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Few countries have sufficiently long and detailed returns data for real estate to permit sophisticated analysis. This paper aims to examine the potential diversification of private real estate investments using returns data for major regional centres in Ireland and the UK.

Optimal real estate‐only portfolios are constructed using total returns, income returns and appreciation returns for office and retail real estate in ten cities within Ireland and the UK. The analysis uses IPD data for the period 1984 to 2002. Total return, income return and appreciation returns are treated as separate asset streams in the modelling of portfolios.

The results show different risk levels; in particular the income stream carries low risk, whereas the capital appreciation element is much more volatile and risky. Optimal portfolios, office or retail, whether income, appreciation or total returns, indicate that provincial markets perform well and are capable of pushing London out of the optimised portfolios.

Limitations stem from the optimal portfolios being based on return series without a consideration of market depth. Future research will seek to construct weighted portfolios.

The paper constructs optimal portfolios for three scenarios: low return; medium return; and high return across sectors, return streams and major regional centres in Ireland and the UK. The results show that regional centres perform well and can exclude London real estate from optimal portfolios.

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.

This study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

This work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.

This study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.

This study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.

The contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

The use of modern portfolio theory (MPT) in the construction real estate portfolios has two serious limitations when used in an ex ante framework: the intertemporal instability of the portfolio weights; and the sharp deterioration in performance of the optimal portfolios outside the sample period used to estimate asset mean returns. Both problems can be traced to wide fluctuations in sample means. Aims to prove that the use of a procedure that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub‐optimal results in subsequent periods.

This study extends previous ex ante‐based studies by evaluating ex post optimal portfolio allocations in subsequent test periods by using methods that have been proposed to reduce the effect of measurement error on optimal portfolio allocations.

While techniques designed to handle estimation risk in capital market studies have yielded promising results, they are not completely successful in improving out‐of‐sample performance in this case. It is hypothesised that such results are due to the cyclical nature of property and that the contrarian and mean‐reversion effects picked up in studies of stocks and bonds are not captured when an asset such as direct property is examined. This conclusion is also supported by the strong performance of the tangency portfolios, and in particular the classical unadjusted Sharpe portfolio, over the shorter horizons, which would be consistent with a cyclical momentum effect.

The results suggest that the consideration of the issue of estimation risk is crucial in the use of MPT in developing a successful real estate portfolio strategy.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

Optimization of energy planning for growth and sustainable development has become very important in the context of climate change mitigation imperatives in developing countries. Existing models do not capture developing country realities adequately. The purpose of this paper is to conceptualizes a framework for energy strategy optimization of the Indian energy sector, which can be applied in all emerging economies.

Hierarchical multi‐objective policy optimization methodology adopts a policy‐centric approach and groups the energy strategies into multi‐level portfolios based on convergence of objectives appropriate to each level. This arrangement facilitates application of the optimality principle of dynamic programming. Synchronised optimization of strategies with respect to the common objectives at each level results in optimal policy portfolios.

The reductionist policy‐centric approach to complex energy economy modelling, facilitated by the dynamic programming methodology, is most suitable for policy optimization in the context of a developing country. Barriers to project implementation and cost risks are critical features of developing countries which are captured in the framework in the form of a comprehensive risk barrier index. Genetic algorithms are suitable for optimization of the first level objectives, while the efficiency approach, using restricted weight stochastic data envelopment analysis, is appropriate for higher levels of the objective hierarchy.

The methodology has been designed for application to the energy sector planning for India's 12th Five Year Plan for which the objectives of faster growth, better inclusion, energy security and sustainability have been identified. The conceptual framework combines, within the policy domain, the bottom‐up and top‐down processes to form a hybrid modelling approach yielding optimal outcomes, transparent and convincing to the policy makers. The research findings have substantial implications for transition management to a sustainable energy framework.

The methodology is general in nature and can be employed in all sectors of the economy. It is especially suited to policy design in developing countries with the ground realities factored into the model as project barriers. It offers modularity and flexibility in implementation and can accommodate all the key strategies from diverse sectors along with multiple objectives in the policy optimization process. It enables adoption of an evidence‐based and transparent approach to policy making. The research findings have substantial value for transition management to a sustainable energy framework in developing countries.

