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The purpose of this paper is to examine the effects of parameter uncertainty in the returns process with regime shifts on optimal portfolio choice over the long run for a static buy-and-hold investor who is investing in industry portfolios.

This paper uses a Markov switching model to model returns on industry portfolios and propose a Gibbs sampling approach to take into account parameter uncertainty. This paper compares the results with a linear benchmark model and estimates without taking into account parameter uncertainty. This paper also checks the predictive power of additional predictive variables.

Incorporating parameter uncertainty and taking into account the possible regime shifts in the returns process, this paper finds that such investors can allocate less in the long run to stocks. This holds true for both the NASDAQ portfolio and the individual high tech and manufacturing industry portfolios. Along with regime switching returns, this paper examines dividend yields and Treasury bill rates as potential predictor variables, and conclude that their predictive effect is minimal: the allocation to stocks in the long run is still generally smaller.

Studying the effect of regime switching behavior in returns on the optimal portfolio choice with parameter uncertainty is our main contribution.

The purpose of this study is to provide further evidence on the importance of international diversification, and to determine the optimal allocation of assets in a portfolio comprising domestic (Australian) and international assets. The study focuses on stock index futures contracts in five countries ‐ Australia, USA, UK, Hong Kong and Japan. Daily data for the five selected contracts over the period from 1 January 1990 to 31 December 2000 is employed in the study. Consistent with previous studies, the results confirm the importance of international diversification and indicate that the portfolio risk is reduced considerably when more international assets are added sequentially to the portfolio. Empirical analysis also shows that the optimal asset allocation results in higher risk reduction and better returns when compared with an equally weighted portfolio.

The purpose of this paper is to report new original evidence on optimal holding periods and optimal asset allocations (Benartzi and Thaler, 1995).

The authors employ a number of different value functions, a recent dataset, different markets, and varying investment horizons.

The authors report original evidence across markets and over-time, employing different value functions and varying investment horizons. The results results indicate that, during the past decades, the optimal holding period (seven months during the whole period and four/five months during crises) is not affected by the value function employed, is in accordance with the Myopic Loss Aversion hypothesis, is consistent across markets, but is sensitive to economic crises and shorter to that reported in Benartzi and Thaler (12 months). The optimal asset allocation is also different to that of Benartzi and Thaler during crises periods and/or assuming value functions with probability distortion.

The paper employs a number of different value functions, with and without probability distortion; it compares investor behavior in three important international markets (USA, UK, Germany); as a further robustness test the authors use various investment horizons.

Property is a key investment asset class that offers considerable benefits in a mixed-asset portfolio. Previous studies have concluded that property allocation should be within the 10-30 per cent range. However, there seems to be wide variation in theory and practice. Historical Australian superannuation data shows that the level of allocation to property asset class in institutional portfolios has remained constant in recent decades, restricted at 10 per cent or lower. This is seen by many in the property profession as a subjective measure and needs further investigation. The purpose of this paper is to compare the performance of the AU$431 billion industry superannuation funds’ strategic balanced portfolio against ten different passive and active investment strategies.

The analysis used 20 years (1995-2015) of quarterly data covering seven benchmark asset classes, namely: Australian equities, international equities, Australian fixed income, international fixed income, property, cash and alternatives. The 11 different asset allocation models are constructed within the modern portfolio theory framework utilising Australian ten-year bonds as the risk free rate. The Sharpe ratio is used as the key risk-adjusted return performance measure.

The ten different asset allocation models perform as well as the industry fund strategic approach. The empirical results show that there is scope to increase the property allocation level from its current 10-23 per cent. Upon excluding unconstrained strategies, the recommended allocation to property for industry funds is 19 per cent (12 per cent direct and 7 per cent listed). This high allocation is backed by improved risk-adjusted return performance.

The constrained optimal, tactical and dynamic models are limited to asset weight, no short selling and turnover parameters. Other institutional constraints that can be added to the portfolio optimisation problem include transaction costs, taxation, liquidity and tracking error constraints.

The 11 different asset allocation models developed to evaluate the property allocation component in industry superannuation funds portfolio will attract fund managers to explore alternative strategies (passive and active) where risk-adjusted returns can be improved, compared to the common strategic approach with increased allocation to property assets.

The research presents a unique perspective of investigating the optimal allocation to property assets within the context of active investment strategies, such as tactical and dynamic models, whereas previous studies have focused mainly on passive investment strategies. The investigation of these models effectively contributes to the transfer of broader finance and investment market theories and practice to the property discipline.

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 purpose of this paper is to empirically analyze the relationship between risky asset allocation and background risk of Chinese residents.

Using Chinese macroeconomic data, this study uses numerical method to solve dynamic stochastic optimal problem.

