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The risk/return trade‐off has been a central tenet of portfolio management since the seminal work of Markowitz [1952]. The basic premise, that higher (expected) returns can only be achieved at the expense of greater risk, leads naturally to the concept of an efficient frontier. The efficient frontier defines the maximum return that can be achieved for a given level of risk or, alternatively, the minimum risk that must be incurred to earn a given return. Traditionally, market risk has been measured by the variance (or standard deviation) of portfolio returns, and this measure is now widely used for credit risk management as well. For example, in the popular Credit‐Metrics methodology (J.P. Morgan [1997]), the standard deviation of credit losses is used to compute the marginal risk and risk contribution of an obligor. Kealhofer [1998] also uses standard deviation to measure the marginal risk and, further, discusses the application of mean‐variance optimization to compute efficient portfolios. While this is reasonable when the distribution of gains and losses is normal, variance is an inappropriate measure of risk for the highly skewed, fat‐tailed distributions characteristic of portfolios that incur credit risk. In this case, quantile‐based measures that focus on the tail of the loss distribution more accurately capture the risk of the portfolio. In this article, we construct credit risk efficient frontiers for a portfolio of bonds issued in emerging markets, using not only the variance but also quantile‐based risk measures such as expected shortfall, maximum (percentile) losses, and unexpected (percentile) losses.

The purpose of this paper is to estimate the performance of 188 mutual funds relative to the risk/return frontier accounting for the transaction costs of producing a portfolio of investments.

The directional output distance function is used to estimate mutual fund performance. The method allows the data to define a frontier of return and risk accounting for the transaction costs associated with securities management and production of risky returns. Proxies for the transaction costs of producing a portfolio of securities include the turnover ratio, load, expense ratio, and net asset value. The estimates of mutual fund performance are bootstrapped to account for the unknown data generating process. By comparing each mutual fund’s performance relative to the capital market line the authors determine how the fund should adjust their portfolio in regard to risk and return in order to maximize the inefficiency adjusted Sharpe ratio.

The bootstrapped estimates indicate that the average mutual fund could simultaneously expand return and contract risk by 3.2 percent if it were to operate on the efficient frontier. After projecting each mutual fund’s return and risk to the efficient frontier the authors find that a majority of the mutual funds should reduce risk to be consistent with the capital market line.

Many researchers have used data envelopment analysis to estimate a piecewise linear frontier of risk and return to measure mutual fund performance. To the authors’ knowledge the research is the first to use a twice-differentiable quadratic directional distance function to measure the managerial performance and risk/return tradeoff of mutual funds.

Standard market risk optimization tools, based on assumptions of normality, are ineffective for evaluating credit risk. In this article, the authors develop three scenario optimization models for portfolio credit risk. They first create the trading risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk‐return efficient frontier is constructed using parametric programming. While scenario optimization of quantile‐based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. The three models are applied to optimizing the risk‐return profile of a portfolio of emerging market bonds.

The research question addressed is whether an event portfolio analysis rooted in financial portfolio theory can yield meaningful insights to complement two approaches to event portfolios. The first approach is extrinsic and rooted in economic impact analysis where events need to demonstrate a financial return on investment. In the second approach events are valued ally, with every event having inherent value and the entire portfolio being valued for its synergistic effects and contribution to social and cultural goals. The paper aims to discuss these issues.

Data from visitors to four events in the Sunshine Coast region of Australia are analyzed to illustrate key points, including the notion of “efficient frontier.”

Conceptual development includes an examination of extrinsic and intrinsic perspectives on portfolios, ways to define and measure value, returns, risk, and portfolio management strategies. In the conclusions a number of research questions are raised, and it is argued that the two approaches to value event portfolios can be combined.

Only four events were studied, in one Australian local authority. The sample of residents who responded to a questionnaire was biased in terms of age, education and gender.

Authorities funding events and developing event portfolios for multiple reasons can benefit from more rigorous analysis of the value created.

This analysis and conceptual development advances the discourse on portfolio theory applied to event management and event tourism.

In September‐October 2008 the Russian stock markets came under severe strain amidst the global financial crisis. During this time the Russian government intervened several times to halt the trade to impede the continuous slide. The government justified its actions owing to the argument that the crisis was due to a trickledown effect from the financial crisis in the USA and the other developed markets. The purpose of this paper is to put to test the government's claim by exploring the level of integration between Russia and the USA and European equity markets.

The study employs Markov Regime Switching Model for tracking structural breaks in the time series. This method divides the data into three periods, i.e. pre crisis, during crisis and post crisis. Next the Multivariate GARCH‐DCC model technique is used to establish the time varying linkages in order to verify the contagion effect. In the final step the Markowitz mean‐variance framework is used to position each individual index portfolio with respect to the efficient frontier to analyze the impact of crisis as well as Russian government intervention.

The findings suggest that the Russian equity market is weakly integrated with US and strongly integrated with European markets. The results correspond to the underlying financial and economic linkages between Russia, the US and Europe. When examined in a portfolio setup, the results show sudden fall in correlation among the Russian, US and European equity markets suggesting weak linkages among these markets. Finally, the Markowitz Efficient frontier indicates dramatic rise in volatility on the day intervention began and ended which signifies the increased uncertainty among the investors owing to Russian government ad hoc interventions.

