation in equity markets of sovereign wealth funds’ (SWF) investments with respect to other institutional investors. To analyze the role of political regimes in the sending and recipient countries as a determinant of the allocation of SWF investments.

Methodology/approach – We use mutual funds’ investments as a benchmark for SWF investment allocations. We collect data of SWF and mutual fund equity investments at the firm level and analyse them on a geographical and sector basis. We compare target investments for these two groups by looking at the political regime in the sending and recipient country, using different political indicators (Polity IV, Bertelsmann). We provide a comparison of SWFs and pension funds based on governance features related to investment.

Findings – We find that the fear that sovereigns with political motivations use their financial power to secure large stakes in OECD countries is not confirmed by the data. SWF investment decisions do not differ greatly from those of other wealth managers. Although there can be differences in the allocation, political regimes in the recipient countries do not play a role in explaining the allocation of sovereign wealth funds.

Social implications – Investment from public institutions, such as sovereign wealth funds, can have significant implications at the economic and social level. Sovereign funds are potential sources of capital for emerging economies, and therefore can enchance economic growth. It is important to understand to what extent public institutional investors behave differently from private investors. The “political bias” is not a relevant factor for sovereign funds, or for other institutional investors, for allocating their capital. More often than not, their asset allocation strategies converge with other large investors, these being driven by financial and not political bias.

Originality/value of the chapter – The chapter is an original contribution providing a firm-level analysis of equity holdings for two groups of institutional investors. Moreover, it emphasizes the political dimension of institutional investments, highlighting the priorities and constraints of public investors participating in financial markets. The chapter suggests that SWFs do not discriminate by the political regime of the recipient country in their asset allocation.

Given the rising need for measuring and controlling of financial risk as proposed in Basel II and Basel III Capital Adequacy Accords, trading risk assessment under illiquid market conditions plays an increasing role in banking and financial sectors, particularly in emerging financial markets. The purpose of this chapter is to investigate asset liquidity risk and to obtain a Liquidity-Adjusted Value at Risk (L-VaR) estimation for various equity portfolios. The assessment of L-VaR is performed by implementing three different asset liquidity models within a multivariate context along with GARCH-M method (to estimate expected returns and conditional volatility) and by applying meaningful financial and operational constraints. Using more than six years of daily return dataset of emerging Gulf Cooperation Council (GCC) stock markets, we find that under certain trading strategies, such as short selling of stocks, the sensitivity of L-VaR statistics are rather critical to the selected internal liquidity model in addition to the degree of correlation factors among trading assets. As such, the effects of extreme correlations (plus or minus unity) are crucial aspects to consider in selecting the most adequate internal liquidity model for economic capital allocation, especially under crisis condition and/or when correlations tend to switch sings. This chapter bridges the gap in risk management literatures by providing real-world asset allocation tactics that can be used for trading portfolios under adverse markets’ conditions. The approach to computing L-VaR has been arrived at through the application of three distinct liquidity models and the obtained results are used to draw conclusions about the relative liquidity of the diverse equity portfolios.

This chapter investigates the relative magnitude of the benefits of global diversification from the viewpoint of domestic investors in various countries by forming time-rolling efficient frontiers. To enhance feasibility of asset allocation strategies, the constraints of short-sales and over-weighting investments are taken into account. The empirical results suggest that local investors in less developed countries, particularly in Latin America, East Asia, and Southern Europe, comparatively benefit more from global diversification. Investors in the countries of civic-law origin tend to benefit more from global investment than the ones in the common-law states. Although the global market has become more integrated over the past decades, diversification benefits for domestic investors declined but did not vanish. The results of this chapter are useful for asset management professionals to determine target markets to promote the sales of international funds.

The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.