When risk of labor income is considered, ratio of risky asset declines with rising of age for those people with same age and wealth state; any of the following situations will lead to lower risky assets holdings: lower labor income growth expectations, higher labor income risk or higher labor and financial market covariance risk.

This study uses real economy investment return as a proxy of risky asset return.

Residents with higher background risks should hold less risky assets, and overcome home‐bias problem during asset allocation.

This study takes two kinds of background risk into consideration: labor income risk, and covariance between labor income and risk asset.

The purpose of this paper is to study the scope for country diversification in international portfolios of mutual funds for the “core” EMU countries. The author uses a sample of daily returns for country indices of French, German and Italian funds to investigate the quest for international diversification. The author focuses on fixed-income mutual funds during the period of the financial market turmoil since 2007.

The author compute optimal portfolio allocations from both unconstrained and constrained mean-variance frameworks that take as input the out-of-sample forecasts for the conditional mean, volatility and correlation of country-level indices for funds returns. The author also applies a portfolio allocation model based on utility maximization with learning about the time-varying conditional moments. The author compares the out-of-sample forecasting performance of 12 multivariate volatility models.

The author finds that there is a “core” EMU country also for the mutual fund industry: optimal portfolios allocate the largest portfolio weight to German funds, with Italian funds assigned a lower weight in comparison to French funds. This result is remarkably robust across competing forecasting models and optimal allocation strategies. It is also consistent with the findings from a utility-maximization model that incorporates learning about time-varying conditional moments.

This is the first study on optimal country-level diversification for a mutual fund investor focused on European countries in the fixed-income space for the turmoil period. The author uses a large array of econometric models that captures the salient features of a period characterized by large changes in volatility and correlation, and compare the performance of different optimal asset allocation models.

Examines the impact of using “unsmoothing” techniques on real estate data to take pension‐plan asset‐allocation decisions. It is generally believed that valuation‐based real estate indices give rise to returns figures which are “smoothed” versions of the underlying transaction prices. Unsmoothing techniques can be used to develop real estate return data series that are believed to be a more accurate representation of underlying transaction prices. If this is done, the resulting data reveal greater volatility of real estate returns. When such data are applied to portfolio selection models, they often reveal a reduced allocation to real estate in efficient portfolios. Looks at the impact of unsmoothing data when taking pension‐plan asset‐allocation decisions. Finds here that the unsmoothed data are more closely correlated with pension plan liabilities. As a result, efficient pension plan portfolios sometimes contain more real estate, rather than less. In general, there is little change in the efficient real estate allocation. These results are very important. They reveal that so‐called “valuation smoothing” may distort property investment decisions less than is commonly thought.

The purpose of this paper is to establish an optimum mix of liquid, publicly traded assets that may be added to a real estate portfolio, such as those held by open-ended funds, to provide the liquidity required by institutional investors, such as UK defined contribution pension funds. This is with the objective of securing liquidity while not unduly compromising the risk-return characteristics of the underlying asset class. This paper considers the best mix of liquid assets at different thresholds for a liquid asset allocation, with the performance then evaluated against that of a direct real estate benchmark index.

The authors employ a mean-tracking error optimisation approach in determining the optimal combination of liquid assets that can be added to a real estate fund portfolio. The returns of the optimised portfolios are compared to the returns for portfolios that employ the use of either cash or listed real estate alone as a liquidity buffer. Multivariate generalised autoregressive models are used along with rolling correlations and tracking errors to gauge the effectiveness of the various portfolios in tracking the performance of the benchmark index.

The results indicate that applying formal optimisation techniques leads to a considerable improvement in the ability of the returns from blended real estate portfolios to track the underlying real estate market. This is the case at a number of different thresholds for the liquid asset allocation and in cases where a minimum return requirement is imposed.

The results suggest that real estate fund managers can realise the liquidity benefits of incorporating publicly traded assets into their portfolios without sacrificing the ability to deliver real estate-like returns. However, in order to do so, a wider range of liquid assets must be considered, not just cash.

Despite their importance in the real estate investment industry, comparatively few studies have examined the structure and operation of open-ended real estate funds. To the authors’ knowledge, this is the first study to analyse the optimal composition of liquid assets within blended or hybrid real estate portfolios.

The paper uses 101 years of Chilean and international financial assets returns to investigate mean-variance optimal portfolio allocations. The key conclusion is that the share of international unhedged investments is substantial even in minimum risk portfolios (20%), unless the period 1980–2002 is assumed to be drawn from a different distribution and previous history is disregarded. In addition to that, the paper finds that mean-variance optimal investors would have generated substantial demand for an asset replicating the return profile of an efficient pay-as-you-go pension scheme. Labour income and departures from log-normality of returns might, however, affect the latter conclusion.