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The purpose of this paper is to explore a regime switching asset allocation model that includes six major real estate security markets (USA, UK, Japan, Australia, Hong Kong and Singapore) and focuses on how the presence of regimes affects portfolio composition.

A Markov switching model is first developed to characterize real estate security markets’ risk‐return in two regimes. The mean‐variance portfolio construction methodology is then deployed in the presence of the two regimes. Finally, the out‐of‐sample analyzes are conducted to examine whether the regime switching allocation outperforms the conventional allocation strategy.

Strong evidence of regimes in the six real estate security markets in detected. The correlations between the various real estate security markets’ returns are higher in the bear market regime than in the bull market regime. Consequently the optimal real estate portfolio in the bear market regime is very different from that in the bull market regime. The out‐of‐sample tests reveal that the regime‐switching model outperforms the non‐regime dependent model, the world real estate portfolio and equally‐weighted portfolio from risk‐adjusted performance perspective.

The application of the Markov switching technique to real estate markets is relatively new and has great significance for international real estate diversification. With increased significance of international securitized property as a real estate investment vehicle for institutional investors to gain worldwide real estate exposure, this study provides significant insights into the investment behavior and optimal asset allocation implications of the listed real estate when returns follow a regime switching process.

The purpose of this paper is to investigate whether asset allocation across various industries listed on the Ghana Stock Exchange (GSE) varies across different monetary policy states.

This paper adopts the Markov Chain technique to split monetary policy into three different states. The authors further adopt the Markowitz portfolio optimization technique to find the minimum variance and optimum portfolio for the industries listed on the GSE.

The finding reveals a dynamic asset allocation, which varies the industry’s weight mix across the various monetary policy states enhance excess returns compared to the static asset allocation. Specifically, the authors find risk-return trade-off among industries listed on the GSE. Financial and Food and Beverage industries portfolios record high returns relative to the Government of Ghana 91-day Treasury bill. The Food and Beverage portfolio is the only portfolio that records relatively high excess returns across all the monetary policy states. The authors also find that, during expansionary state (high monetary policy rates) of the monetary policy, investors are to allocate about 69 and 30 percent of their investment into food and beverages and financials, respectively. Corner solution is found in the transient state where 100 percent of wealth is allocated to financial to obtain the optimum portfolio. The optimum portfolio in the contraction state assigns 52 percent to financials and 42 percent to manufacturing. In summary, the result supports the dependence of investors’ asset allocation decisions on monetary policy.

Therefore, the authors propose an investment strategy which is dynamic and takes into consideration the monetary policy states rather than static asset allocation which maintains the same industry weight mix over the investment period.

In sum, the authors interpret the result as support for the dependence of investors’ asset allocation decisions on monetary policy. In Ghana, an increase in the monetary policy appears to support industries listed on the equity market. The result also gives knowledge about investors’ asset allocation decisions on the GSE, which is practical balanced source of information for investors’ risk and return choices. For a prudent monetary policy framework, the monetary policy committee should monitor industries listed on the GSE. The result from the analysis has also an implication for investors, portfolio managers and fund managers to consider the state of the monetary policy in Ghana when making investment decisions.

The study differs from earlier research on asset allocation by breaking new grounds on two levels. First of all, based on the notion that different industries have different exposures to monetary policy states, the authors extend the portfolios by grouping the equities listed on the GSE into their industrial sectors. Second, the authors examine how investors’ optimal portfolio allocation may change depending on the state of monetary policy.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

One of the agency conflicts between investors and managers in fund management is reflected by risk‐taking behaviors led by their different goals. The investors may stop their investments in risky assets before the end of the investment horizon to minimize risk, while the managers may do so to entrench their reputation so as to pursue better opportunities in the labor market. This study aims to consider a one principal‐one agent model to investigate this agency conflict.

The paper derives optimal asset allocation strategies for both parties by extending the traditional dynamic mean‐variance model and considering possibilities of optimal early stopping. Doing so illustrates the principal‐agent conflict regarding risk‐taking behaviors and managerial investment myopia in fund management.

This paper not only paves the way for further studies along this line, but also presents results useful for practitioners in the money management industry.

According to the theoretical analysis and numerical simulations, the paper shows that potential early stop can make the agency conflict worsen, and it proposes a way to mitigate this agency problem.

As one of the exploratory studies in investigating agency conflict regarding risk‐taking behaviors in the literature, this study makes multiple contributions to the literature on fund management, asset allocation, portfolio optimization, and risk management.

The student-managed investment fund (SMIF) program at California State University, Long Beach (CSULB), was launched in 1995 with one portfolio worth $50,000. In the two decades since then, the program has grown to include three portfolios with a combined value of more than $700,000, managed on behalf of three different clients. The purpose of this paper is to describe the creation, evolution and growth of the program including the development of the new quantitative approach and its subsequent implementation. The paper also discusses the ongoing organizational, educational and investment-management challenges associated with the program.

The paper includes a description of the development and evolution of the program along with a discussion of the investment results for one of its three portfolios.