We consider the partial hedging of stochastic electricity load pattern with static forward strategies. We assume that the company under consideration maximizes the risk adjusted expected value of its electricity cash flows. First, we calculate an optimal hedge ratio and after that we use this hedge ratio to solve the optimal hedging time. Our results indicate, for instance that agents with high load volatility hedge later than agents that have low load volatility. Moreover, negative correlation between forwards and electricity load pattern postpones the hedging timing.

Systematic mortality risk, i.e. the risk of unexpected changes in mortality and survival rates, can substantially impact a life insurers' risk and solvency situation. By using the “natural hedge” between life insurance and annuities, insurance companies have an effective tool for reducing their net‐exposure. The purpose of this paper is to analyze this risk management tool and to quantify its effectiveness in hedging against changes in mortality with respect to default risk measures.

To achieve this goal, the paper models the insurance company as a whole and takes into account the interaction between assets and liabilities. Systematic mortality risk is considered in two ways. First, systematic mortality risk is modeled using scenario analyses and, second, empirically observed changes in mortality rates for the last 10‐15 years are used.

The paper demonstrates that the consideration of both the asset and liability side is vital to obtain deeper insight into the impact of natural hedging on an insurer's risk situation and shows how to reach a desired safety level while simultaneously immunizing the portfolio against changes in mortality rates.

The paper contributes to the literature by considering the insurance company as a whole in a multi‐period setting and taking into account both, assets and liabilities, as well as their interaction. Furthermore, the paper shows how to obtain a desired safety level while simultaneously immunizing a portfolio against changes in default risk.

The purpose of this paper is to apply safety‐first portfolio principles in an environment where financial risk exists because of the probability of terrorist attacks, where the catastrophic events of September 11, 2001 (911) are the focal point of the analysis.

Safety‐first portfolios of US equities bilaterally combined with 12 developed and emerging region global equity indices are obtained for 911. Extreme value theory and safety‐first principles are used to optimize these portfolios for US risk‐averse investors. The actual performances of all portfolios in the post‐911 period are compared to the optimal results. The robustness of the results is examined by replicating the analysis for the period following July 7, 2006, when no actual terrorist attacks occurred on US soil.

Optimal ex ante (ex post) safety‐first portfolios on 911 have high (low) US weights, and on July 7, 2006 low US weights. The differences are attributed to changes in market projections and/or conditions. In all cases, wealth is preserved even without the ex post optimal portfolios.

Safety‐first portfolio optimization can protect wealth given financial risks of extreme events like terrorist attacks.

The paper shows that quantitative assessments of financial risk are feasible, even though uncertainty with experts' risk assessments of extreme events such as 911 exists because of limited historical data and low probability of occurrence. The results are useful to investors developing international diversification strategies to protect wealth given the risks of terrorist attacks.

The question as to whether it is better to diversify a real estate portfolio within a property type across the regions or within a region across the property types is one of continuing interest for academics and practitioners alike. However, this study is somewhat different from the usual sector/regional analysis in that this study is designed to investigate whether a real estate fund manager can obtain a statistically significant improvement in risk/return performance from extending out of a London based portfolio into firstly the rest of the South East of England and then into the remainder of the UK, or whether the manger would be better off staying within London and diversifying across the various property types.

In order to examine these issues we form a number of portfolios that can be directly compared to a number of benchmark portfolios, as well as to each other. Then using the statistical tests developed by Gibbons et al. and Jobson and Korkie, we investigate whether the benefits that accrue from the differing diversification strategies are statistically significant or not.

The results show that staying within only one sector and one region (London) is undesirable in terms of risk and return compared with all three benchmark portfolios considered here. Secondly diversification on a naïve basis, or in an optimal fashion, leads to significant improvements in performance, irrespective of whether it is across different property types within London or within the same sector across the regions. Finally the results indicate that staying within London and diversifying across the various property types may offer performance comparable with regional diversification, although this conclusion largely depends on the time period and the fund manager's ability to diversify efficiently.

The results suggest that diversification almost always offers increased performance. Indeed a little diversification can quickly lead to levels of performance that is superior to number of benchmarks as well as performance insignificantly different from that of the most diversified portfolio that could be constructed! Consequently fund managers should be encouraged to diversify, be it across the regions or across the sectors of the UK.