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The purpose of the paper is to examine the role of securitised real estate within the confines of a multi‐asset investment portfolio and to identify if indeed securitised real estate can afford investors the desired investment benefits of direct property investment whilst mitigating many of the recognised barriers and risks.

The paper employs a suite of analytical techniques; lead‐lag correlations are utilised to examine market dynamics between listed and direct real estate markets across jurisdictions. Grainger causality and co‐integration techniques are applied to examine the nature and extent of relationships between investment markets with optimal portfolio analysis utilised to explore the role of securitised real estate and the optimum weighting allocation within the confines of a multi‐asset investment portfolio.

The findings demonstrated the unresponsive nature of direct real estate markets relative to listed real estate markets – in some jurisdictions the extent of lag can be as much as 12 months. Whilst the research did not identify a Grainger causality relationship between listed and direct property markets across the jurisdictions, co‐integration analysis does infer trend reverting price behaviour in the long run (ten years) between direct and listed real estate markets. Optimal portfolio analysis serves to demonstrate the crucial role of real estate within a multi‐asset portfolio in terms of both mitigating risk and enhancing performance over the ten‐year time series. Indeed, the optimal portfolio analysis highlights the compatibility and complementarity of listed and direct real estate within a multi‐asset investment portfolio.

The question if securitised real estate is a viable proxy for direct property investment is as inconclusive as it is enduring. In contrast to the large embodiment of previous work, this paper adopts an international market perspective depicting the global nature of securitised real estate investment markets whilst also reflecting on the extent of co‐integration between asset classes and across jurisdictions during a period of extreme financial and economic distress.

The aim of this paper is to develop the optimal delta hedge for a portfolio of mortgage servicing rights (MSR) under the constraint of a zero‐gamma in order to avoid costs related to the rebalancing of such portfolio.

The paper develops the optimal delta hedge ratio with gamma and vega constraints for an MSR portfolio by finding the different combinations of coupon/maturity bonds (c, n) that satisfy the constraints under different yields (y). Rather than search for the available fixed income securities and see by trial and error which one should be added to the portfolio of MSR such that when market rates go up and down, the value of the portfolio is not affected, the model finds the optimal pairs of coupon/maturity bonds over a range of yields, that satisfy the constraints. It develops the conditions for a delta‐hedged portfolio of bonds and MSR under an investor's or a portfolio manager's value constraint K. The share α of the MSR's value and the share β of the bond's value had to be such that a zero‐delta portfolio that satisfies the constant value of the portfolio can be created. The paper “optimized” α by requiring simultaneously that α′=0 and also that α″=0, for a fixed y given by the market that will guarantee that the function α satisfies simultaneously the two conditions: α′=0 and α″=0. These two conditions will yield equations in the parameters of c and n. The “optimization” problem for arbitrary y's is solved, and n and c for the appropriate bond are found to be added to the MSR portfolio.

Maple software is used to simulate a portfolio of MSR that is delta hedged with bonds, whose appropriate coupon and maturity are found with the model developed under the constraint of a zero‐gamma, in order to avoid costs related to the rebalancing of such portfolio. The optimal hedge ratio with gamma and vega constraints for an MSR portfolio is developed by finding the different combinations of coupon/maturity bonds that satisfy the constraints under different yields (the “triples”).

The optimal triples (c, n, y) are obtained in order to optimize α and simultaneously α′=0 and α″=0. For example, for a yield of 16.1 percent, a bank with a portfolio of MSR based on the data used, should add to it, bonds with a 6.33 percent coupon that matures in 7.55 years.

This paper is the first to the authors' knowledge to derive the different triples (yield, coupon, maturity) of bonds that when combined with MSR create a portfolio that is dynamically hedged against interest rate risk and prepayment risk, and therefore eliminates the need of periodic rebalancing of a portfolio of MSR, which is costly.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

New methods of integrated risk modeling play an important role in determining the efficiency of bank portfolio management. The purpose of this paper is to suggest a systematic approach for risk strategies formulation based on risk‐return optimized portfolios, which applies different methodologies of risk measurement in the context of actual regulatory requirements.

Optimization problems to illustrate different levels of integrated bank portfolio management has been set up. It constrains economic capital allocation using different risk aggregation methodologies. Novel methods of financial engineering to relate actual bank capital regulations to recently developed methods of risk measurement value‐at‐risk (VaR) and conditional value‐at‐risk (CVaR) deviation are applied. Optimization problems with the portfolio safeguard package by American Optimal Decision (web site: www.AOrDA.com) are run.

This paper finds evidence that risk aggregation in Internal Capital Adequacy Assessment Process (ICAAP) should be based on risk‐adjusted aggregation approaches, resulting in an efficient use of economic capital. By using different values of confidence level α in VaR and CVaR, deviation, it is possible to obtain optimal portfolios with similar properties. Before deciding to insert constraints on VaR or CVaR, one should analyze properties of the dataset on which computation are based, with particular focus on the model for the tails of the distribution, as none of them is “better” than the other.

This study should further be extended by an inclusion of simulation‐based scenarios and copula approaches for integrated risk measurements.

The suggested optimization models support a systematic generation of risk‐return efficient target portfolios under the ICAAP. However, issues of practical implementation in risk aggregation and capital allocation still remain unsolved and require heuristic implementations.

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.

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.

The purpose of this paper is to study the asset allocation problem for a pension fund which maximizes the expected present value of its wealth augmented by the prospective mathematical reserve at the death time of a representative member.

The paper applies the stochastic optimization technique in continuous time. In order to present an explicit solution it considers the case of both deterministic interest rate and market price of risk.

The paper demonstrates that the optimal portfolio is always less risky than the Merton's (1969‐1971) one. In particular, the asset allocation is less and less risky until the pension date while, after retirement of the fund's representative member, it becomes riskier and riskier.

The paper shows the best way for managing a pension fund portfolio during both the accumulation and the decumulation phases.

The paper fills a gap in the optimal portfolio literature about the joint analysis of both the actuarial and the financial framework. In particular, it shows that the actuarial part strongly affects the behaviour of the optimal asset allocation.

The purpose of this article is to help investors build less-concentrated portfolios as well as to construct optimal return-concentration portfolios.

An alternative portfolio objective is proposed where investors care about the level of concentration of their portfolio weights. Minimizing the concentration of portfolio weights leads to the well-known equal-weight portfolio as the optimal choice. Maximizing the trade-off between the portfolio's expected return and the weight concentration produces a novel portfolio with weights proportional to the expected return of each security.

An empirical application with 30 industry portfolios and 1,000 individual stocks finds that both proposed strategies perform well out-of-sample both in terms of the proposed concentration measure but also in terms of more traditional risk-based measures like Sharpe ratios, abnormal returns and market betas.

The optimal risk-concentration portfolio proposed in this paper is a novel result. The portfolio generalizes prior practitioner intuition on focusing on securities with the highest expected returns and the concept of diversification.

Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios.

This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost.

The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary.

The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.

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.