The paper finds: the new quantitative approach implemented in the program is effective as insurance against “black swan” events; and SMIF-type programs can provide learning experiences both for students and faculty members.

The paper explains the practical application of the new quantitative approach as well as the educational benefits of a SMIF-type program.

The paper provides insight into the structure of CSULB’s SMIF program and discusses a unique quantitative approach to asset allocation and security selection.

Academic research has shown that diversification today may not only include stocks and bonds but also alternative investments like hedge funds. However, practical and effective methods to identify the hedge fund styles that really enhance the risk return characteristics of a traditional portfolio as well as optimal allocation sizes are not available. The aim of the paper is to try to close this gap by proposing a portfolio optimization approach based upon the traditional market exposures of the different hedge fund strategies.

For this purpose, the paper first measures the bull and bear market betas of the main hedge fund strategies (equity market neutral, event driven, global macro, relative value, and managed futures). Based on the strategy characteristics, the authors then develop a systematic framework that calculates what percentage of each basic asset should be substituted for by hedge fund strategies to achieve the maximum results. The paper uses hedge fund index data from Hedge Fund Research and Barclay Hedge for the January 1999‐April 2011 sample period.

The empirical results show that this approach leads to an improvement in the annualized return of the optimized portfolio.

The paper adds to the existing literature by showing that it is possible to substitute traditional assets with hedge fund indices based on their exposures (beta) in varying market environments as a way to optimize the overall portfolio.

The purpose of this paper is to address a research gap by providing a comprehensive survey of sovereign wealth funds (SWFs) as international institutional investors and clarifying the definition of SWFs. By doing so, this paper aims to provide a balanced set of policy prescriptions towards SWFs.

This paper conducted a comprehensive survey of world major 24 SWFs with assets under management of 500 million USD between 2008 and 2012. Key dimensions include objectives, funding and governance, asset allocation and investment activities.

SWFs are planning institutions with management direction. They present great variety in terms of funding mechanism, governance, asset allocation and investment strategies, but they in essence pursue financial returns. It is not evident that SWFs are primarily motivated by political objectives and distinctively different from other international institutional investors. Difficulty in interpreting SWFs should not lead to the imposition of constraints on SWFs.

More in-depth and dynamic analysis of SWFs requires better data access. For such a purpose, case studies and longitudinal studies should be adopted, with particular emphasis on comparing SWFs with different types of financial institutional investors as well as typical state-owned enterprises (SOEs) and multinational enterprises.

This study is trying to demystify SWFs based on a comprehensive survey. As a result, this paper may assist investors, policy-makers and regulators to gain a better understanding of SWFs, their investment behaviours and rationales behind.

SWFs like other long-term capital is important for economic and job growth. To attract long-term investments, creating an open, unbiased and welcoming investment environment is the key.

The contribution of this paper is that we provide a deeper understanding of the strategy and empirics of SWF operations. First, after a clearer definition of the phenomenon of SWFs, we can explain their investment strategies and behaviour as firms. Second, we can derive rational policy prescriptions, and third, we can propose a research agenda that will further deepen our understanding of SWFs and the appropriate policy prescriptions.

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.

The purpose of this paper is to examine the regime shifts and stock market volatility in the stock market returns of seven emerging economies popularly called as “BRIICKS” which stands for Brazil, Russia, India, Indonesia, China, South Korea and South Africa, over the period from February 1996 to January 2012 by applying Markov regime switching (MS) in mean-variance model.

The authors apply MS model developed by Hamilton (1989) using its mean-variance switching framework on the monthly returns data of BRIICKS stock markets. Further, the estimated probabilities along with variances have been used to calculate the time-varying volatility. The authors also examine market synchronization and portfolio diversification possibilities in sample markets by calculating the Logit transformation based cross-market correlations and Sharpe ratios.

The applied model finds two regimes in each of these markets. The estimated results also helped in formulating the asset allocation strategies based on market synchronization and Sharpe ratio. The results suggest that BRIICKS is not a homogeneous asset class and each market should be independently evaluated in terms of its regime-switching behavior, volatility persistence and level of synchronization with other emerging markets. The study finally concludes that Russia, India and China as the best assets to invest within this emerging market basket which can be pooled with a mature market portfolio to achieve further benefits of risk diversification.

The study does not provide macroeconomic and financial explanations of the observed differences in dynamics among sample emerging stock markets. The study does not examine these markets under multivariate framework.

The results highlight the role of regime shifts and stock market volatility in the asset allocation and risk management. This study has important implications for international asset allocation and stock market regulation by way of identifying and recognizing the differences on regimes and on the dynamics of the swings which can be very useful in the field of portfolio and public financial management.

The paper is novel in employing tests of MS under mean-variance framework to examine the regime shifts and volatility switching behavior in seven promising BRIICKS stock market. Further, using MS model, the authors analyze the duration (persistence) of each identified regime across sample markets. The empirical results of MS model have been used for making portfolio allocation strategies and also examine the synchronization across markets. All these aspects of stock market regime have been largely ignored by the existing studies in emerging market context particularly the BRIICKS markets.

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.