The paper attempts to examine the Russian government intervention in the backdrop of financial crisis 2008 and concludes that the government intervention essentially increased the uncertainty in the local as well as international markets. Therefore, it is essential that the government should avoid direct intervention in its stock market.

This paper seeks to address the question of consistency, regarding the allocation of real estate in the mixed‐asset portfolio.

To address the question of consistency the allocation of real estate in the mixed‐asset portfolio was calculated over different holding periods varying from five to 25 years. For each portfolio and holding period, the percentage of portfolios with real estate was computed, as was the average real estate allocation in the optimum solution. Then, the risk and return differences between the two efficient frontiers, with and without real estate, were calculated to estimate real estate's marginal impact on portfolio performance.

First, the results suggest strongly that real estate has possessed the attribute of consistency in optimised portfolios. Second, the benefits from including real estate in the mixed‐asset portfolio tend to increase as the investment horizon is extended. Third, the position of real estate changes across the efficient frontier from its return enhancing ability to its risk‐reducing facility. Finally, the results show that the gain in return from adding real estate to the mixed‐asset portfolio is typically less compared with the reduction in portfolio risk.

The results highlight a number of issues in relation to the role of direct real estate within a mixed‐asset framework. In particular, the rationale behind the inclusion of real estate in the mixed‐asset portfolio depends on the length of the holding period of the investor and their position on the efficient frontier.

The study examines the attractiveness of direct real estate in the context of mixed‐asset portfolio.

This paper aims to forgo the conventional (degree of operating leverage) risk measure by replacing elasticity of operating profits with respect to output with elasticity of free cash flow (FCF) with respect to optimal output and by considering exogenous random demand shocks for the firm’s products as a source of risk.

The elasticity risk measure accounts for corporate taxes and the cost of bankruptcy. The methodology is selecting optimal level of production investment and capital structure to generate efficient frontier of expected FCF and its risk in terms of its elasticity with respect to output.

The risk measure leads to efficient frontier between expected FCF and its idiosyncratic managerial risk. The model also resolves the empirical debate on the tradeoff between operating and financial leverages.

It is the first elasticity risk measure that embodied the impact of future level of capital expenditure, total level of assets and their sensitivity to random shocks in the product market.

In response to common criticisms on the appropriateness of mean-variance in asset allocation decisions involving hedge funds, we offer a mean-Gini framework as an alternative. The mean-Gini framework does not require the usual normality assumption concerning return distributions. We also evaluate empirically the differences in allocation outcomes between the two frameworks using historical data. The differences turn out to be significant. The evidence thus confirms the inappropriateness of the mean-variance framework and enhances the attractiveness of mean-Gini for this asset class.

The purpose of this paper is to present a discrete quantitative trading strategy to directly control a portfolio's maximum percentage of drawdown losses while trying to maximize the portfolio's long‐term growth rate.

The loss control target is defined through a Rolling Economic Drawdown (REDD) with a constant look‐back time window. The authors specify risk aversion in the power‐law portfolio wealth utility function as the complement of maximum percentage loss limit and assume long‐term stable Sharpe ratios for asset class indexes while updating volatility estimation in dynamic asset allocation implementation.

Over a test period of the past 20 years (1992‐2011), a risk‐based out‐of‐sample dynamic asset allocation among three broad based indexes (equity, fixed income and commodities) and a risk free asset, is robust against variations in capital market expectation inputs, and out‐performs the in‐the‐sample calibrated model and traditional asset allocation significantly.

The current proposal can lead to a new mathematical framework for portfolio selection. Besides investors' liquidity and behavioural constraints, macroeconomic and market cycle, and the potential of central bank interventions following a market crash, could be additionally considered for a more rigorous dynamic asset allocation model.

Besides the benefit of a clear mandate to construct suitable client portfolios, the portfolio approach can be applied to design invest‐able securities, such as principal‐guaranteed investment products, target risk asset allocation ETFs, and target‐date mutual funds with a glide path, etc. The formulation can also be implemented as a managed futures hedge fund portfolio.

The paper introduces the Rolling Economic Drawdown (REDD) concept and specifies risk aversion as the floor of maximum percentage loss tolerance. Dynamic asset allocation is implemented through updating estimation of asset class volatilities.

The purpose of this paper is to evaluate two different asset selection methodologies and further examine these by forming optimal portfolios.

This paper deals with the problem of portfolio formation, broadly in two steps: asset selection and asset allocation by using the two different approaches for the first step and then well‐known mean variance portfolio optimization. In addition, the resulting portfolios are compared using Sharpe ratio.

The empirical observations prove the applicability of the methodology adopted in the research design, ordered weighted averaging (OWA)‐heuristic algorithm gives us a better portfolio from the sample observations. Also the asset selection procedures adopted in the research proves to be of help when an investor has to narrow down the number of assets to invest in.

The analysis provides two different methodologies for portfolio formation – though the asset allocation is based on the mean variance portfolio optimization, the asset selection methods adopted provide a systematic approach to select the efficient securities.

This paper shows that OWA can be used to decide the order of inputs for the heuristic algorithm. Also an attempt is made to use data envelopment analysis to find a solution to the problem of portfolio formation.